{
  "abstract": "Hybrid active-passive extremely large-scale reconfigurable intelligent surface (XL-RIS) architectures mitigate the double-hop path loss of passive RIS while enabling near-field spatial energy focusing, but impose a computationally intensive mixed discrete-continuous beamforming design problem under strict radio-frequency (RF) power constraints. This paper investigates a hybrid XL-RIS-assisted downlink multi-user multiple-input single-output (MU-MISO) system in the near-field regime. The joint optimization of the base station (BS) precoder, discrete XL-RIS phase shifts, and active-element amplification gains is addressed via a discrete coordinate ascent (DCA)-based alternating optimization (AO) framework, in which discrete variables are updated through local index-domain searches and the BS precoder is derived in closed form via a noise-aware minimum mean-square error (MMSE) criterion. To reduce the per-sample computational cost for XL-RIS apertures, a deep unfolding (DU) framework is proposed as a low-complexity alternative, unrolling a fixed number of Riemannian gradient ascent steps on the sum-rate objective and recovering the discrete phase configuration via quantized phasor projection, with one learnable step size per layer trained in a self-supervised manner. Numerical results under near-field spherical-wave channel models confirm that the DU framework achieves sum-rate performance close to the AO scheme with substantially lower and nearly constant runtime, and that both schemes consistently outperform manifold optimization under discrete hardware constraints.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.10569",
  "author_total": 2,
  "authors": [
    {
      "name": "Awuni Mohammed Abdul-Razak"
    },
    {
      "name": "Kyungchun Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5t0w4shqj2w2f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.10569",
  "fulltext_url": null,
  "id": "5t0w4shqj2w2f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.10569",
  "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/2611.10569",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5t0w4shqj2w2f.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:33Z",
      "native_id": "2611.10569",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.10569"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Learning-Assisted Beamforming Optimization for Hybrid XL-RIS-Aided Communication",
  "title_text": "Learning-Assisted Beamforming Optimization for Hybrid XL-RIS-Aided Communication",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:33Z",
  "url": "https://research.draconitic.com/paper/5t0w4shqj2w2f",
  "venue": "arXiv",
  "versions": []
}