Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI

TL;DR

This paper presents a deep reinforcement learning method for joint downlink beamforming and RIS configuration in MU-MISO systems, effectively handling hardware impairments and imperfect CSI to enhance system performance.

Contribution

It introduces the first DRL-based approach for phase-dependent RIS amplitude models in MU-MISO systems, addressing practical hardware impairments and CSI mismatches.

Findings

Significantly outperforms vanilla DRL under mismatch conditions

Approaches the performance of the ideal standard in practical scenarios

Demonstrates robustness to hardware impairments and CSI imperfections

Abstract

We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser multiple input single output (MU-MISO) system to maximize the sum downlink rate under the phase-dependent reflection amplitude model. Our approach addresses the challenge of imperfect channel state information (CSI) and hardware impairments by considering a practical RIS amplitude model. We compare the performance of our approach against a vanilla DRL agent in two scenarios: perfect CSI and phase-dependent RIS amplitudes, and mismatched CSI and ideal RIS reflections. The results demonstrate that the proposed framework significantly outperforms the vanilla DRL agent under mismatch and approaches the golden standard. Our contributions include modifications to the DRL approach to address the joint design of transmit beamforming and phase shifts and the phase-dependent amplitude model. To the best of our knowledge, our method is the first DRL-based approach for the phase-dependent reflection amplitude model in RIS-aided MU-MISO systems. Our findings in this study highlight the potential of our approach as a promising solution to overcome hardware impairments in RIS-aided wireless communication systems.