News
Deep Learning for Physical Layer Communications: An Attempt towards 6G
Date: May 18, 8:00 PM EDT
Lecturer: Prof. Feifei Gao, Associate Professor, IEEE Fellow, Department of Automation, Tsinghua University, China.
Bio:
Prof. Gao’s research interest 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 papers and more than 150 IEEE conference proceeding papers that are cited more than 10000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor), IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Wireless Communications Letters, and China Communications. He has also serves as the symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018 IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM), 2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical Committee Members for more than 50 IEEE conferences.
Abstract:
Merging artificial intelligence into the system design has appeared as a new trend in wireless communications areas and has been deemed as one of the 6G technologies. In this talk, we will present how to apply the deep neural network (DNN) for various aspects of physical layer communications design, including the channel estimation, channel prediction, channel feedback, data detection, and beamforming, etc. We will also present a promising new approach that is driven by both the communications data and the communication models. It will be seen that the DNN can be used to enhance the performance of the existing technologies once there is model mismatch. More interestingly, we will show that applying DNN can deal with the conventionally unsolvable problems, thanks to the universal approximation capability of DNN. With the well-defined propagation model in communication areas, we also attempt to explain the DNN under the scenario of channel estimation and reach a strong conclusion that DNN can always provide the asymptotically optimal channel estimations. We have also build test-bed to show the effectiveness of the AI aided wireless communications. In all, DNN is shown to be a very powerful tool for communications and would make the communications protocols more intelligently. Nevertheless, as a new born stuff, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere.
Event Host(s):
Kingston Section Chapter, C16/COM19
Quebec Section Jt. Chapter, SP01/COM19
Windsor Section Joint Chapter, SP01/COM19
Saint Maurice Sect Chap, COM19
Toronto Section Chapter, COM19/BT02
Vancouver Jt Chpt VT06/COM19/PHO36/BT02/IT12/ITS38
Montreal Section Chapter, IT12/COM19
Canadian Atlantic Section Chapter, COM19
North Saskatchewan Sect. Jt. Chap,CAS04/COM19/SP01