报告题目：Deep Learning Approach to Precoding for Massive MIMO Systems
讲座嘉宾：Wei Yu，加拿大多伦多大学教授、IEEE Fellow，加拿大工程院院士
讲座时间：2020.11.27（周五）上午 10:00-11:00
讲座地点：深圳大学沧海校区致信楼电子与信息工程学院N710
报告摘要： In this talk, we propose a deep learning approach to channel sensing and downlink precoding for both the time-domain duplex (TDD) and the frequency-domain duplex (FDD) massive MIMO systems at the mmWave frequency. The first part of the talk considers a TDD system employing hybrid analog and digital beamforming. Instead of the conventional approach of first estimating the channel followed by precoder design, we show that the channel sensing and the downlink precoding matrices can be designed directly from the received pilots using a deep learning approach without the intermediate channel estimation step. The proposed methodology is generalizable with respect to the number of users and requires significantly less training overhead than the conventional channel recovery based approach. In the second part of the talk, we further show that deep learning can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for an FDD cellular system with rate-limited feedback. The key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem, in contrast to the conventional approach where the channel state information is independently quantized at each user. We show that a deep learning architecture that maps the received pilots directly into finite feedback bits at the user side, then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall system performance.
报告人：: Wei Yu received the B.A.Sc. degree in Computer Engineering and Mathematics from the University of Waterloo Canada in 1997 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, U.S.A., in 1998 and 2002, respectively. Since 2002, he has been with the Electrical and Computer Engineering Department at the University of Toronto, Canada, where he is now Professor and holds a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications. Prof. Wei Yu is a Fellow of IEEE, a Fellow of the Canadian Academy of Engineering, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. He received the Steacie Memorial Fellowship in 2015, the IEEE Marconi Prize Paper Award in Wireless Communications in 2019, the IEEE Communications Society Award for Advances in Communication in 2019, the IEEE Signal Processing Society Best Paper Award in 2017 and 2008, the Journal of Communications and Networks Best Paper Award in 2017, and the IEEE Communications Society Best Tutorial Paper Award in 2015. He is currently Vice President and President-Elect of the IEEE Information Theory Society, and has served on its Board of Governors since 2015. He served as the Chair of the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society in 2017-18. Prof. Wei Yu was an IEEE Communications Society Distinguished Lecturer in 2015-16. He served as the TPC co-Chair of the IEEE International Symposium on Information Theory in 2020. He is currently an Area Editor for the IEEE Transactions on Wireless Communications, and in the past served as an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Communications, and IEEE Transactions on Wireless Communications.