Learning to maximize a convex quadratic function with application to intelligent reflection surface for wireless communication
ID:73 View Protection:ATTENDEE Updated Time:2022-10-11 13:39:48 Hits:587 Invited speech

Start Time:2022-10-21 10:30(Asia/Shanghai)

Duration:60min

Session:P Plenary Session » P3Plenary Session 3

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Abstract
In this talk we consider learning and optimizing a rank-2 convex quadratic function over N variables, each taking K discrete values on the unit circle. This problem arises from optimal design of a passive beamformer for intelligent reflecting surface (IRS) in order to maximize the overall channel strength. When K=2 and the quadratic function (or channel state information) is known, we propose a linear time algorithm that is capable of reaching a globally optimal solution of the problem. When the quadratic function is unknown (i.e. in the absence of channel state information) we propose a random max sampling (RMS) method and a conditional sample mean (CSM) method to maximize the quadratic function. We show that RMS method can provide a SNR boost that is linear in N (the number of reflective elements in IRS),while the CSM can boost the SNR quadratically (in N), all with polynomial number of samples. Field trial results demonstrate the effectiveness of the proposed CSM method in the commercial 5G communication networks, providing >10dB SNR gain in both typical indoor and outdoor scenarios, and with no need to modify the current communication protocols and design.
 
Keywords
Speaker
Zhi-Quan (Tom) Luo
Vice President (Acad The Chinese University of Hong Kong

Zhi-Quan (Tom) Luo is the Vice President (Academic) at The Chinese University of Hong Kong, Shenzhen where he has been a professor since 2014. He is the Director of Shenzhen Research Institute of Big Data and also the Chinese University of Hong Kong (Shenzhen)-Shenzhen Research Institute of Big Data-Huawei Innovation Laboratory of Future Network System Optimization. He completed his Ph.D. at the Massachusetts Institute of Technology and his undergraduate studies at Peking University, China. His research interests lie in the area of big data, signal processing and digital communication, ranging from theory to design to implementation. He has served on more than forty conference and workshop program committees and been the Chair of the IEEE Signal Processing Society Technical Committee on Signal Processing for Communications (SPCOM). He was the Editor in Chief for IEEE Transactions on Signal Processing from 2012 to 2014 and served as the Associate Editor for many internationally recognized journals. Currently, he is the Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Society for Industrial and Applied Mathematics (SIAM). He was elected to Foreign Member of the Chinese Academy of Engineering (CAE) in 2021. He received the 2010 Farkas Prize from the INFORMS Optimization Society. He also received three Best Paper Awards from the IEEE Signal Processing Society in 2004, 2009 and 2011 respectively, and a 2011 Best Paper Award from the EURASIP. In 2014, he was elected to the Royal Society of Canada. In 2018, he was awarded the prize of Paul Y. Tseng Memorial Lectureship in Continuous Optimization.

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Important Date
  • Conference Date

    Oct 19

    2022

    to

    Oct 22

    2022

Sponsored By
Zhejiang University
Organized By
Zhejiang University