{
  "abstract": "In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed and partially informed operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, user equipment (UE) and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex- concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0:95.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.27778",
  "author_total": 5,
  "authors": [
    {
      "name": "Zinat Behdad"
    },
    {
      "name": "Özlem Tuğfe Demir"
    },
    {
      "name": "Ki Won Sung"
    },
    {
      "name": "Pei Xiao"
    },
    {
      "name": "Cicek Cavdar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6vpmytp6dxn1b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.27778",
  "fulltext_url": null,
  "id": "6vpmytp6dxn1b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.27778",
  "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|Advanced MIMO Systems Optimization"
    ]
  ],
  "openalex": "W7172007360",
  "pdf_url": "https://arxiv.org/pdf/2607.27778",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6vpmytp6dxn1b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:22Z",
      "native_id": "2607.27778",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.27778"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation",
  "title_text": "Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:22Z",
  "url": "https://research.draconitic.com/paper/6vpmytp6dxn1b",
  "venue": "arXiv",
  "versions": []
}