{
  "abstract": "Downlink channel state information (CSI) feedback is a key bottleneck in frequency-division duplex (FDD) massive MIMO systems, as the user equipment (UE) must convey its estimated channel to the base station (BS) over a limited uplink (UL) budget. To improve CSI reconstruction accuracy under tight feedback constraints, we propose prior-aided masked vector quantization (PM-VQ), a learning-based separate source--channel coding (SSCC) feedback scheme conditioned on the average angle--delay power map---a compact representation of the channel second-order statistics available at both the UE and the BS. In PM-VQ, a prior-aided encoder maps the CSI to latent tokens, and a spatially-adaptive masking module (SAMM) scores and selects the most informative tokens within the feedback budget. The selected tokens are vector-quantized and fed back together with their positions, while an adaptive de-masking module (ADM) completes the latent representation at the BS before prior-conditioned decoding. To support variable-rate compression, a single model is trained over a range of selected-token counts, enabling operation across multiple feedback dimensions without retraining. We evaluate PM-VQ against three representative baselines on a Sionna-generated 3GPP TR~38.901 UMa dataset, focusing on the most challenging diffuse regime where channel energy is spread across many angle--delay coefficients. Simulation results show that PM-VQ achieves the lowest NMSE across all tested SNR levels and feedback dimensions in this regime. Moreover, the angle--delay power-map prior remains beneficial even when estimated from only a few channel realizations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17769",
  "author_total": 7,
  "authors": [
    {
      "name": "Yi Song"
    },
    {
      "name": "Tianyu Yang"
    },
    {
      "name": "Kangda Zhi"
    },
    {
      "name": "Shuangyang Li"
    },
    {
      "name": "Fangzhou Wu"
    },
    {
      "name": "Songyan Xue"
    },
    {
      "name": "Giuseppe Caire"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cwh1q7837nv97.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17769",
  "fulltext_url": null,
  "id": "cwh1q7837nv97",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17769",
  "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",
      "2208|Advanced MIMO Systems Optimization"
    ]
  ],
  "openalex": "W7213505377",
  "pdf_url": "https://arxiv.org/pdf/2609.17769",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cwh1q7837nv97.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:04Z",
      "native_id": "2609.17769",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17769"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Prior-Aided Masked Vector Quantization CSI Feedback for FDD Massive MIMO Systems",
  "title_text": "Prior-Aided Masked Vector Quantization CSI Feedback for FDD Massive MIMO Systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:04Z",
  "url": "https://research.draconitic.com/paper/cwh1q7837nv97",
  "venue": "arXiv",
  "versions": []
}