{
  "abstract": "Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \\emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09409",
  "author_total": 5,
  "authors": [
    {
      "name": "Jiaying Li"
    },
    {
      "name": "Haifeng Wen"
    },
    {
      "name": "Changsheng You"
    },
    {
      "name": "Yuanwei Liu"
    },
    {
      "name": "Hong Xing"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2sdd5nsy16xvx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09409",
  "fulltext_url": null,
  "id": "2sdd5nsy16xvx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09409",
  "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.AI"
    ],
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Indoor and Outdoor Localization Technologies"
    ]
  ],
  "openalex": "W7212164810",
  "pdf_url": "https://arxiv.org/pdf/2609.09409",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2sdd5nsy16xvx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:15Z",
      "native_id": "2609.09409",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09409"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC",
  "title_text": "Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:15Z",
  "url": "https://research.draconitic.com/paper/2sdd5nsy16xvx",
  "venue": "arXiv",
  "versions": []
}