http://iis-projects.ee.ethz.ch/api.php?action=feedcontributions&user=Vmenescal&feedformat=atomiis-projects - User contributions [en]2024-03-29T01:24:47ZUser contributionsMediaWiki 1.28.0http://iis-projects.ee.ethz.ch/index.php?title=Practical_Reconfigurable_Intelligent_Surfaces_(RIS)&diff=9922Practical Reconfigurable Intelligent Surfaces (RIS)2023-11-16T10:25:32Z<p>Vmenescal: /* Status: Completed */</p>
<hr />
<div>[[File:Image_RIS.png|400px|thumb|RIS aided wireless system.]]<br />
==Short Description==<br />
<br />
Since the beginning of research for fifth-generation (5G) wireless systems and beyond, providing high data rates for all users was one of the key requirements. From now on, the expectation is to have more than 50 billions connected devices [1] in the network. To meet this demand, technologies that improve energy efficiency are a big trend, so as to promote green and sustainable wireless systems. <br />
<br />
Reconfigurable intelligent surfaces (RISs) are a promising solution that has emerged in recent years with the goal of altering electromagnetic fields by controlling the phase, amplitude, frequency and polarization of the incoming signals [2]. With a meta-surface equipped with integrated electronic circuits, RIS has the power to combat adversarial effects of wireless propagation and will substitute the traditional structure of wireless networks to an innovative hybrid one with both active and passive components [2,3]. <br />
<br />
In [1], a RIS is used in the downlink of a MIMO system to investigate the improvement provided by these devices in terms of energy efficiency. A design focused on this purpose is developed not only for the transmit power allocation but also for the phase shifts of the reflecting elements and predicts to improve the energy efficiency in 300%, compared to regular multi-antenna amplify-and-forward relaying. As opposed to the majority of existing results that consider continuous phase shifts at the reflecting elements, in [4], a finite set of discrete phase shifts is considered at each element, with the objective to minimize the transmit power at each access point (AP). This minimization is performed with a joint optimization of the precoding at the AP and the discrete reflect phase shifts at the RIS, subject to a set of constraints, and claims to maintain squared power gain when compared to continuous phase shifts. Similarly, new problems were solved in [5] to minimize the total transmit power at the AP, also by joint optimization of the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the RIS. <br />
<br />
Unlike the scenario in [4], one of the many problems that can be addressed in practical RIS is the transmit precoding and phase shifts optimization with multiple APs and RIS. In realistic scenarios, APs are assisted by multiple RIS and RIS can assist multiple APs. Therefore, in this project, we will explore the potential of RIS for 5G and beyond, considering a distributed network with multiple APs and RIS so as to address the phase selection problem. By developing algorithms to adjust the phase shifts, we are able to boost the desired signal power and mitigate interference, improving the system’s performance, without the need of extra APs [4].<br />
<br />
<br />
<br />
References:<br />
<br />
[1] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah and C. Yuen, "Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication," in IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157-4170, Aug. 2019, doi: 10.1109/TWC.2019.2922609. [https://ieeexplore.ieee.org/document/8741198 Link]<br />
<br />
[2] E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. Alouini and R. Zhang, "Wireless Communications Through Reconfigurable Intelligent Surfaces," in IEEE Access, vol. 7, pp. 116753-116773, 2019, doi: 10.1109/ACCESS.2019.2935192. [https://ieeexplore.ieee.org/document/8796365 Link]<br />
<br />
[3] Q. Wu and R. Zhang, "Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network," in IEEE Communications Magazine, vol. 58, no. 1, pp. 106-112, January 2020, doi: 10.1109/MCOM.001.1900107. [https://ieeexplore.ieee.org/document/8910627 Link]<br />
<br />
[4] Q. Wu and R. Zhang, "Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts," in IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838-1851, March 2020, doi: 10.1109/TCOMM.2019.2958916. [https://ieeexplore.ieee.org/document/8683145 Link]<br />
<br />
[5]Q. Wu and R. Zhang, "Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming," in IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394-5409, Nov. 2019, doi: 10.1109/TWC.2019.2936025. [https://ieeexplore.ieee.org/document/8811733 Link]<br />
<br />
===Status: Not available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
: VLSI I (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Student A, StudentB<br />
: Supervision: [[:User:Mluisier | Mathieu Luisier]]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Practical_Reconfigurable_Intelligent_Surfaces_(RIS)&diff=9921Practical Reconfigurable Intelligent Surfaces (RIS)2023-11-16T10:23:17Z<p>Vmenescal: /* Status: Available */</p>
<hr />
<div>[[File:Image_RIS.png|400px|thumb|RIS aided wireless system.]]<br />
==Short Description==<br />
<br />
Since the beginning of research for fifth-generation (5G) wireless systems and beyond, providing high data rates for all users was one of the key requirements. From now on, the expectation is to have more than 50 billions connected devices [1] in the network. To meet this demand, technologies that improve energy efficiency are a big trend, so as to promote green and sustainable wireless systems. <br />
