Deep Reinforcement Learning Based Power Minimization for RIS-Assisted MISO-OFDM Systems  

作　　者：Peng Chen Wenting Huang Xiao Li Shi Jin 

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

出　　处：《China Communications》2023年第4期259-269,共11页中国通信（英文版）

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

摘　　要：In this paper,we investigate the downlink orthogonal frequency division multiplexing(OFDM)transmission system assisted by reconfigurable intelligent surfaces(RISs).Considering multiple antennas at the base station(BS)and multiple single-antenna users,the joint optimization of precoder at the BS and the phase shift design at the RIS is studied to minimize the transmit power under the constraint of the certain quality-of-service.A deep reinforcement learning(DRL)based algorithm is proposed,in which maximum ratio transmission(MRT)precoding is utilized at the BS and the twin delayed deep deterministic policy gradient(TD3)method is utilized for RIS phase shift optimization.Numerical results demonstrate that the proposed DRL based algorithm can achieve a transmit power almost the same with the lower bound achieved by manifold optimization(MO)algorithm while has much less computation delay.

关 键 词：deep reinforcement learning OFDM PRECODING reconfigurable intelligent surface 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]