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Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems — arXiv:2610.39827

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39827
License (as archived): CC0-1.0
Archived: 2026-11-01T02:30:58.467Z
SHA-256 of the archived text: 81f79b18bff7e04d90a68bd47fd2629370c3e1655f8a2d0fdc6f533a6d988167
Stable URL of this page: https://metraipolis.com/documento/dc_ba0fc19971cf

How to cite

Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems — arXiv:2610.39827 — arXiv (en), https://arxiv.org/abs/2610.39827 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ba0fc19971cf (Metraipolis archive dc_ba0fc19971cf, sha256 81f79b18bff7e04d90a68bd47fd2629370c3e1655f8a2d0fdc6f533a6d988167)

BibTeX

@misc{dcba0fc19971cf,
  title = {Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems — arXiv:2610.39827},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39827},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ba0fc19971cf; sha256 81f79b18bff7e04d90a68bd47fd2629370c3e1655f8a2d0fdc6f533a6d988167},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39827. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ML, nuovi lavori del giorno).
Categorie arXiv: Information Theory (cs.IT) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2610.39827 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:13:28.612Z dalla pagina https://arxiv.org/list/stat.ML/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).