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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.04401 |
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| _version_ | 1866915276246745088 |
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| author | Wang, Wei Li, Peizheng Doufexi, Angela Beach, Mark A. |
| author_facet | Wang, Wei Li, Peizheng Doufexi, Angela Beach, Mark A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs Wang, Wei Li, Peizheng Doufexi, Angela Beach, Mark A. Signal Processing Machine Learning 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. |
| title | A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2505.04401 |