<br />
Reconfigurable intelligent surfaces (RISs) are a promising solution that has emerged in recent years with the goal of altering electromagnetic fields by controlling the phase, amplitude, frequency and polarization of the incoming signals [2]. With a meta-surface equipped with integrated electronic circuits, RIS has the power to combat adversarial effects of wireless propagation and will substitute the traditional structure of wireless networks to an innovative hybrid one with both active and passive components [2,3]. <br />
<br />
In [1], a RIS is used in the downlink of a MIMO system to investigate the improvement provided by these devices in terms of energy efficiency. A design focused on this purpose is developed not only for the transmit power allocation but also for the phase shifts of the reflecting elements and predicts to improve the energy efficiency in 300%, compared to regular multi-antenna amplify-and-forward relaying. As opposed to the majority of existing results that consider continuous phase shifts at the reflecting elements, in [4], a finite set of discrete phase shifts is considered at each element, with the objective to minimize the transmit power at each access point (AP). This minimization is performed with a joint optimization of the precoding at the AP and the discrete reflect phase shifts at the RIS, subject to a set of constraints, and claims to maintain squared power gain when compared to continuous phase shifts. Similarly, new problems were solved in [5] to minimize the total transmit power at the AP, also by joint optimization of the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the RIS. <br />
<br />
Unlike the scenario in [4], one of the many problems that can be addressed in practical RIS is the transmit precoding and phase shifts optimization with multiple APs and RIS. In realistic scenarios, APs are assisted by multiple RIS and RIS can assist multiple APs. Therefore, in this project, we will explore the potential of RIS for 5G and beyond, considering a distributed network with multiple APs and RIS so as to address the phase selection problem. By developing algorithms to adjust the phase shifts, we are able to boost the desired signal power and mitigate interference, improving the system’s performance, without the need of extra APs [4].<br />
<br />
<br />
<br />
References:<br />
<br />
[1] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah and C. Yuen, "Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication," in IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157-4170, Aug. 2019, doi: 10.1109/TWC.2019.2922609. [https://ieeexplore.ieee.org/document/8741198 Link]<br />
<br />
[2] E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. Alouini and R. Zhang, "Wireless Communications Through Reconfigurable Intelligent Surfaces," in IEEE Access, vol. 7, pp. 116753-116773, 2019, doi: 10.1109/ACCESS.2019.2935192. [https://ieeexplore.ieee.org/document/8796365 Link]<br />
<br />
[3] Q. Wu and R. Zhang, "Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network," in IEEE Communications Magazine, vol. 58, no. 1, pp. 106-112, January 2020, doi: 10.1109/MCOM.001.1900107. [https://ieeexplore.ieee.org/document/8910627 Link]<br />
<br />
[4] Q. Wu and R. Zhang, "Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts," in IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838-1851, March 2020, doi: 10.1109/TCOMM.2019.2958916. [https://ieeexplore.ieee.org/document/8683145 Link]<br />
<br />
[5]Q. Wu and R. Zhang, "Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming," in IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394-5409, Nov. 2019, doi: 10.1109/TWC.2019.2936025. [https://ieeexplore.ieee.org/document/8811733 Link]<br />
<br />
===Status: Completed ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
: VLSI I (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Student A, StudentB<br />
: Supervision: [[:User:Mluisier | Mathieu Luisier]]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7865Cell-Free mmWave Massive MIMO Communication2022-07-13T19:34:32Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: Completed ===<br />
: Student: Jannik Brun<br />
: Date: Spring Semester 2022<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares] [https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Completed]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
[[Category:2022]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7864Cell-Free mmWave Massive MIMO Communication2022-07-13T19:32:41Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: Completed ===<br />
: Student: Jannik Brun<br />
: Date: Spring Semester 2022<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares] [https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Completed]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7863Cell-Free mmWave Massive MIMO Communication2022-07-13T19:32:06Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: Completed ===<br />
: Student: Jannik Brun<br />
: Date: Spring Semester 2022<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares] [https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7780Cell-Free mmWave Massive MIMO Communication2022-05-23T09:36:21Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7311Cell-Free mmWave Massive MIMO Communication2021-11-23T15:39:37Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: In Progress ===<br />
