A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs
This work addresses phase optimization for large-scale RISs, which is an incremental improvement in wireless communication systems.
The paper tackled the challenge of optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) by proposing a heuristic-integrated deep reinforcement learning (DRL) framework, which combined a double deep Q-network with a greedy algorithm to refine RIS configurations, but no concrete performance numbers were provided in the 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.