Joint Beamforming and Time Switching Designs for Energy-Constrained Cognitive Two-Way Relay Networks

作     者：Meijuan Zhang Shibing Zhang Zhihua Bao Wei Wang Xiaoge Zhang Yonghong Chen Meijuan Zhang;Shibing Zhang;Zhihua Bao;Wei Wang;Xiaoge Zhang;Yonghong Chen

作者机构：School of Information Science and TechnologyNantong UniversityNantong 226019China Nantong Research Institute for Advanced Communication TechnologyNantong 226019China Xinglin CollegeNantong UniversityNantong 226019China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第5期

页      面：110-118页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：National Natural Science Foundation of China (61871241, 61771263) Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18-2422) Six Categories Talent Peak of Jiangsu Province (KTHY-039) Science and Technology Program of Nantong (JC2018127, JC2018129, GY22017013) Stereoscopic Coverage Communication Network Verification Platform for China Sea (PCL2018KP002) Open Research Fund of Nantong University-Nantong Joint Research Center for Intelligent Information Technology (KFKT2017A05, KFKT2017B02) 

主　　题：cognitive networks two-way relay sum rate energy harvesting beamforming 

摘      要：In this paper, we investigate a joint beamforming and time switching(TS) design for an energy-constrained cognitive two-way relay(TWR) network. In the network, the energy-constrained secondary user(SU) relay employs TS protocol to harvest energy from the signals sent by the circuit-powered primary user(PU) transmitter, and then exploits the harvested energy to perform information forwarding. Our aim is to maximize the sum rate of SUs under the constraints of the data rate of PU, the energy harvesting and the transmit power of the SU relay. To determine the beamforming matrix and TS ratio, we decouple the original non-convex problem into two subproblems which can be solved by semidefinite relaxation and successive convex optimization methods. Furthermore, we derive closed form expressions of the optimal solutions for each subproblem, which facilitates the design of a suboptimal iterative algorithm to handle the original sum rate maximization problem. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.