DRL-based max-min fair RIS discrete phase shift optimization for MISO-OFDM systems  

作　　者：Peng Chen Huaqian Zhang Xiao Li Shi Jin 

机构地区：[1]National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China

出　　处：《Journal of Information and Intelligence》2023年第3期281-293,共13页信息与智能学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China(62231009,61971126,62261160576,and 61921004);in part by the Natural Science Foundation of Jiangsu Province(BK20211511);in part by the Jiangsu Province Frontier Leading Technology Basic Research Project(BK20212002).

摘　　要：In this paper,we investigate a reconfigurable intelligent surface(RIS)assisted downlink orthogonal frequency division multiplexing(OFDM)transmission system.Taking into account hardware constraint,the RIS is considered to be organized into several blocks,and each block of RIS share the same phase shift,which has only 1-bit resolution.With multiple antennas at the base station(BS)serving multiple single-antenna users,we try to design the BS precoder and the RIS reflection phase shifts to maximize the minimum user spectral efficiency,so as to ensure fairness.A deep reinforcement learning(DRL)based algorithm is proposed,in which maximum ratio transmission(MRT)precoding is utilized at the BS and the dueling deep Q-network(DQN)framework is utilized for RIS phase shift optimization.Simulation results demonstrate that the proposed DRL-based algorithm can achieve almost optimal performance,while has much less computation consumption.

关 键 词：Fairness transmission Reconfigurable intelligent surface(RIS) Orthogonal frequency division multiplexing (OFDM) Deep reinforcement learning(DRL) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]