学术报告

报告题目：Resource Allocation for Cognitive Small Cell Networks

报告人：张海君博士

时间：2016年5月6日9：00

地点：仙林校区6号学科楼327

报告人简介：Haijun Zhang is a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Vancouver, Canada. He received his Ph.D. degree in Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts Telecommunications (BUPT). From September 2011 to September 2012, he visited Centre for Telecommunications Research, King’s College London, London, UK, as a joint Ph.D. student and Visiting Research Associate. Dr. Zhang has published more than 70 papers and authored 2 books. He serves as Editors of Journal of Network and Computer Applications, Wireless Networks, Telecommunication Systems, KSII Transactions on Internet and Information Systems, and Leading Guest Editor of ACM/Springer Mobile Networks & Applications (MONET). He serves as General Chair of GameNets'16, and served as Symposium Chair of the GameNets'14 and Track Chair of ScalCom2015. He also serves or served as TPC member of many conferences, such as Globecom and ICC. His current research interests include 5G, Resource Allocation, Heterogeneous Small Cell Networks and Ultra-Dense Networks.

报告内容：Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently,we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.