Antenna Selection in Energy Harvesting Relaying Networks Using Q-Learning Algorithm  

作　　者：Daliang Ouyang Rui Zhao Yuanjian Li Rongxin Guo Yi Wang 

机构地区：[1]College of Information Science and Engineering,Huaqiao University,Xiamen 361021,China [2]Centre for Telecommunications Research,King’s College London,London WC2R 2LS,U.K. [3]School of Electronics and Communication Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China [4]China National Digital Switching System Engineering and Technological Research Center,Zhengzhou 450002,China

出　　处：《China Communications》2021年第4期64-75,共12页中国通信（英文版）

基　　金：supported in part by the National Natural Science Foundation of China under Grant 61720106003,Grant 61401165,Grant 61379006,Grant 61671144,and Grant 61701538;in part by the Natural Science Foundation of Fujian Province under Grants 2015J01262;in part by Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under Grant ZQN-PY407;in part by Science and Technology Innovation Teams of Henan Province for Colleges and Universities(17IRTSTHN014);in part by the Scientific and Technological Key Project of Henan Province under Grant 172102210080 and Grant 182102210449;in part by the Collaborative Innovation Center for Aviation Economy Development of Henan Province。

摘　　要：In this paper,a novel opportunistic scheduling(OS)scheme with antenna selection(AS)for the energy harvesting(EH)cooperative communication system where the relay can harvest energy from the source transmission is proposed.In this considered scheme,we take into both traditional mathematical analysis and reinforcement learning(RL)scenarios with the power splitting(PS)factor constraint.For the case of traditional mathematical analysis of a fixed-PS factor,we derive an exact closed-form expressions for the ergodic capacity and outage probability in general signal-to-noise ratio(SNR)regime.Then,we combine the optimal PS factor with performance metrics to achieve the optimal transmission performance.Subsequently,based on the optimized PS factor,a RL technique called as Q-learning(QL)algorithm is proposed to derive the optimal antenna selection strategy.To highlight the performance advantage of the proposed QL with training the received SNR at the destination,we also examine the scenario of QL scheme with training channel between the relay and the destination.The results illustrate that,the optimized scheme is always superior to the fixed-PS factor scheme.In addition,a better system parameter setting with QL significantly outperforms the traditional mathematical analysis scheme.

关 键 词：Q-LEARNING optimal PS factor outage probability ergodic capacity antenna selection 

分 类 号：TN925[电子电信—通信与信息系统] TN820[电子电信—信息与通信工程]