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

Abstract

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.