: Student: Jannik Brun<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares], [https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Jannik Brun<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares, https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:In progress]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Cell-Free_mmWave_Massive_MIMO_Communication&diff=7310Cell-Free mmWave Massive MIMO Communication2021-11-23T15:38:08Z<p>Vmenescal: </p>
<hr />
<div>[[File:Figure_cell_free_mmwave_mimo.png|500px|thumb|Cell-free mmWave massive MIMO network [4].]]<br />
==Short Description==<br />
<br />
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era [1]. <br />
A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems [2], cell-free mmWave systems will operate at frequencies exceeding 28 GHz [3].<br />
<br />
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In [1], channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in [3]. Last, to solve the lack of usage of all APs in this type of system, [4] presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems. <br />
<br />
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.<br />
<br />
References:<br />
<br />
[1] Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. [https://ieeexplore.ieee.org/document/8815888 Link]<br />
<br />
[2] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. [https://ieeexplore.ieee.org/document/7827017 Link]<br />
<br />
[3] G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. [https://ieeexplore.ieee.org/document/8678745 Link]<br />
<br />
[4] J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. [https://ieeexplore.ieee.org/document/9149862 Link]<br />
<br />
===Status: In Progress ===<br />
: Student: Jannik Brun<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares], [https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Jannik Brun<br />
: Supervision: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares, https://ee.ethz.ch/the-department/people-a-z/person-detail.MTk4MzMz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Gian Marti]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:In Progress]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Practical_Reconfigurable_Intelligent_Surfaces_(RIS)&diff=7309Practical Reconfigurable Intelligent Surfaces (RIS)2021-11-23T15:29:01Z<p>Vmenescal: </p>
<hr />
<div>[[File:Image_RIS.png|400px|thumb|RIS aided wireless system.]]<br />
==Short Description==<br />
<br />
Since the beginning of research for fifth-generation (5G) wireless systems and beyond, providing high data rates for all users was one of the key requirements. From now on, the expectation is to have more than 50 billions connected devices [1] in the network. To meet this demand, technologies that improve energy efficiency are a big trend, so as to promote green and sustainable wireless systems. <br />
<br />
Reconfigurable intelligent surfaces (RISs) are a promising solution that has emerged in recent years with the goal of altering electromagnetic fields by controlling the phase, amplitude, frequency and polarization of the incoming signals [2]. With a meta-surface equipped with integrated electronic circuits, RIS has the power to combat adversarial effects of wireless propagation and will substitute the traditional structure of wireless networks to an innovative hybrid one with both active and passive components [2,3]. <br />
<br />
In [1], a RIS is used in the downlink of a MIMO system to investigate the improvement provided by these devices in terms of energy efficiency. A design focused on this purpose is developed not only for the transmit power allocation but also for the phase shifts of the reflecting elements and predicts to improve the energy efficiency in 300%, compared to regular multi-antenna amplify-and-forward relaying. As opposed to the majority of existing results that consider continuous phase shifts at the reflecting elements, in [4], a finite set of discrete phase shifts is considered at each element, with the objective to minimize the transmit power at each access point (AP). This minimization is performed with a joint optimization of the precoding at the AP and the discrete reflect phase shifts at the RIS, subject to a set of constraints, and claims to maintain squared power gain when compared to continuous phase shifts. Similarly, new problems were solved in [5] to minimize the total transmit power at the AP, also by joint optimization of the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the RIS. <br />
<br />
Unlike the scenario in [4], one of the many problems that can be addressed in practical RIS is the transmit precoding and phase shifts optimization with multiple APs and RIS. In realistic scenarios, APs are assisted by multiple RIS and RIS can assist multiple APs. Therefore, in this project, we will explore the potential of RIS for 5G and beyond, considering a distributed network with multiple APs and RIS so as to address the phase selection problem. By developing algorithms to adjust the phase shifts, we are able to boost the desired signal power and mitigate interference, improving the system’s performance, without the need of extra APs [4].<br />
<br />
<br />
<br />
References:<br />
<br />
[1] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah and C. Yuen, "Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication," in IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157-4170, Aug. 2019, doi: 10.1109/TWC.2019.2922609. [https://ieeexplore.ieee.org/document/8741198 Link]<br />
<br />
[2] E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. Alouini and R. Zhang, "Wireless Communications Through Reconfigurable Intelligent Surfaces," in IEEE Access, vol. 7, pp. 116753-116773, 2019, doi: 10.1109/ACCESS.2019.2935192. [https://ieeexplore.ieee.org/document/8796365 Link]<br />
