Joint User Scheduling and Precoding for RIS-Aided MU-MISO Systems: A MADRL Approach

TL;DR

This paper introduces a multi-agent deep reinforcement learning framework for joint user scheduling and precoding in RIS-aided MU-MISO systems, significantly reducing computational complexity while maintaining near-optimal performance.

Contribution

It presents a novel MADRL-based scalable approach for joint optimization in RIS systems, outperforming existing algorithms in efficiency and effectiveness.

Findings

Reduces computational complexity by three orders of magnitude.

Achieves 6% performance improvement over existing MADRL algorithms.

Maintains 97% of the optimal ergodic sum rate with much lower complexity.

Abstract

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability of controlling wireless environment. However, there is still a lack of treatments to deal with the growth of the number of users and RIS elements, which may incur performance degradation or computational complexity explosion. In this paper, we investigate the joint optimization of user scheduling and precoding for distributed RIS-aided communication systems. Firstly, we propose an optimization-based numerical method to obtain suboptimal solutions with the aid of the approximation of ergodic sum rate. Secondly, to reduce the computational complexity caused by the high dimensionality, we propose a data-driven scalable and generalizable multi-agent deep reinforcement learning (MADRL) framework with the aim to maximize the ergodic sum rate approximation through the cooperation of all agents. Further, we propose a novel dynamic working process exploiting the trained MADRL algorithm, which enables distributed RISs to configure their own passive precoding independently. Simulation results show that our algorithm substantially reduces the computational complexity by a time reduction of three orders of magnitude at the cost of 3% performance degradation, compared with the optimization-based method, and achieves 6% performance improvement over the state-of-the-art MADRL algorithms.