{
  "abstract": "Existing airspace capacity metrics mainly quantify occupancy or flow, although the same number of aerial vehicles may result in different motion alternatives. This letter establishes \\emph{mobility information capacity} as an information-theoretic measure for low-altitude wireless networks. It quantifies the maximum information that trajectory observations reveal about intentional maneuver inputs under a given maneuver-resource budget and environmental uncertainty. For a common fixed feedback architecture, we formulate a lifted linear-Gaussian mobility channel and derive its finite-horizon log-determinant capacity. Cost and uncertainty whitening gives the spatiotemporal mobility eigenmodes, whose optimal maneuver-resource allocation follows water-filling. When the number of nondegenerate modes grows linearly with time and their efficiencies become asymptotically symmetric, we arrive at the Shannon-like law $R_M^{\\rm G}=\\frac{B_M}{2}\\log_2(1+\\mathrm{MNR})$, where MNR is the mobility-to-noise ratio. The proposed measure opens a motion-centric capacity perspective for the sky, while remaining a distinguishability baseline rather than a collision- or geometry-constrained airspace capacity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10436",
  "author_total": 5,
  "authors": [
    {
      "name": "Weijie Yuan"
    },
    {
      "name": "Fan Liu"
    },
    {
      "name": "Shuangyang Li"
    },
    {
      "name": "Lin Zhou"
    },
    {
      "name": "Pingzhi Fan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/57132e4azcg1f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10436",
  "fulltext_url": null,
  "id": "57132e4azcg1f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10436",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2202|Air Traffic Management and Optimization"
    ]
  ],
  "openalex": "W7212144814",
  "pdf_url": "https://arxiv.org/pdf/2609.10436",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/57132e4azcg1f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:16Z",
      "native_id": "2609.10436",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10436"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Mobility Information Capacity in the Sky: A Gaussian Channel Perspective",
  "title_text": "Mobility Information Capacity in the Sky: A Gaussian Channel Perspective",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:16Z",
  "url": "https://research.draconitic.com/paper/57132e4azcg1f",
  "venue": "arXiv",
  "versions": []
}