A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs

• URL: http://arxiv.org/abs/2505.04401v1
• Date: Wed, 07 May 2025 13:34:12 GMT
• Title: A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs
• Authors: Wei Wang, Peizheng Li, Angela Doufexi, Mark A. Beach,
• Abstract summary: optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) is challenging due to their non-linear nature.<n>We propose a deep learning reinforcement (DRL) framework that leverages actions multiple steps in a deep network QGA (DDQ) for RIS column-wise control.<n>The proposed approach effectively addresses RIS optimization within a small DRL action space, demonstrating capability to optimize its large-scale RISs.
• Score: 4.209737625992893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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