T9: Deep Learning Empowered Large-Scale Antenna Systems
Organizer: Feifei Gao, Tsinghua University Beijing, China
Organizer: Shun Zhang, Xidian University, China
Organizer: Zhen Gao, Beijing Institute of Technology, China
Abstract: With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multi-output (MIMO), and reconfigurable intelligent surface (RIS) assisted communications. Meanwhile, it has been recently admitted that implementing deep learning (DL) into large-scale antenna communications will extensively benefit the system capacity and enhance the robustness to complicated transmission environments. Different from traditional model-driven approaches, DL can help deal with the existing communications and signal processing problems in a data driven perspective by digging the inherent characteristic from the real data. Thus, DL is particularly suitable for large-scale antenna systems under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. Motivated by this, this tutorial aims to provide the audience a general picture of the recent developments in this exciting area. Specifically, in this interactive presentation we will introduce the merging of DL and large-scale antenna systems, over various topics, including channel acquisition, signal detection, and beam forming design, etc. We will also discuss the challenges of DL empowered large-scale antenna systems and present some interesting future directions.
Bio: Feifei Gao (F’20) received the B.Eng. degree from Xi’an Jiaotong University, Xi’an, China, in 2002, the M.Sc. degree from McMaster University, Hamilton, ON, Canada, in 2004, and the Ph.D. degree from the National University of Singapore, Singapore, in 2007. Since 2011, he has been with the Department of Automation, Tsinghua University, Beijing, China, where he is currently an Associate Professor. His research interests include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence assisted communications. He has authored/coauthored more than 150 refereed IEEE journal articles and more than 150 IEEE conference proceeding papers that are cited more than 8800 times in Google Scholar. He has served as a technical committee member for more than 50 IEEE conferences. He has also served as the Symposium Co-Chair of the 2019 IEEE International Conference on Communications (ICC), the 2018 IEEE Vehicular Technology Conference (VTC) Spring, the 2015 IEEE International Conference on Communications (ICC), the 2014 IEEE Global Communications Conference (GLOBECOM), and the 2014 IEEE Vehicular Technology Conference (VTC) Fall. He has served as an Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Cognitive Communications and Networking, IEEE Wireless Communications Letters, and China Communications, a Lead Guest Editor for IEEE Journal of Selected Topics in Signal Processing, and a Senior Editor for IEEE Signal Processing Letters and IEEE Communications Letters.
Bio: Shun Zhang (Senior Member, IEEE) received the B.S. degree in communication engineering from Shandong University, Jinan, China, in 2007, and the Ph.D. degree in communications and signal processing from Xidian University, Xi’an, China, in 2013. He is currently with the State Key Laboratory of Integrated Services Networks, Xidian University, where he is currently an Associate Professor. His research interests include massive MIMO, millimeter wave systems, RIS assisted communications, deep learning for communication systems, orthogonal time frequency space (OTFS) systems, and multiple access techniques. He is an Editor for Physical Communication. He has authored or coauthored more than 80 journal and conference papers, and is the inventor of 16 granted patents (including a PCT patent authorized by US Patent and Trademark Office ). He has received two Best Paper Awards in conferences, and two prize awards in natural sciences for research excellence by both China Institute of Communications and Chinese Institute of Electronics.
Bio: Zhen Gao (Member, IEEE) received the B.S. degree in information engineering from the Beijing Institute of Technology, Beijing, China, in 2011, and the Ph.D. degree in communication and signal processing with the Department of Electronic Engineering, Tsinghua University, China, in 2016. He is currently an Assistant Professor with the Beijing Institute of Technology. His research interests are in wireless communications, with a focus on multi-carrier modulations, multiple antenna systems, and sparse signal processing. He was a recipient of the IEEE Broadcast Technology Society 2016 Scott Helt Memorial Award (Best Paper), the Exemplary Reviewer of IEEE COMMUNICATION LETTERS in 2016, IET Electronics Letters Premium Award (Best Paper) 2016, UCET 2020 Best Paper Award, and the Young Elite Scientists Sponsorship Program (2018–2020) from China Association for Science and Technology.