Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System

Abstract

Due to the benefits of the spectrum and energy efficiency, intelligent reflecting surfaces (IRSs) are regarded as a promising technology for future networks. In this work, we consider a single cellular network where multiple IRSs are deployed to assist the downlink transmissions from the base station (BS) to multiple user equipment (UE). Hence, we aim to jointly optimize the configuration of the BS active beamforming and reflection beamforming of the IRSs that meet the UE’s QoS while allowing the lowest transmit power consumption at the BS. Although the conventional alternating approach is widely used to find converged solutions, its applicability is restricted by high complexity, which is more severe in a dynamic environment. Consequently, an alternative approach, i.e., machine learning (ML), is adopted to find the optimal solution with lower complexity. For the static UE scenario, we propose a low-complexity optimization algorithm based on the new generalized neural network (GRNN). Meanwhile, for the dynamic UE scenario, we propose a deep reinforcement learning (DRL)-based optimization algorithm. Specifically, a deep deterministic policy gradient (DDPG)-based algorithm is designed to address the GRNN algorithm’s restrictions and efficiently handle the dynamic UE scenario. Simulation results confirm that the proposed algorithms can achieve better power-saving performance and convergence with a noteworthy reduction in the computation time compared to the alternating optimization-based approaches. In addition, our results show that the total transmit power at the BS decreases with the increasing number of reflecting units at the IRSs.