{
  "abstract": "Site-specific training can improve wireless receiver performance without increasing computational complexity. However, real-world results have so far focused on fully trainable neural receivers and single-layer transmissions. We study site-specific finetuning of three receiver architectures: fully trainable neural, model-driven neural, and model-based. We train and evaluate these receivers using new measurements from a standard-compliant 5G NR testbed at ETH Zurich with dual-layer uplink transmission, including measurement campaigns conducted more than six months apart. Our results show that site-specific finetuning (i) substantially improves fully trainable and model-driven neural receivers, while resulting in only marginal gains for the less tunable model-based receiver; (ii) enables a single neural receiver jointly finetuned for single- and dual-layer transmission to closely match receivers finetuned separately for each configuration; and (iii) remains effective across measurement campaigns separated by more than six months. We also investigate site-specific linear minimum mean-square error channel estimation using covariance matrices estimated from either synthetic channels or site-specific measurements. When combined with iterative detection and decoding, site-specific channel estimation achieves the lowest error rate observed in our datasets. Our finetuning code and measurement datasets are publicly available at https://github.com/IIP-Group/site_specific_training",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04004",
  "author_total": 4,
  "authors": [
    {
      "name": "Reinhard Wiesmayr"
    },
    {
      "name": "Nuri Berke Baytekin"
    },
    {
      "name": "Chris Dick"
    },
    {
      "name": "Christoph Studer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/28k6a19j8jsa4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04004",
  "fulltext_url": null,
  "id": "28k6a19j8jsa4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04004",
  "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",
      "2208|Advanced Wireless Communication Techniques"
    ]
  ],
  "openalex": "W7208720072",
  "pdf_url": "https://arxiv.org/pdf/2609.04004",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/28k6a19j8jsa4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:36Z",
      "native_id": "2609.04004",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04004"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "On the Impact of Site-Specific Training for a Real-World 5G NR System",
  "title_text": "On the Impact of Site-Specific Training for a Real-World 5G NR System",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:36Z",
  "url": "https://research.draconitic.com/paper/28k6a19j8jsa4",
  "venue": "arXiv",
  "versions": []
}