<br />
[3] Q. Wu and R. Zhang, "Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network," in IEEE Communications Magazine, vol. 58, no. 1, pp. 106-112, January 2020, doi: 10.1109/MCOM.001.1900107. [https://ieeexplore.ieee.org/document/8910627 Link]<br />
<br />
[4] Q. Wu and R. Zhang, "Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts," in IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838-1851, March 2020, doi: 10.1109/TCOMM.2019.2958916. [https://ieeexplore.ieee.org/document/8683145 Link]<br />
<br />
[5]Q. Wu and R. Zhang, "Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming," in IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394-5409, Nov. 2019, doi: 10.1109/TWC.2019.2936025. [https://ieeexplore.ieee.org/document/8811733 Link]<br />
<br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
: VLSI I (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Student A, StudentB<br />
: Supervision: [[:User:Mluisier | Mathieu Luisier]]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_5G]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=File:Image_RIS.png&diff=7308File:Image RIS.png2021-11-23T15:27:45Z<p>Vmenescal: </p>
<hr />
<div></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Self-Supervised_User_Positioning_in_Cell-Free_Massive_MIMO_Systems&diff=7307Self-Supervised User Positioning in Cell-Free Massive MIMO Systems2021-11-23T15:27:28Z<p>Vmenescal: </p>
<hr />
<div>[[File:Image_positioning.png|400px|thumb|Triangulation for user positioning.]]<br />
==Short Description==<br />
<br />
Massive multiple-input multiple-output (MIMO) systems have been extensively investigated in the past decade as one of the most attractive solutions for the fifth generation (5G) of wireless systems. One of the grand challenges in such systems is user positioning, which is critical for emergency services, area-specific advertisements, content caching, and autonomous driving [1,2]. Global navigation satellite systems (GNSS) are being extensively used to provide this kind of information. However, the base station does not have access to GNSS information and satellite-based localization is unreliable indoors and in dense rural scenarios. As a consequence, current research focuses on developing new strategies to use wireless signals to extract positioning data [2]. Emerging approaches that have been proposed for massive MIMO and distributed massive MIMO (DM-MIMO) systems are channel charting [3], fingerprinting techniques [1], and triangulation/trilateration [4].<br />
<br />
Fingerprinting techniques based on the received signal strength (RSS) and Gaussian process regression (GPR) have been applied to user positioning, but require extensive training with labeled data [1]. Triangulation and trilateration as described in [4], estimate the user’s position by jointly processing the RSS or time-of-flight information, but require line-of-sight connectivity to at least three base stations. Channel charting, as put forward in [3], captures the local spatial geometry of an area by extracting relative location information directly from channel state information (CSI). The method collects the data from multiple users over time and generates the channel charts in a self-supervised fashion using machine learning techniques. While all of these methods have been proposed for conventional cellular wireless systems, not much is known about their efficacy for cell-free wireless networks, which consists of a very large number of access points (AP) distributed over a large area, communicating via a centralized processor. Having access to the extreme amount of measurements at the distributed APs has the potential to significantly increase localization accuracy in indoor and rural scenarios, while avoiding the need of labeled training data. <br />
<br />
This project will investigate self-supervised user positioning in cell-free massive MIMO systems using channel charting. More specifically, we will investigate scalable algorithms that enable accurate user localization from CSI using machine learning techniques that do not require labeled data (i.e., ground truth location information). The goal is to achieve meter-level accuracy in indoor and rural scenarios in a purely data-driven fashion. The techniques to be learned in this project include wireless communication, dimensionality reduction, deep neural networks, and manifold learning. <br />
<br />
References:<br />
<br />
[1] V. Savic and E. G. Larsson, "Fingerprinting-Based Positioning in Distributed Massive MIMO Systems," 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, 2015, pp. 1-5, doi: 10.1109/VTCFall.2015.7390953. [https://ieeexplore.ieee.org/document/7390953 Link]<br />
<br />
[2] K. N. R. S. V. Prasad, E. Hossain and V. K. Bhargava, "Machine Learning Methods for RSS-Based User Positioning in Distributed Massive MIMO," in IEEE Transactions on Wireless Communications, vol. 17, no. 12, pp. 8402-8417, Dec. 2018, doi: 10.1109/TWC.2018.2876832. [https://ieeexplore.ieee.org/document/8509634 Link]<br />
<br />
[3] C. Studer, S. Medjkouh, E. Gonultaş, T. Goldstein and O. Tirkkonen, "Channel Charting: Locating Users Within the Radio Environment Using Channel State Information," in IEEE Access, vol. 6, pp. 47682-47698, 2018, doi: 10.1109/ACCESS.2018.2866979. [https://ieeexplore.ieee.org/document/8444621 Link]<br />
<br />
[4] N. Garcia, H. Wymeersch, E. G. Larsson, A. M. Haimovich, M. Coulon, “Direct Localization for Massive MIMO,” IEEE Trans. Signal Processing, vol. 65, no. 10, pp. 2475-2487, May 2017. [https://ieeexplore.ieee.org/document/7849233 Link]<br />
