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Energy Harvesting Cognitive Non-Orthogonal Multiple Access: Optimal Resource Allocation
时间:2019-05-13 17:03    点击:   所属单位:通信工程学院
讲座名称 Energy Harvesting Cognitive Non-Orthogonal Multiple Access: Optimal Resource Allocation
讲座时间 2019-05-16 09:30:00
讲座地点 西电北校区新科技楼 1012会议室
讲座人 Prof. Hai Jiang
讲座人介绍
Hai Jiang received the B.Sc. and M.Sc. degrees in electronics engineering from Peking University, Beijing, China, in 1995 and 1998, respectively, and the Ph.D. degree in electrical engineering from the University of Waterloo, Waterloo, Ontario, Canada, in 2006. Since July 2007, he has been a faculty member with the University of Alberta, Edmonton, Alberta, Canada, where he is currently a Professor at the Department of Electrical and Computer Engineering. Dr. Jiang received an Alberta Ingenuity New Faculty Award in 2008, a Best Paper Award from the IEEE Global Communications Conference (GLOBECOM) in 2008, a Faculty of Engineering Research Award from the Faculty of Engineering, University of Alberta, in 2015, and a Best Paper Award from the Green Communications & Computing Technical Committee, IEEE Communications Society, in 2018. His research interests include radio resource management, cognitive radio networking, and cooperative communications.
讲座内容
 In this talk, we will discuss a cognitive radio system, in which a secondary transmitter harvests energy from a primary transmitter's RF signal. The secondary transmitter, which provides decode-and-forward relaying service for the primary system, transmits its own data by using downlink non-orthogonal multiple access (NOMA). A time-switching protocol is used by the secondary transmitter to harvest energy and decode the primary transmitter's information. Our objective is to achieve maximal secondary throughput, by optimally selecting the time portion used for energy harvesting and the secondary transmitter's power allocation in NOMA transmission. Two optimization problems are formulated, in which the secondary receiver performs or does not perform successive interference cancellation (SIC), respectively. Although the two problems are nonconvex, we devise a method to transform the problems into equivalent problems under difference cases. Then we theoretically prove that the objective functions of the equivalent problems are quasiconcave, based on which we develop two-level bisection search algorithms to solve the equivalent problems. Interestingly, we show that performing SIC at the secondary receiver does not always guarantee a higher secondary throughput than the case without performing SIC.
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