【Academic Forum】Learning to maximize a convex quadratic function with application to intelligent reflection surface for wireless communication

Topic：Learning to maximize a convex quadratic function with application to intelligent reflection surface for wireless communication
Lecturer：Luo Zhiquan  Fellow of the Royal Society of Canada,Vice President and Professor at The Chinese University of Hongkong (CUHK) (Shenzhen), Fellow of IEEE/SIAM  
Host：Ge Fei   Vice President and Professor at Xiangtan University
Time：15:00-16:00 PM, September 18, 2021
Venue：Room 308, South Building, School of Mathematics and Computational Science
Abstract：
In this talk we consider learning and optimizing a rank-2 convex quadratic function over K discrete variables. This problem arises from optimal design of a passive beamformer for intelligent reflecting surface (IRS) in order to maximize the overall channel strength. When the quadratic function (or channel state information) is known, we propose a linear time algorithm that is capable of reaching a near-optimal solution with an approximation ratio of (1+cos(π/K))/2, i.e., its performance is at least 75% of the global optimum for K ≥ 3. Furthermore we develop methods to learn and optimize the beamforming strategy when the quadratic function is unknown (i.e. in the absence of channel state information).
Profile：
Luo Zhiquan has long been engaged in research on optimization theory, algorithm design and wireless communication; related papers have been rated as the Best Paper of the Year by IEEE and other authoritative academic institutions 7 times; won the Farkas Prize ( USA ) and Paul Y. Tseng Memorial Lectureship in Continuous Optimization. In 2020, he was engaged as the Director of eLab by Huawei. The 5G network optimization technology that he presided over for R & D has been implemented on the GTS platform of Huawei.　

Hunan National Center for Applied Mathematics
Academic Affairs Office of Xiangtan University