<br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Student A, StudentB<br />
: Supervision: [[:User:Mluisier | Mathieu Luisier]]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_POS]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Self-Supervised_User_Positioning_in_Cell-Free_Massive_MIMO_Systems&diff=7306Self-Supervised User Positioning in Cell-Free Massive MIMO Systems2021-11-23T15:27:09Z<p>Vmenescal: </p>
<hr />
<div>[[File:Image_positioning.png|400px|thumb|Triangulation for user positioning in DM-MIMO [1].]]<br />
==Short Description==<br />
<br />
Massive multiple-input multiple-output (MIMO) systems have been extensively investigated in the past decade as one of the most attractive solutions for the fifth generation (5G) of wireless systems. One of the grand challenges in such systems is user positioning, which is critical for emergency services, area-specific advertisements, content caching, and autonomous driving [1,2]. Global navigation satellite systems (GNSS) are being extensively used to provide this kind of information. However, the base station does not have access to GNSS information and satellite-based localization is unreliable indoors and in dense rural scenarios. As a consequence, current research focuses on developing new strategies to use wireless signals to extract positioning data [2]. Emerging approaches that have been proposed for massive MIMO and distributed massive MIMO (DM-MIMO) systems are channel charting [3], fingerprinting techniques [1], and triangulation/trilateration [4].<br />
<br />
Fingerprinting techniques based on the received signal strength (RSS) and Gaussian process regression (GPR) have been applied to user positioning, but require extensive training with labeled data [1]. Triangulation and trilateration as described in [4], estimate the user’s position by jointly processing the RSS or time-of-flight information, but require line-of-sight connectivity to at least three base stations. Channel charting, as put forward in [3], captures the local spatial geometry of an area by extracting relative location information directly from channel state information (CSI). The method collects the data from multiple users over time and generates the channel charts in a self-supervised fashion using machine learning techniques. While all of these methods have been proposed for conventional cellular wireless systems, not much is known about their efficacy for cell-free wireless networks, which consists of a very large number of access points (AP) distributed over a large area, communicating via a centralized processor. Having access to the extreme amount of measurements at the distributed APs has the potential to significantly increase localization accuracy in indoor and rural scenarios, while avoiding the need of labeled training data. <br />
<br />
This project will investigate self-supervised user positioning in cell-free massive MIMO systems using channel charting. More specifically, we will investigate scalable algorithms that enable accurate user localization from CSI using machine learning techniques that do not require labeled data (i.e., ground truth location information). The goal is to achieve meter-level accuracy in indoor and rural scenarios in a purely data-driven fashion. The techniques to be learned in this project include wireless communication, dimensionality reduction, deep neural networks, and manifold learning. <br />
<br />
References:<br />
<br />
[1] V. Savic and E. G. Larsson, "Fingerprinting-Based Positioning in Distributed Massive MIMO Systems," 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, 2015, pp. 1-5, doi: 10.1109/VTCFall.2015.7390953. [https://ieeexplore.ieee.org/document/7390953 Link]<br />
<br />
[2] K. N. R. S. V. Prasad, E. Hossain and V. K. Bhargava, "Machine Learning Methods for RSS-Based User Positioning in Distributed Massive MIMO," in IEEE Transactions on Wireless Communications, vol. 17, no. 12, pp. 8402-8417, Dec. 2018, doi: 10.1109/TWC.2018.2876832. [https://ieeexplore.ieee.org/document/8509634 Link]<br />
<br />
[3] C. Studer, S. Medjkouh, E. Gonultaş, T. Goldstein and O. Tirkkonen, "Channel Charting: Locating Users Within the Radio Environment Using Channel State Information," in IEEE Access, vol. 6, pp. 47682-47698, 2018, doi: 10.1109/ACCESS.2018.2866979. [https://ieeexplore.ieee.org/document/8444621 Link]<br />
<br />
[4] N. Garcia, H. Wymeersch, E. G. Larsson, A. M. Haimovich, M. Coulon, “Direct Localization for Massive MIMO,” IEEE Trans. Signal Processing, vol. 65, no. 10, pp. 2475-2487, May 2017. [https://ieeexplore.ieee.org/document/7849233 Link]<br />
<br />
===Status: Available ===<br />
: Looking for 1-2 Semester/Master students<br />
: Contact: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjgxNDUz.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Victoria Menescal Tupper Palhares]<br />
<br />
===Prerequisites===<br />
: Communication Systems (''recommended'')<br />
<!-- <br />
===Status: Completed ===<br />
: Fall Semester 2014 (sem13h2)<br />
: Matthias Baer, Renzo Andri<br />
---><br />
<!-- <br />
===Status: In Progress ===<br />
: Student A, StudentB<br />
: Supervision: [[:User:Mluisier | Mathieu Luisier]]<br />
---><br />
===Character===<br />
: 20% Literature Research<br />
: 80% System Development<br />
<br />
===Professor===<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---><br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---><br />
<!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---><br />
<!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---><br />
: [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer]<br />
