{
  "abstract": "Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and synchronized multimodal data. These limitations are more pronounced in mobile and aerial scenarios with time-varying environments and channels. To address these limitations, we propose DeepTelecom, a material-aware, high-fidelity multimodal channel dataset and reproducible generation framework for networks involving unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces. DeepTelecom refines coarse indoor/outdoor reconstructions and uses a large language model to map segmented surfaces to frequency-dependent electromagnetic parameters, yielding material-aware Level of Detail 3 digital twins. Then, a differentiable GPU-accelerated Sionna ray tracer simulates propagation across the sub-6 GHz/millimeter-wave/sub-terahertz bands, and specifically captures rotor-induced micro-Doppler signatures of stationary or moving UAVs at different pitch angles and translational velocities. The framework synchronously produces channel tensors, coverage maps, transmission videos, multi-view images, and weather-conditioned renderings, and supports differentiable material calibration from sparse channel anchors. The released dataset contains 10 material-aware indoor/outdoor scenes and 50,000 synchronized UAV samples; the framework supports scalable generation of additional data for user-defined scenes and system configurations. In 3D UAV localization, DeepTelecom reduces the 90th-percentile error from 31.05 m to 2.43 m compared with schemes considering only channel state information, demonstrating its significance for artificial-intelligence-enabled wireless systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14545",
  "author_total": 15,
  "authors": [
    {
      "name": "Bohao Wang"
    },
    {
      "name": "Tianen Yuan"
    },
    {
      "name": "William Yen"
    },
    {
      "name": "Zehua Jiang"
    },
    {
      "name": "Chongwen Huang"
    },
    {
      "name": "Li Wei"
    },
    {
      "name": "Chen Zhu"
    },
    {
      "name": "Jiguang He"
    },
    {
      "name": "Hang Zou"
    },
    {
      "name": "Lina Bariah"
    },
    {
      "name": "Brahim Mefgouda"
    },
    {
      "name": "Anis Bara"
    },
    {
      "name": "Yongliang Shen"
    },
    {
      "name": "Zhaoyang Zhang"
    },
    {
      "name": "Merouane Debbah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1d4p0ch0dbav0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14545",
  "fulltext_url": null,
  "id": "1d4p0ch0dbav0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14545",
  "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",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Millimeter-Wave Propagation and Modeling"
    ]
  ],
  "openalex": "W7221756877",
  "pdf_url": "https://arxiv.org/pdf/2610.14545",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1d4p0ch0dbav0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.14545",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14545"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments",
  "title_text": "DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/1d4p0ch0dbav0",
  "venue": "arXiv",
  "versions": []
}