Topic:A Stochastic Multi-channel Spectrum Access Game with Incomplete Information

Time:2015年4月21日（周二）下午14:00-15:30

Venue:信电大楼-215学术厅

Speaker:Huaiyu Dai, Associated Professor

           Electrical and Computer Engineering

          North Carolina State University ，USA

Biography

Huaiyu Dai (M’03, SM’09) received the B.E. and M.S. degrees in electrical engineering from Tsinghua University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2002. He was with Bell Labs, Lucent Technologies, Holmdel, NJ, during summer 2000, and with AT&T Labs-Research, Middletown, NJ, during summer 2001. Currently he is an Associate Professor of Electrical and Computer Engineering at NC State University, Raleigh. His research interests are in the general areas of communication systems and networks, advanced signal processing for digital communications, and communication theory and information theory. His current research focuses on networked information processing and cross layer design in wireless networks, cognitive radio networks, wireless security, and associated information-theoretic and computation-theoretic analysis.  He has served as an editor of IEEE Transactions on Communications, Signal Processing, and Wireless Communications. He co-edited two special issues of EURASIP journals on distributed signal processing techniques for wireless sensor networks, and on multiuser information theory and related applications, respectively. He co-chairs the Signal Processing for Communications Symposium of IEEE Globecom 2013, the Communications Theory Symposium of IEEE ICC 2014, and the Wireless Communications Symposium of IEEE Globecom 2014.

Abstract

To ensure continuous functioning and satisfactory performance, a wireless communication system has to not only learn and adapt to the unknown and ever-changing wireless environment, but also strategically deal with the usually unfamiliar peers. Incomplete information stochastic game (SG) is a promising model for the corresponding analysis and strategy design. In this work, an exemplary multi-channel spectrum access game (SAG) with unknown environment dynamics and limited information of the other player is considered to illustrate the proposed solution for the corresponding incomplete information SG. To find the best communication strategy in the face of uncertainty, a joint reinforcement learning and type identification algorithm is developed, which is provably convergent under certain technical conditions. Numerical results show that using the proposed algorithm, a wireless user can gradually achieve the same performance as that in the corresponding complete information game.