<!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---><br />
<br />
[[#top|↑ top]]<br />
==Detailed Task Description==<br />
<br />
===Goals===<br />
===Practical Details===<br />
* '''[[Project Plan]]'''<br />
* '''[[Project Meetings]]'''<br />
* '''[[Design Review]]'''<br />
* '''[[Coding Guidelines]]'''<br />
* '''[[Final Report]]'''<br />
* '''[[Final Presentation]]'''<br />
<br />
==Results== <br />
<br />
==Links== <br />
<br />
[[Category:Available]]<br />
[[Category:IIP]]<br />
[[Category:IIP_POS]]<br />
<br />
[[#top|↑ top]]<br />
<!-- <br />
<br />
COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES<br />
<br />
GROUP<br />
<br />
[[Category:cat2]]<br />
[[Category:cat3]]<br />
[[Category:cat4]]<br />
[[Category:cat5]]<br />
<br />
<br />
[[Category:Digital]]<br />
SUB CATEGORIES<br />
NEW CATEGORIES<br />
[[Category:Computer Architecture]]<br />
[[Category:Acceleration and Transprecision]]<br />
[[Category:Heterogeneous Acceleration Systems]]<br />
[[Category:Event-Driven Computing]]<br />
[[Category:Predictable Execution]]<br />
[[Category:SmartSensors]]<br />
[[Category:Transient Computing]]<br />
[[Category:System on Chips for IoTs]]<br />
[[Category:Energy Efficient Autonomous UAVs]]<br />
[[Category:Biomedical System on Chips]]<br />
[[Category:Digital Medical Ultrasound Imaging]]<br />
[[Category:Cryptography]]<br />
[[Category:Deep Learning Acceleration]]<br />
[[Category:Hyperdimensional Computing]] <br />
<br />
[[Category:Competition]] <br />
[[Category:EmbeddedAI]] <br />
<br />
<br />
[[Category:ASIC]]<br />
[[Category:FPGA]]<br />
<br />
[[Category:System Design]]<br />
[[Category:Processor]]<br />
[[Category:Telecommunications]]<br />
[[Category:Modelling]]<br />
[[Category:Software]]<br />
[[Category:Audio]]<br />
<br />
[[Category:Analog]]<br />
[[Category:Nano-TCAD]]<br />
<br />
[[Category:AnalogInt]]<br />
SUB CATEGORIES<br />
[[Category:Telecommunications]]<br />
<br />
<br />
STATUS<br />
[[Category:Available]]<br />
[[Category:In progress]]<br />
[[Category:Completed]]<br />
[[Category:Hot]]<br />
<br />
TYPE OF WORK<br />
[[Category:Group Work]]<br />
[[Category:Semester Thesis]]<br />
[[Category:Master Thesis]]<br />
[[Category:PhD Thesis]]<br />
[[Category:Research]]<br />
<br />
NAMES OF EU/CTI/NT PROJECTS<br />
[[Category:Oprecomp]]<br />
[[Category:Antarex]]<br />
[[Category:Hercules]]<br />
[[Category:Icarium]]<br />
[[Category:PULP]]<br />
[[Category:ArmaSuisse]]<br />
[[Category:Mnemosene]]<br />
[[Category:Aloha]]<br />
[[Category:Ampere]]<br />
[[Category:ExaNode]]<br />
[[Category:EPI]]<br />
[[Category:Fractal]]<br />
<br />
<br />
YEAR (IF FINISHED)<br />
[[Category:2010]]<br />
[[Category:2011]]<br />
[[Category:2012]]<br />
[[Category:2013]]<br />
[[Category:2014]]<br />
[[Category:2015]]<br />
[[Category:2016]]<br />
[[Category:2017]]<br />
[[Category:2018]]<br />
[[Category:2019]]<br />
[[Category:2020]]<br />
<br />
<br />
---><br />
Describe your new note here.</div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=File:Image_positioning.png&diff=7305File:Image positioning.png2021-11-23T15:26:04Z<p>Vmenescal: </p>
<hr />
<div></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5966Integrated Information Processing2020-11-12T11:52:10Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 15 530 630 90 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1315 90 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2010 90 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2720 90 [[All-Digital In-Memory Processing]]<br />
rect 15 1100 630 660 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
rect 705 1100 1315 660 [[Nonlinear DSP]]<br />
rect 1400 1100 2010 660 [[Real-Time Optimization]]<br />
rect 2100 1100 2720 660 [[Audio_Signal_Processing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
====[[Nonlinear DSP]]====<br />
<br />
<br />
====[[Real-Time Optimization]]====<br />
<br />
<br />
====[[Audio Signal Processing]]====<br />
<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList><br />
<br />
===[[Nonlinear DSP]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_DSP<br />
</DynamicPageList><br />
<br />
===[[Real-Time Optimization]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_OPT<br />
</DynamicPageList><br />
<br />
===[[Audio Signal Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_AUDIO<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5962Integrated Information Processing2020-11-12T11:49:16Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 15 530 630 90 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1315 90 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2010 90 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2720 90 [[All-Digital In-Memory Processing]]<br />
rect 15 1100 630 660 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
====[[Nonlinear DSP]]====<br />
<br />
<br />
====[[Real-Time Optimization]]====<br />
<br />
<br />
====[[Audio Signal Processing]]====<br />
<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList><br />
<br />
===[[Nonlinear DSP]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_DSP<br />
</DynamicPageList><br />
<br />
===[[Real-Time Optimization]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_OPT<br />
</DynamicPageList><br />
<br />
===[[Audio Signal Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_AUDIO<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5961Integrated Information Processing2020-11-12T11:49:04Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 15 530 630 90 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1315 90 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2010 90 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2720 90 [[All-Digital In-Memory Processing]]<br />
