Prof. Caijun Zhong

Title: Intelligent Reflecting Surfaces Assisted Wireless Communications


Intelligent reflecting surface (IRS) is an artificial planar structure made of sub-wavelength unit cells with adjustable electromagnetic responses, which has the potential to manipulate the propagation environments in an intelligent manner. Therefore, IRS has been envisioned as a promising technique to build spectral and energy efficient wireless communication systems, and has received considerable research interests from the community. Motivated by this, in this talk, we first introduce the basic of IRS, followed by its potential application scenarios in wireless communications. Then we elaborate on recent advances of IRS assisted communications. Finally, we discuss some important future directions.


Caijun Zhong is a Full Professor at Zhejiang University, China. He received the Ph.D. degree in Electronic and Electrical Engineering in 2010 from University College London, UK. His main research interests include intelligent reflecting surface assisted wireless communications, wireless powered communications and massive MIMO communications. He authored over 150+ papers, appeared in top-level international peer-reviewed journals and conference proceedings, five of which received the best paper award, including ICC 2019, ICCC 2018, etc. He is currently on the Editorial Board of IEEE Transactions on Wireless Communications, Science China: Information Science, and China Communications. He served as the symposium co-chairs in IEEE Global Communication Conference (GLOBECOM 2018) and IEEE International Conference on Communications in China (ICCC 2017). He is the recipient of 2013 IEEE Asia Pacific (AP) Outstanding Young Researcher Award. 

Prof. Feifei Gao

IEEE Fellow
Tsinghua University, China

Title: Deep Learning for Physical Layer Communications: An Attempt Towards 6G


Merging artificial intelligence  in to 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. 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.


Feifei Gao received the B.Eng. degree from Xi’an Jiaotong University, China in 2002, the M.Sc. degree from McMaster University, Canada in 2004, and the Ph.D. degree from National University of Singapore in 2007. He was a Research Fellow with the Institute for Infocomm Research (I2R), A*STAR, Singapore in 2008 and an Assistant Professor with the School of Engineering and Science, Jacobs University, Bremen, Germany from 2009 to 2010. In 2011, he joined the Department of Automation, Tsinghua University, China, where he is currently an Associate Professor.

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 9000 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 served 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.