Shunqing Zhang


Prof. Shunqing Zhang, Shanghai University, China
Title: From GREAT to GREAT - Wireless Communications and Deep Learning
Abstract: Energy efficiency has become a basic performance measure for 5G communication systems. In this talk, we will first review the past fundamental tradeoffs and the corresponding energy efficient schemes in 4G communication systems that were raised by Green Radio Excellence in Architecture and Technology (GREAT) project 5 years ago. Meanwhile, to deliver energy efficient schemes, we established the Group of Research and Education in AI and Telecommunication (GREAT) team to combine the machine learning techniques with wireless big data. Specifically, we will use the machine learning technology to design the reconfigurable decoder, dynamic carrier and power amplifier mapping, as well as wireless localization problems.
Short bio: Shunqing Zhang received the B.S. degree from the Department of Microelectronics, Fudan University, Shanghai, China, in 2005, and the Ph.D. degree from the Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, in 2009. He was with the Communication Technologies Laboratory, Huawei Technologies, as a Research Engineer and then a Senior Research Engineer from 2009 to 2014, and a Senior Research Scientist of Intel Collaborative Research Institute on Mobile Networking and Computing, Intel Labs from 2015 to 2017. Since 2017, he has been with the School of Communication and Information Engineering, Shanghai University, Shanghai, China, as a Full Professor. His current research interests include energy efficient 5G/5G+ communication networks, hybrid computing platform, and joint radio frequency and baseband design. He has published over 60 peer-reviewed journal and conference papers, as well as over 50 granted patents. He is a senior member of IEEE, received the National Young 1000-Talents Program, and won the paper award for Advances in Communications from IEEE Communications Society in 2017.