{
  "abstract": "This paper focuses on the joint robust beamforming and resource allocation for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise adjustment of beamforming and resource allocation, posing a major challenge to meeting heterogeneous users' strict quality of service (QoS) requirements. To address this challenge, we first formulate a chance-constrained optimization problem to minimize the total transmit power while satisfying QoS requirements under a predefined outage probability. By leveraging semidefinite relaxation (SDR), the objective function is transformed into a linear function of the traces of the beamforming matrices. Then, we propose a deep neural network (DNN)-driven channel uncertainty learning to dynamically learn and model the uncertain CSI as an asymmetric uncertainty set. Under the constructed CSI uncertainty set, a robust counterpart method based on pre-trained network parameters is developed. It characterizes the uncertainty set as a finite union of convex sets, thereby providing a tractable approximation for the original chance constraints. Finally, we design an adaptive iterative algorithm to jointly optimize the resource allocation and beamforming vectors. Simulation results show that our proposed DNN-driven method outperforms traditional robust and non-robust methods, achieving a superior energy efficiency and robust reliability in SAGIN.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34728",
  "author_total": 6,
  "authors": [
    {
      "name": "Haonan Zhang"
    },
    {
      "name": "Weihua Wu"
    },
    {
      "name": "Qi Zhang"
    },
    {
      "name": "Runzi Liu"
    },
    {
      "name": "Zewei Jing"
    },
    {
      "name": "Weijia Han"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/79bra3kx3kaj8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34728",
  "fulltext_url": null,
  "id": "79bra3kx3kaj8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34728",
  "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",
      "2202|Satellite Communication Systems"
    ]
  ],
  "openalex": "W7214774888",
  "pdf_url": "https://arxiv.org/pdf/2609.34728",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/79bra3kx3kaj8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:17Z",
      "native_id": "2609.34728",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34728"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Robust Resource Management for SAGIN using DNN-Driven Channel Uncertainty Learning",
  "title_text": "Robust Resource Management for SAGIN using DNN-Driven Channel Uncertainty Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/79bra3kx3kaj8",
  "venue": "arXiv",
  "versions": []
}