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arXiv · 2610.39827

Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems

Oumaïma Bounhar·Thomas Chêne·Ghaya Rekaya-Ben Othman·Oussama Damen
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Abstract

Reconfigurable Intelligent Surfaces (RIS) are a promising solution for improving millimeter-wave (mmWave) communications by controlling signal propagation. Yet, selecting the optimal RIS configuration remains challenging due to the high-dimensional search space and signaling overhead. We propose a Deep Reinforcement Learning (DRL) framework for blind passive beamforming, where a Deep Q-Learning (DQL) agent learns an adaptive policy to select near-optimal RIS configurations while sounding only few codewords, without channel state information. Results show that our approach outperforms Q-Learning (QL) and conventional baselines in convergence, performance, and overhead reduction.

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Oumaïma Bounhar, Thomas Chêne, Ghaya Rekaya-Ben Othman, Oussama Damen. 2026-10-29. Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems. https://arxiv.org/abs/2610.39827

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