{
  "abstract": "Distributed detection plays a fundamental role in communication-efficient collaborative inference over large-scale networks, where spatially distributed nodes must cooperate using severely quantized messages due to bandwidth constraints. While low-bit quantization is essential for reducing communication overhead, it can significantly degrade detection performance, particularly when prior knowledge of observation noise is unavailable. To address this challenge, we propose a model-driven deep learning framework for distributed detection with low-bit quantization. For fixed local quantizers, we first derive the exact finite-sensor maximum a posteriori (MAP) detection error and prove that, under conditionally independent and identically distributed (i.i.d.) observations, independent local randomizations, and a common quantization-level constraint, identical quantizers preserve the supremum of the average network Chernoff information. Guided by these results, we develop a binary probabilistic quantizer with a trainable neural probability controller and design a tree-structured probabilistic quantizer for scalable multi-bit quantization. The exact finite-sensor MAP error provides the training objective for the binary quantizer, while we derive a computationally tractable Bhattacharyya surrogate objective for training the multi-bit quantizer. For both cases, we derive analytical MAP fusion rules based on the class-conditional probability mass functions (PMFs) of the quantized messages. When the observation statistics are unknown, these PMFs are estimated from held-out labeled data, yielding plug-in MAP detectors. Simulation results show that the proposed method approaches centralized-oracle performance at higher quantization resolutions and remains effective under changes in network size and test-time signal amplitude.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21759",
  "author_total": 7,
  "authors": [
    {
      "name": "Wei Guo"
    },
    {
      "name": "Meng He"
    },
    {
      "name": "Chuan Huang"
    },
    {
      "name": "Hengtao He"
    },
    {
      "name": "Shenghui Song"
    },
    {
      "name": "Jun Zhang"
    },
    {
      "name": "Khaled B. Letaief"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bna20v16sp4g4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21759",
  "fulltext_url": null,
  "id": "bna20v16sp4g4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21759",
  "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",
      "math.IT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.21759",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bna20v16sp4g4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:54Z",
      "native_id": "2610.21759",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21759"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization",
  "title_text": "Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:54Z",
  "url": "https://research.draconitic.com/paper/bna20v16sp4g4",
  "venue": "arXiv",
  "versions": []
}