rect 15 1100 630 660 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
====[[Nonlinear DSP]]====<br />
<br />
Write here.<br />
<br />
====[[Real-Time Optimization]]====<br />
<br />
Write here.<br />
<br />
====[[Audio Signal Processing]]====<br />
<br />
Write here.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList><br />
<br />
===[[Nonlinear DSP]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_DSP<br />
</DynamicPageList><br />
<br />
===[[Real-Time Optimization]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_OPT<br />
</DynamicPageList><br />
<br />
===[[Audio Signal Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_AUDIO<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5959Integrated Information Processing2020-11-12T11:43:58Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 15 530 630 90 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1315 90 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2010 90 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2720 90 [[All-Digital In-Memory Processing]]<br />
rect 15 1100 630 660 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5957Integrated Information Processing2020-11-12T11:37:35Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 15 1100 630 660 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5956Integrated Information Processing2020-11-12T11:35:45Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 200 700 630 650 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5955Integrated Information Processing2020-11-12T11:35:16Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 200 700 630 600 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5954Integrated Information Processing2020-11-12T11:34:56Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 200 700 630 700 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5953Integrated Information Processing2020-11-12T11:34:44Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 200 700 630 500 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5952Integrated Information Processing2020-11-12T11:34:30Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 200 700 630 200 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5951Integrated Information Processing2020-11-12T11:33:51Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 700 630 200 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5950Integrated Information Processing2020-11-12T11:33:29Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 500 630 800 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5949Integrated Information Processing2020-11-12T11:33:07Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 200 630 800 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5948Integrated Information Processing2020-11-12T11:32:50Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 800 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5947Integrated Information Processing2020-11-12T11:32:38Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 500 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5946Integrated Information Processing2020-11-12T11:31:45Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 530 630 500 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5945Integrated Information Processing2020-11-12T11:31:27Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 500 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5944Integrated Information Processing2020-11-12T11:31:19Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 300 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5943Integrated Information Processing2020-11-12T11:31:12Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 200 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5942Integrated Information Processing2020-11-12T11:30:51Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 830 630 100 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5941Integrated Information Processing2020-11-12T11:30:37Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 530 630 100 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5940Integrated Information Processing2020-11-12T11:30:04Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 530 630 200 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5939Integrated Information Processing2020-11-12T11:29:51Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 530 630 500 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5938Integrated Information Processing2020-11-12T11:29:15Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
rect 0 530 630 500 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5937Integrated Information Processing2020-11-12T11:28:30Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
rect 2100 530 2730 100 [[All-Digital In-Memory Processing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5936Integrated Information Processing2020-11-12T11:25:40Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
