{
  "abstract": "Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation. It is particularly challenging when the desired signal is strong relative to the residual self-interference. In this regime, conventional iterative digital cancellers update their coefficients from desired-signal-contaminated received samples and may suffer substantial performance degradation. This paper reformulates digital-domain self-interference cancellation as an operator-learning problem. The proposed framework learns the temporal evolution of the residual self-interference channel from historical observations and uses the learned operator together with the known local transmit signal to reconstruct the current residual self-interference. A Transformer neural operator is used to realize this framework. The resulting method is trained in a desired-signal-free setting and then applied in the presence of a desired signal. A continuity-based analysis characterizes the sensitivity of the learned operator to the resulting input perturbation. Numerical experiments in static and time-varying channels, including time-varying SIR conditions, demonstrate stable cancellation performance over a wide range of interference levels. At 30 dB SIR, the proposed method improves the cancellation performance by 5--15 dB over the considered conventional methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19490",
  "author_total": 3,
  "authors": [
    {
      "name": "Zhijie Su"
    },
    {
      "name": "Shiquan Zhang"
    },
    {
      "name": "Qiaolin He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ecfhgsa5pbyh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19490",
  "fulltext_url": null,
  "id": "8ecfhgsa5pbyh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19490",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Full-Duplex Wireless Communications"
    ]
  ],
  "openalex": "W7222199858",
  "pdf_url": "https://arxiv.org/pdf/2610.19490",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ecfhgsa5pbyh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:35Z",
      "native_id": "2610.19490",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19490"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators",
  "title_text": "Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:35Z",
  "url": "https://research.draconitic.com/paper/8ecfhgsa5pbyh",
  "venue": "arXiv",
  "versions": []
}