{
  "abstract": "This letter investigates age-of-information (AoI) minimization in multi-user wireless networks operating in the finite-blocklength (FBL) regime, which is critical for low-latency transmission of short state-update packets. While rate-splitting multiple access (RSMA) provides a powerful and flexible framework for interference management in multi-user FBL systems, the joint optimization of its parameters, such as precoding vectors, power allocation, and rate-splitting ratios, to guarantee information freshness results in analytically intractable complexity. To address this challenge, we propose an actor--critic deep reinforcement learning (DRL) framework to learn dynamic resource-allocation policies in multi-user multiple-input single-output (MU-MISO) broadcast channels. Simulation results show that the proposed RSMA-RL framework achieves consistently lower AoI than the state-of-the-art benchmarks, with substantial gains observed at low signal-to-noise ratio (SNR) and short blocklengths, while matching benchmark performance at high SNR with significantly lower online complexity via a single neural-network forward pass at execution.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11396",
  "author_total": 4,
  "authors": [
    {
      "name": "Enes Kaya"
    },
    {
      "name": "Mehmet Alp Demircioğlu"
    },
    {
      "name": "Elif Tugce Ceran"
    },
    {
      "name": "Melda Yuksel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3m517fgk7g163.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11396",
  "fulltext_url": null,
  "id": "3m517fgk7g163",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11396",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2610.11396",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3m517fgk7g163.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:58Z",
      "native_id": "2610.11396",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11396"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "DRL-Based AoI Minimization for RSMA in Finite-Blocklength MU-MISO",
  "title_text": "DRL-Based AoI Minimization for RSMA in Finite-Blocklength MU-MISO",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:58Z",
  "url": "https://research.draconitic.com/paper/3m517fgk7g163",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1109/lcomm.2026.3726818",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}