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(Theory, Algorithms, and Hardware for Beyond 5G)
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====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====
 
====[[Positioning with Wireless Signals|Positioning with Wireless Signals]]====
  
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). Most projects focus on channel charting, a new technology developed in the IIP group that enables self-supervised positioning from CSI without the users' consent.  
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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.  
  
 
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====
 
====[[Simultaneous Sensing and Communication|Simultaneous Sensing and Communication]]====
  
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. Most projects in the realm of the emerging paradigm 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.
+
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.
  
 
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====
 
====[[All-Digital In-Memory Processing|All-Digital In-Memory Processing]]====

Revision as of 13:14, 19 August 2020


Integrated Information Processing Group

The Integrated Information Processing (IIP) Group carries out research in the following areas:

Theory, Algorithms, and Hardware for Beyond 5G

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.

Positioning with Wireless Signals

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.

Simultaneous Sensing and Communication

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.

All-Digital In-Memory Processing

Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. Most projects in this research area are in designing all-digital and semi-custom PIM accelerators that can be fabricated with conventional CMOS technologies and for emerging applications in machine learning, signal processing, and wireless communication.

Analog-to-Information Conversion for Low-Power Sensing

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.

Available Projects