A Unified Group-Wise SAC Framework for RIS Deployment Optimization in Cell-Free MIMO Networks

Abstract

Reconfigurable intelligent surface (RIS)-aided cellfree multiple-input multiple-output (CF-MIMO) is emerging as a pivotal architecture for 6G networks, promising ubiquitous coverage and high spectral efficiency. However, the large-scale deployment of RISs entails significant challenges in network planning, specifically the joint optimization of sparse RIS deployment, phase configuration, and active beamforming. This constitutes a complex mixed-integer non-linear programming (MINLP) problem. To address this, we propose a unified deep reinforcement learning (DRL) framework, which optimizes discrete RIS selection and continuous beamforming variables in an end-to-end manner. Specifically, we introduce a Top-K relaxation mechanism to handle the binary deployment constraints and group-wise phase control strategy to mitigate the high-dimensional action space in large-scale RISs. Simulation results demonstrate that the proposed framework effectively achieves rapid convergence. Specifically, the proposed joint optimization yields a maximum sum-rate improvement of 40.4% over the network without RISs.