{
  "abstract": "Millimeter-wave (mmWave) communication relies on directional beam sweeping for initial access, which incurs substantial latency and signaling overhead as antenna arrays grow large and deployments become denser. Machine-learning-based beam prediction can reduce this overhead, but its effectiveness depends on training data that accurately reflects the deployment environment, a requirement that is difficult to meet in practice. This paper presents a latency-aware beam management architecture that integrates digital-twin-assisted prediction, regionbased localization, weighted ensemble learning, and explainability-driven beam pruning into a unified framework. A digital twin (DT) generates site-specific ray-traced channel knowledge offline, enabling environment-aware beam prediction without exhaustive real-world measurements and supporting both initial access and beam tracking. The proposed ensemble learning scheme combines a base classifier and a specialist classifier through reliability-weighted fusion to improve prediction accuracy and robustness, while explainable artificial intelligence (XAI)-based attribution pruning reduces the beam codebook to a small subset of the most informative beams for beam allocation. To capture the distributional mismatch between the DT and real-world environments: the network was trained on imperfect digital-twin data but evaluated on real-world surrobase data. Results show that the proposed method achieves 96.80% top-1 and 98.36% top-3 beam prediction accuracy, while reducing over-the-air sounding overhead by nearly an order of magnitude relative to exhaustive sweeping, outperforming convolutional and deep neural network baselines from prior work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01107",
  "author_total": 2,
  "authors": [
    {
      "name": "Muhammad Kamran Saeed"
    },
    {
      "name": "Ashfaq Khokhar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3v88dwxs6cbnq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01107",
  "fulltext_url": null,
  "id": "3v88dwxs6cbnq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01107",
  "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.LG"
    ],
    [
      "arxiv",
      "cs.NI"
    ],
    [
      "arxiv",
      "math.IT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01107",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3v88dwxs6cbnq.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:10Z",
      "native_id": "2611.01107",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01107"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Digital Twin-Assisted Fast Beam Alignment for mmWave MIMO Networks",
  "title_text": "Digital Twin-Assisted Fast Beam Alignment for mmWave MIMO Networks",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:22Z",
  "url": "https://research.draconitic.com/paper/3v88dwxs6cbnq",
  "venue": "arXiv",
  "versions": []
}