Topic：Using Control Theory in Wireless Communications: Convergence of Distributed
Algorithms in Stochastic Wireless Networks
Speaker：Prof. Vincent Lau (Hong Kong University of Science and Technology, Hong Kong)
Vincent obtained B.Eng (Distinction 1st Hons) from the University of Hong Kong (1989-1992) and Ph.D. from the Cambridge University (1995-1997). He completed the Ph.D. degree in two years and joined Bell Labs from 1997-2004. He joined the Department of ECE, Hong Kong University of Science and Technology (HKUST) in 2004 and is currently a Professor and the Founding Director of Huawei-HKUST Joint Innovation Lab at HKUST. He is also elected as IEEE Fellow, Croucher Senior Research Fellow and Changjiang Chair Professor. Vincent has published more than 200 IEEE journal and conference papers and has contributed to 28 US patents on various wireless systems. In addition, he is also the key contributor of four IEEE standard contributions to IEEE 802.22 (WRAN / Cognitive Radio). His current research focus includes robust cross layer optimization for MIMO/OFDM wireless systems, interference mitigation techniques for wireless networks, delay-optimal cross layer optimizations as well as multi-timescale stochastic network optimization. He has obtained three IEEE best paper awards and is currently an area editor of IEEE Transactions on Wireless Communications, area editor of IEEE Signal Processing Letters, EUARSIP Wireless Communications and Networking as well as guest editor of JSAC.
Distributive and Iterative Algorithms play a very important role in wireless resource optimizations or game problems in wireless networks. One important issue associated with distributed algorithm is on the convergence analysis. Traditional convergence analysis is all based on an important assumption that the wireless channels are static during the iterations. However, this assumption is quite unrealistic in practice if the algorithms involve over-the-air iterations. In this case, the channel fading will change well before the algorithm iteration converges and the analysis of convergence behavior under such dynamic environment is very challenging. In this talk, we shall try to address the convergence behavior of iterative algorithms under time-varying channels. We shall first propose a systematic framework to analyze the “algorithm trajectory” and the “time varying target” using non-linear control theory. We found that the time-varying channels act like an "external force" in a virtual dynamic system. Based on the framework, we can sometimes improve the convergence of distributed algorithms using compensation method.