Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments

Abstract

In this paper, we study a reconfigurable intelligent surface (RIS)-aided multiuser MISO system with imperfect hardware, where the transceiver design is based on the statistical channel state information (CSI). Considering the transceiver hardware impairments (HWI), we aim to maximize the minimum average user data rate, where the precoding matrices at the base station (BS) and the reflecting phase shifts at the RIS are jointly optimized. Since the problem is nonconvex and the objective function cannot be derived in closed form, we adopt the deep deterministic policy gradient (DDPG) algorithm to deal with this challenging optimization problem, where we generate a set of CSI vectors in an offline way, and then these data sets are used to train the neural networks. The simulation results demonstrate the rapid convergence speed of the adopted DDPG algorithm and also emphasize that it is crucial to consider the HWI when optimizing the transceiver.