{
  "abstract": "Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI methods only provide discrete device labels and no human-interpretable representation of the relations among received signals. We propose supervised device charting, which maps location-insensitive channel-state information (CSI) fingerprints to a low-dimensional chart that visualizes cluster compactness, overlap, and outliers. We evaluate the method with real-world 5G New Radio (5G NR) measurements from six commercial smartphones and introduce the neighbor label error rate (NLER) to quantify class-separation accuracy. Our results demonstrate that two- and three-dimensional device charts provide an interpretable visualization of the learned RFFI representation. For three-dimensional device charts, the NLER is 0.22% for same-day measurements and 7.38% for measurements from the next day. The device charts reveal a cross-day distribution shift and map the held-out device close to the known device of the same model. Increasing the device chart dimensions further improves cluster separation at the expense of interpretability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24213",
  "author_total": 4,
  "authors": [
    {
      "name": "Mischa Vasylyev"
    },
    {
      "name": "Frederik Zumegen"
    },
    {
      "name": "Reinhard Wiesmayr"
    },
    {
      "name": "Christoph Studer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3n0xd0c9z6q04.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24213",
  "fulltext_url": null,
  "id": "3n0xd0c9z6q04",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24213",
  "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",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "1702|Wireless Signal Modulation Classification"
    ]
  ],
  "openalex": "W7214026950",
  "pdf_url": "https://arxiv.org/pdf/2609.24213",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3n0xd0c9z6q04.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:27Z",
      "native_id": "2609.24213",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24213"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments",
  "title_text": "Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:27Z",
  "url": "https://research.draconitic.com/paper/3n0xd0c9z6q04",
  "venue": "arXiv",
  "versions": []
}