Discrete phase shifts control and beam selection in RIS-aided MISO system via deep reinforcement learning

Abstract

Reconfigurable intelligent surface (RIS) for wireless networks have drawn lots of attention in both academic and industry communities. RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction, thus it provides supplementary links for wireless networks. Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts, but phase shifts of RIS are discrete in practical hardware. Thus we focus on the actual discrete phase shifts on RIS in this paper. Using the advanced deep reinforcement learning (DRL), we jointly optimize the transmit beamforming matrix from the discrete Fourier transform (DFT) codebook at the base station (BS) and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio (SINR). Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts, the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network. Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.