Difference between revisions of "Low-Complexity MIMO Detection"
From iis-projects
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− | + | [[File:Low_complexity_mimo_detection.png|450px|thumb|High performance low-complexity iterative MIMO receiver.]] | |
+ | ==Short Description== | ||
− | Contact: Reinhard Wiesmayr | + | Iterative detection and decoding (IDD) is a performant, near-capacity achieving, MIMO detection and decoding scheme. However, due to multiple iterations, the complexity of the soft-input soft-output MIMO detector is of high relevance to outperform non-iterative methods. This project addresses the performance/complexity trade-off of state-of-the-art MIMO detection algorithms, with a special focus on GPU-based implementations. The objective of this project is to implement a low complexity MIMO detection algorithm and to optimize its performance with state-of-the-art machine learning methods. Therefore, a novel 5G-compliant link-level simulation and machine learning framework will be applied to implement performant GPU-based simulations. |
+ | |||
+ | ===Status: Available === | ||
+ | : Looking for 1-2 Semester/Bachelor/Master students | ||
+ | : Contact: [https://iip.ethz.ch/people/profiles.MzAxMjAz.TGlzdC80MTExLDEwNjY3Mjg3NDU=.html Reinhard Wiesmayr] | ||
+ | |||
+ | ===Prerequisites=== | ||
+ | : Basic programming skills (e.g. MATLAB, or Python/Numpy/Tensorflow) | ||
+ | : Wireless Communications | ||
+ | <!-- | ||
+ | ===Status: Completed === | ||
+ | : Fall Semester 2014 (sem13h2) | ||
+ | : Matthias Baer, Renzo Andri | ||
+ | ---> | ||
+ | <!-- | ||
+ | ===Status: In Progress === | ||
+ | : Student A, StudentB | ||
+ | : Supervision: [[:User:Mluisier | Mathieu Luisier]] | ||
+ | ---> | ||
+ | ===Character=== | ||
+ | : 70% Programming (e.g. with MATLAB, Python, Tensorflow) | ||
+ | : 20% Theory | ||
+ | : 10% Literature research | ||
+ | ===Professor=== | ||
+ | <!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini] ---> | ||
+ | <!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang] ---> | ||
+ | <!--: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=80923 Mathieu Luisier] ---> | ||
+ | <!--: [https://ee.ethz.ch/the-department/people-a-z/person-detail.MjUwODc0.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html Taekwang Jang] ---> | ||
+ | : [https://ee.ethz.ch/the-department/faculty/professors/person-detail.OTY5ODg=.TGlzdC80MTEsMTA1ODA0MjU5.html Christoph Studer] | ||
+ | <!-- : [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=79172 Andreas Schenk] ---> | ||
+ | |||
+ | [[#top|↑ top]] | ||
+ | ==Detailed Task Description== | ||
+ | |||
+ | ===Goals=== | ||
+ | ===Practical Details=== | ||
+ | * '''[[Project Plan]]''' | ||
+ | * '''[[Project Meetings]]''' | ||
+ | * '''[[Design Review]]''' | ||
+ | * '''[[Coding Guidelines]]''' | ||
+ | * '''[[Final Report]]''' | ||
+ | * '''[[Final Presentation]]''' | ||
+ | |||
+ | ==Results== | ||
+ | |||
+ | ==Links== | ||
+ | |||
+ | [[Category:Available]] | ||
+ | [[Category:IIP]] | ||
+ | [[Category:IIP_5G]] | ||
+ | |||
+ | [[#top|↑ top]] | ||
+ | <!-- | ||
+ | |||
+ | COPY PASTE FROM THE LIST BELOW TO ADD TO CATEGORIES | ||
+ | |||
+ | GROUP | ||
+ | |||
+ | [[Category:cat2]] | ||
+ | [[Category:cat3]] | ||
+ | [[Category:cat4]] | ||
+ | [[Category:cat5]] | ||
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+ | [[Category:Digital]] | ||
+ | SUB CATEGORIES | ||
+ | NEW CATEGORIES | ||
+ | [[Category:Computer Architecture]] | ||
