{
  "abstract": "In this paper, we study a significance-driven cross- layer semantic communication design problem. Based on sta- tistical decision theory, we introduce an information-theoretic measure of per-sample data significance that quantifies the task-specific value of each individual observation. Using this metric, we formulate a cross-layer optimization problem that simultaneously optimizes (i) physical-layer semantic encoding and inference and (ii) MAC-layer resource allocation, with the objective of maximizing semantic spectrum efficiency, defined as the semantic value delivered per unit bandwidth per unit time. At the physical layer, we develop Meta-Learning Variational Information Bottleneck (Meta-VIB), a new semantic transceiver that employs a meta-learned hypernetwork to compress high- dimensional observations into semantically significant latents, enabling instantaneous adaptation to dynamic channel conditions and varying symbol budgets without online retraining. At the MAC layer, we model channel allocation as a Multi-Action Restless Multi-Armed Bandit (MA-RMAB) and adopt the Q- Maximization algorithm, which dynamically allocates channel resources to sensors based on their semantic value of information. Experimental results on a real-world pedestrian safety dataset demonstrate that our joint design achieves substantial gains in semantic spectrum efficiency over baselines, reaching up to 1000 times gain at an average SNR of 0 dB and 40 times gain at an average SNR of 5 dB.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.28441",
  "author_total": 4,
  "authors": [
    {
      "name": "Christian McDowell"
    },
    {
      "name": "Andrea Panebianco"
    },
    {
      "name": "Sirin Chakraborty"
    },
    {
      "name": "Yin Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/88rcsvdcyshn5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.28441",
  "fulltext_url": null,
  "id": "88rcsvdcyshn5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.28441",
  "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 Wireless Communication Technologies"
    ]
  ],
  "openalex": "W7204972599",
  "pdf_url": "https://arxiv.org/pdf/2608.28441",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/88rcsvdcyshn5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:04Z",
      "native_id": "2608.28441",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.28441"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Significance-Driven Semantic Communication",
  "title_text": "Significance-Driven Semantic Communication",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:04Z",
  "url": "https://research.draconitic.com/paper/88rcsvdcyshn5",
  "venue": "arXiv",
  "versions": []
}