rect 1400 530 2030 100 [[Simultaneous Sensing and Communication]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5935Integrated Information Processing2020-11-12T11:23:59Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1330 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5934Integrated Information Processing2020-11-12T11:23:38Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 630 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1350 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5933Integrated Information Processing2020-11-12T11:23:26Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1350 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5932Integrated Information Processing2020-11-12T11:23:12Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 530 1400 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5931Integrated Information Processing2020-11-12T11:22:56Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect -5 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 750 530 1400 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5930Integrated Information Processing2020-11-12T11:22:36Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 750 530 1400 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5929Integrated Information Processing2020-11-12T11:21:41Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 0 830 1000 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5928Integrated Information Processing2020-11-12T11:20:06Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 0 830 650 500 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5927Integrated Information Processing2020-11-12T11:19:21Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 0 830 650 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5926Integrated Information Processing2020-11-12T11:18:44Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 0 530 650 100 [[Positioning with Wireless Signals]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescalhttp://iis-projects.ee.ethz.ch/index.php?title=Integrated_Information_Processing&diff=5910Integrated Information Processing2020-11-12T10:04:01Z<p>Vmenescal: </p>
<hr />
<div>__NOTOC__<br />
<imagemap><br />
Image:IIP_overview_nov_2020-v1.png|800px<br />
rect 0 530 650 100 [[Theory, Algorithms, and Hardware for Beyond 5G]]<br />
rect 700 1000 650 0 [[Positioning with Wireless Signals]]<br />
rect 2278 1101 3418 0 [[Simultaneous Sensing and Communication]]<br />
rect 2278 1101 3418 0 [[All-Digital In-Memory Processing]]<br />
rect 2278 1101 3418 0 [[Analog-to-Information Conversion for Low-Power Sensing]]<br />
default [[Integrated Information Processing Group]]<br />
desc none<br />
</imagemap><br />
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}<br />
= Integrated Information Processing Group =<br />
The Integrated Information Processing (IIP) Group carries out research in the following areas:<br />
<br />
====[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]====<br />
<br />
The main focus of the IIP Group is on theory, algorithm design, and hardware implementation of new technologies for beyond fifth-generation (5G) wireless communication systems. The projects in this area focus on emerging communication technologies including massive MIMO, millimeter-wave (mmWave) and terahertz communication, cell-free massive MIMO, intelligent reflective surfaces, ultra low-latency short-packet transmission, and testbed design for massive MIMO prototyping.<br />
<br />
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====<br />
<br />
Indoor positioning and outdoor positioning in urban scenarios of mobile phones is a notoriously difficult task. Recently, tools from machine learning have been used to perform positioning from channel-state information (CSI). The projects in this area focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.<br />
<br />
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====<br />
<br />
Modern wireless systems are equipped with large arrays of parallel radio-frequency (RF) chains. Such RF chains are extremely accurate sensors that can be used not only for high-rate data transmission but also for sensing. The projects in the emerging area of simultaneous sensing and communication (SISCO) are on imaging the area next to the antenna array and on classification of user behavior using machine learning techniques.<br />
<br />
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====<br />
<br />
Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. The projects in this area are in designing all-digital and semi-custom PIM accelerators (application-specific integrated circuits) that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.<br />
<br />
====[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]====<br />
<br />
Always-on sensors that continuously monitor the environment for certain events must operate with energy-efficient classification and detection pipelines. The projects in this area build upon a novel classification pipeline developed in the IIP group called analog-to-feature (A2F) conversion that directly acquires features in the analog domain using non-uniform wavelet sampling (NUWS). Possible applications are real-time sensing and classification of EEG, ECG, RF, and audio signals.<br />
<br />
=Available Projects=<br />
<br />
===[[Theory, Algorithms, and Hardware for Beyond 5G|Theory, Algorithms, and Hardware for Beyond 5G]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_5G<br />
</DynamicPageList><br />
<br />
===[[Positioning with Wireless Signals|Positioning with Wireless Signals]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_POS<br />
</DynamicPageList><br />
<br />
===[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_SISCO<br />
</DynamicPageList><br />
<br />
===[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_PIM<br />
</DynamicPageList><br />
<br />
===[[Analog-to-Information Conversion for Low-Power Sensing|Analog-to-Information Conversion for Low-Power Sensing]]===<br />
<DynamicPageList><br />
suppresserrors = true<br />
category = Available<br />
category = IIP_A2F<br />
</DynamicPageList></div>Vmenescal