{
  "abstract": "Adaptive modulation and coding (AMC) improves the spectral efficiency of underwater acoustic (UWA) links, but its feedback is inevitably stale: sound propagates slowly, and by the time channel state information (CSI) arrives, the fast time-varying multipath channel has moved on. Predictive link adaptation therefore needs more than a point forecast: it needs a distribution over each candidate action's consequences. We propose PRB-WM, a physics-regularized Beta world model for predictive per-subcarrier link adaptation on real time-varying UWA channels. A persistence-anchored stochastic latent-space dynamics model learns how the channel evolves; a counterfactual outcome head evaluates, for every candidate modulation and coding scheme (MCS), a Beta-binomial distribution of bit errors on shared imagined futures; and a chance-constrained receding-horizon model predictive controller maximizes expected spectral efficiency subject to a bounded probability of violating the bit error rate (BER) target. To the best of our knowledge, PRB-WM is the first world model for UWA communications. In closed-loop replay of six measured sea-trial links from the public UWA Channel Library against a 24-method pool spanning the full methodological spectrum, PRB-WM attains a mean goodput 87% above the strongest baseline, ranks first on all six time-varying links at the longest lookahead, and is the only method whose realized frame risk decreases systematically with lookahead, meeting the BER-violation contract where no baseline does. Ablations confirm that rollout sampling dispersion is essential to the chance constraint and that the physics anchor is necessary for trainability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16628",
  "author_total": 8,
  "authors": [
    {
      "name": "Shengjie Zhong"
    },
    {
      "name": "Zhongliang Zhao"
    },
    {
      "name": "Jingxuan Chen"
    },
    {
      "name": "Dianrun Huang"
    },
    {
      "name": "Zichen Zhao"
    },
    {
      "name": "Yang Liu"
    },
    {
      "name": "Dapeng O. Wu"
    },
    {
      "name": "Tony Q. S. Quek"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/35t4fjpnngx65.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16628",
  "fulltext_url": null,
  "id": "35t4fjpnngx65",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16628",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.SD"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2212|Underwater Vehicles and Communication Systems"
    ]
  ],
  "openalex": "W7221760714",
  "pdf_url": "https://arxiv.org/pdf/2610.16628",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/35t4fjpnngx65.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.16628",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16628"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels",
  "title_text": "PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/35t4fjpnngx65",
  "venue": "arXiv",
  "versions": []
}