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T6: Deep Learning for Wireless Communications - VTC2021-Fall

T6: Deep Learning for Wireless Communications

Organizer: Geoffrey Ye Li, Imperial College London, UK
Organizer: Zhijin Qin, Queen Mary University of London, UK

Abstract: In the tutorial, we will provide a comprehensive overview on DL for wireless communications, including physical layer processing, resource allocation, and semantic communications.

We first present progress in DL in physical layer communications. we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we introduce joint channel estimation and signal detection based on a fully connected deep neural network, modeldrive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems.

In the second part of this tutorial, we will present recent progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first introduce how to use deep learning to solve optimization problems for resource allocation. We will then discuss deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.

Enabled by deep learning, semantic communications are promising to further improve the communication system efficiency, which is regarded as the second level of communications by Shannon and Weaver in addition to typical communications focusing on successful transmission of symbols. Semantic communications aim to realize the successful semantic information exchange rather than receive the transmitted bit sequences or symbols accurately. In this part, we will first introduce the concept of the semantic communication. We then detail the principles and performance metrics of semantic communications. Afterwards, we will present the initial work on deep learning enabled semantic communications and research challenges.


Bio: Dr. Geoffrey Ye Li is currently a Chair Professor in wireless systems with Imperial College London. Before joining Imperial in 2020, he was with Georgia Institute of Technology for 20 years and AT&T (Bell) Labs – Research for about five years. His general research interests include statistical signal processing and machine learning for wireless communications. In the related areas, he has published over 500 journal and conference papers in addition to over 40 granted patents. His publications have been cited by over 40,000 times and he has been recognized as the World’s Most Influential Scientific Mind, also known as a Highly Cited Researcher, by Thomson Reuters almost every year.

Dr. Li was awarded IEEE Fellow for his contributions to signal processing for wireless communications in 2005. He won several prestigious awards from IEEE Signal Processing Society (Donald G. Fink Overview Paper Award in 2017), IEEE Vehicular Technology Society (James Evans Avant Garde Award in 2013 and Jack Neubauer Memorial Award in 2014), and IEEE Communications Society (Stephen O. Rice Prize Paper Award in 2013, Award for Advances in Communication in 2017, and Edwin Howard Armstrong Achievement Award in 2019). He also received 2015 Distinguished ECE Faculty Achievement Award from Georgia Tech.

Dr. Li has organized and chaired many international conferences and has been involved in editorial activities of many journals, including the founding Editor-in-Chief of IEEE JSAC ML Series.

Dr. Li once provided 4 different tutorials at IEEE ICC, Globecom, and VTC for 27 times in total.


Bio:  Dr. Zhijin Qin is a lecturer (assistant professor) at Queen Mary University of London. She was with Lancaster University and Imperial College London as a lecturer and research associate, respectively, from 2016 to 2018. Her research interests include semantic communications, end-to-end communications, resource allocation in LoRa. She serves as an area editor of IEEE JSAC Series on Machine learning in Communications and Networks, an associate editor of IEEE Transactions on Communications, IEEE Transactions on Cognitive Communications and Networking, and IEEE Communications Letters. She served as the symposium co-chair for IEEE VTC Fall 2019, Globecom 2020/2021. She received the Best Paper Award from IEEE Globecom 2017, and the IEEE Signal Processing Society Young Author Best Paper Award 2018. Dr Qin has presented the tutorial at IEEE Globecom 2020.