+ | [[Category:Acceleration and Transprecision]] | ||
+ | [[Category:Heterogeneous Acceleration Systems]] | ||
+ | [[Category:Event-Driven Computing]] | ||
+ | [[Category:Predictable Execution]] | ||
+ | [[Category:SmartSensors]] | ||
+ | [[Category:Transient Computing]] | ||
+ | [[Category:System on Chips for IoTs]] | ||
+ | [[Category:Energy Efficient Autonomous UAVs]] | ||
+ | [[Category:Biomedical System on Chips]] | ||
+ | [[Category:Digital Medical Ultrasound Imaging]] | ||
+ | [[Category:Cryptography]] | ||
+ | [[Category:Deep Learning Acceleration]] | ||
+ | [[Category:Hyperdimensional Computing]] | ||
+ | |||
+ | [[Category:Competition]] | ||
+ | [[Category:EmbeddedAI]] | ||
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+ | |||
+ | [[Category:ASIC]] | ||
+ | [[Category:FPGA]] | ||
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+ | [[Category:System Design]] | ||
+ | [[Category:Processor]] | ||
+ | [[Category:Telecommunications]] | ||
+ | [[Category:Modelling]] | ||
+ | [[Category:Software]] | ||
+ | [[Category:Audio]] | ||
+ | |||
+ | [[Category:Analog]] | ||
+ | [[Category:Nano-TCAD]] | ||
+ | |||
+ | [[Category:AnalogInt]] | ||
+ | SUB CATEGORIES | ||
+ | [[Category:Telecommunications]] | ||
+ | |||
+ | |||
+ | STATUS | ||
+ | [[Category:Available]] | ||
+ | [[Category:In progress]] | ||
+ | [[Category:Completed]] | ||
+ | [[Category:Hot]] | ||
+ | |||
+ | TYPE OF WORK | ||
+ | [[Category:Group Work]] | ||
+ | [[Category:Semester Thesis]] | ||
+ | [[Category:Master Thesis]] | ||
+ | [[Category:PhD Thesis]] | ||
+ | [[Category:Research]] | ||
+ | |||
+ | NAMES OF EU/CTI/NT PROJECTS | ||
+ | [[Category:Oprecomp]] | ||
+ | [[Category:Antarex]] | ||
+ | [[Category:Hercules]] | ||
+ | [[Category:Icarium]] | ||
+ | [[Category:PULP]] | ||
+ | [[Category:ArmaSuisse]] | ||
+ | [[Category:Mnemosene]] | ||
+ | [[Category:Aloha]] | ||
+ | [[Category:Ampere]] | ||
+ | [[Category:ExaNode]] | ||
+ | [[Category:EPI]] | ||
+ | [[Category:Fractal]] | ||
+ | |||
+ | |||
+ | YEAR (IF FINISHED) | ||
+ | [[Category:2010]] | ||
+ | [[Category:2011]] | ||
+ | [[Category:2012]] | ||
+ | [[Category:2013]] | ||
+ | [[Category:2014]] | ||
+ | [[Category:2015]] | ||
+ | [[Category:2016]] | ||
+ | [[Category:2017]] | ||
+ | [[Category:2018]] | ||
+ | [[Category:2019]] | ||
+ | [[Category:2020]] | ||
+ | |||
+ | |||
+ | ---> |
Revision as of 13:33, 30 May 2022
Contents
Short Description
Iterative detection and decoding (IDD) is a performant, near-capacity achieving, MIMO detection and decoding scheme. However, due to multiple iterations, the complexity of the soft-input soft-output MIMO detector is of high relevance to outperform non-iterative methods. This project addresses the performance/complexity trade-off of state-of-the-art MIMO detection algorithms, with a special focus on GPU-based implementations. The objective of this project is to implement a low complexity MIMO detection algorithm and to optimize its performance with state-of-the-art machine learning methods. Therefore, a novel 5G-compliant link-level simulation and machine learning framework will be applied to implement performant GPU-based simulations.
Status: Available
- Looking for 1-2 Semester/Bachelor/Master students
- Contact: Reinhard Wiesmayr
Prerequisites
- Basic programming skills (e.g. MATLAB, or Python/Numpy/Tensorflow)
- Wireless Communications
Character
- 70% Programming (e.g. with MATLAB, Python, Tensorflow)
- 20% Theory
- 10% Literature research