{
  "abstract": "Fluid antenna systems (FAS) leverage continuous reconfigurable apertures to transcend the spatial limits of fixed-position arrays, yet acquiring full-aperture channel state information (CSI) from sparse observations remains a formidable bottleneck. In this paper, we establish a continuous-field formulation for FAS by recasting channel estimation as a stochastic inverse field-reconstruction problem constrained by the projected Helmholtz model. Through electromagnetic information theory (EIT)-based spectral leakage analysis, we demonstrate that finite-aperture reconstruction induces an intrinsic convolutional relationship in the spectral domain, rendering the estimation task a fundamental frequency-domain inverse deconvolution problem. To solve this, we propose a U-shaped Fourier neural operator (UFNO) specifically tailored for noisy, underdetermined wireless environments. Furthermore, we leverage the Landau--Pollak theorem to derive an aperture-dependent EDoF bound that guides the spatial mode budget, and employ a physics-aware Sobolev loss to promote consistency in both channel values and spatial variations. Unlike conventional learning-based approaches, a single frozen UFNO can be directly evaluated at unseen spatial--frequency resolutions without resolution-specific retraining or explicit output interpolation. Extensive simulations show that our framework significantly outperforms state-of-the-art benchmarks in accuracy while achieving orders-of-magnitude lower inference latency than iterative solvers at high spatial--frequency resolutions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36787",
  "author_total": 5,
  "authors": [
    {
      "name": "Zixin Wang"
    },
    {
      "name": "Lizhong Zheng"
    },
    {
      "name": "Yuanming Shi"
    },
    {
      "name": "Kaibin Huang"
    },
    {
      "name": "Khaled B. Letaief"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3trqmnhhsst7t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36787",
  "fulltext_url": null,
  "id": "3trqmnhhsst7t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36787",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7224090494",
  "pdf_url": "https://arxiv.org/pdf/2610.36787",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3trqmnhhsst7t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:53Z",
      "native_id": "2610.36787",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36787"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3109|Model Reduction and Neural Networks",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Rethinking Channel Estimation for Fluid Antenna Systems: A Physics-Informed Operator Learning",
  "title_text": "Rethinking Channel Estimation for Fluid Antenna Systems: A Physics-Informed Operator Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:53Z",
  "url": "https://research.draconitic.com/paper/3trqmnhhsst7t",
  "venue": "arXiv",
  "versions": []
}