{
  "abstract": "Improving the throughput of the weakest users is a central objective of wireless resource allocation, and the objective around which the user-centric cell-free massive multiple-input multiple-output (UC-mMIMO) architecture is built. This paper brings the two together by formulating and solving percentile programs for UC-mMIMO networks. We maximize the sum-least-qth-percentile (SLqP) rate via downlink power control, a utility that targets the user equipments (UEs) in the lowest q percentile of the rate distribution and subsumes max-min-rate and sum-rate maximization as special cases. The problem is non-convex, non-smooth and NP-hard. The cyclic maximization approach of one of the previous percentile optimization series does not carry over: the coherent joint transmission technique introduces cross terms in the powers that destroy the block-concavity on which it relies. We develop a change of variables that restores block-concavity, yielding an algorithm with closed-form auxiliary updates and a concave power update, and we give a self-contained proof of non-decreasing convergence to a directional stationary point. Simulations establish two facts: first, percentile optimization retains its value in an architecture with no cell edges; relative to the weighted minimum mean-square error benchmark, it raises the average rate of the lowest-10th-percentile UEs by 56--72% and Jain's fairness index from below 0.92 to above 0.99. Second, compared directly with massive MIMO and small cells under the same SLqP utility, UC-mMIMO delivers the highest lower-percentile rates at every access point density tested, by up to 62% and 2.7$\\times$ respectively, at the same fairness, and its advantage widens with densification.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33982",
  "author_total": 3,
  "authors": [
    {
      "name": "Ahmad Khan"
    },
    {
      "name": "Hussein A. Ammar"
    },
    {
      "name": "Raviraj Adve"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e7dbr6a0p09p7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33982",
  "fulltext_url": null,
  "id": "e7dbr6a0p09p7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33982",
  "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"
    ],
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      "arxiv",
      "cs.NI"
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      "arxiv",
      "cs.SY"
    ],
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      "arxiv",
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      "arxiv",
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      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Advanced MIMO Systems Optimization"
    ]
  ],
  "openalex": "W7223925280",
  "pdf_url": "https://arxiv.org/pdf/2610.33982",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e7dbr6a0p09p7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:40Z",
      "native_id": "2610.33982",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33982"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Unifying Performance and Equity via Percentile Optimization and Cell-Free Massive MIMO",
  "title_text": "Unifying Performance and Equity via Percentile Optimization and Cell-Free Massive MIMO",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:40Z",
  "url": "https://research.draconitic.com/paper/e7dbr6a0p09p7",
  "venue": "arXiv",
  "versions": []
}