Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913020487139328 |
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| author | Tran, Phuong Nam Nguyen, Nhan Thanh Juntti, Markku |
| author_facet | Tran, Phuong Nam Nguyen, Nhan Thanh Juntti, Markku |
| contents | Hybrid reconfigurable intelligent surfaces (HRIS) enhance wireless systems by combining passive reflection with active signal amplification. However, jointly optimizing the transmit beamforming with the HRIS reflection and amplification coefficients to maximize spectral efficiency (SE) is a non-convex problem, and conventional iterative solutions are computationally intensive. To address this, we propose a deep reinforcement learning (DRL) framework that learns a direct mapping from channel state information to the near-optimal transmit beamforming and HRIS configurations. The DRL model is trained offline, after which it can compute the beamforming and HRIS configurations with low complexity and latency. Simulation results demonstrate that our DRL-based method achieves 95% of the SE obtained by the alternating optimization benchmark, while significantly lowering the computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications Tran, Phuong Nam Nguyen, Nhan Thanh Juntti, Markku Signal Processing Hybrid reconfigurable intelligent surfaces (HRIS) enhance wireless systems by combining passive reflection with active signal amplification. However, jointly optimizing the transmit beamforming with the HRIS reflection and amplification coefficients to maximize spectral efficiency (SE) is a non-convex problem, and conventional iterative solutions are computationally intensive. To address this, we propose a deep reinforcement learning (DRL) framework that learns a direct mapping from channel state information to the near-optimal transmit beamforming and HRIS configurations. The DRL model is trained offline, after which it can compute the beamforming and HRIS configurations with low complexity and latency. Simulation results demonstrate that our DRL-based method achieves 95% of the SE obtained by the alternating optimization benchmark, while significantly lowering the computational complexity. |
| title | Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.18453 |