{
  "abstract": "This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09797",
  "author_total": 8,
  "authors": [
    {
      "name": "Mingcheng Nie"
    },
    {
      "name": "Shuangyang Li"
    },
    {
      "name": "Geng Wang"
    },
    {
      "name": "Peng Cheng"
    },
    {
      "name": "Shenghong Li"
    },
    {
      "name": "Chang Liu"
    },
    {
      "name": "Giuseppe Caire"
    },
    {
      "name": "Yonghui Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/asngxhz9jp58e.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09797",
  "fulltext_url": null,
  "id": "asngxhz9jp58e",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09797",
  "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",
      "math.IT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.09797",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/asngxhz9jp58e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:15Z",
      "native_id": "2609.09797",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09797"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Learning-Aided Short Code Design for ISAC based on MIMO-OFDM",
  "title_text": "Learning-Aided Short Code Design for ISAC based on MIMO-OFDM",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:15Z",
  "url": "https://research.draconitic.com/paper/asngxhz9jp58e",
  "venue": "arXiv",
  "versions": []
}