Optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) is challenging due to their non-convex and non-linear nature. In this letter, we propose a heuristic-integrated deep reinforcement learning (DRL) framework that (1) leverages accumulated actions over multiple steps in the double deep Q-network (DDQN) for RIS column-wise control and (2) integrates a greedy algorithm (GA) into each DRL step to refine the state via fine-grained, element-wise optimization of RIS configurations. By learning from GA-included states, the proposed approach effectively addresses RIS optimization within a small DRL action space, demonstrating its capability to optimize phase-shift configurations of large-scale RISs.
@article{arxiv.2505.04401,
title = {A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs},
author = {Wei Wang and Peizheng Li and Angela Doufexi and Mark A. Beach},
journal= {arXiv preprint arXiv:2505.04401},
year = {2025}
}
Comments
5 pages, 5 figures. This work has been accepted for publication in IEEE Communications Letters