{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.39827",
  "author_total": 4,
  "authors": [
    {
      "name": "Oumaïma Bounhar"
    },
    {
      "name": "Thomas Chêne"
    },
    {
      "name": "Ghaya Rekaya-Ben Othman"
    },
    {
      "name": "Oussama Damen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0yjxq70myjbax.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.39827",
  "fulltext_url": null,
  "id": "0yjxq70myjbax",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.39827",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.39827",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0yjxq70myjbax.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:32Z",
      "native_id": "2610.39827",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.39827"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems",
  "title_text": "Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/0yjxq70myjbax",
  "venue": "arXiv",
  "versions": []
}