{
  "abstract": "To meet the extended ultra-low latency and high reliability (xURLLC) requirements for autonomous driving sys-tems, multiple access schemes must operate reliably in high-mobility and complex propagation environments. Recently, rate-splitting multiple access (RSMA) has emerged as a promis-ing multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent. Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles facilitate the construction of a channel knowledge map (CKM), which is a key enabler for environment-aware communications in future 6G networks. In this work, we propose a CKM-aware finite-blocklength (FBL) RSMA for a downlink autonomous driving system. The location-dependent large-scale channel information provided by CKM is exploited to guide the common/private power split and common-rate allocation in RSMA. Specifically, we derive a new and tight closed-form bound for the private-stream ergodic rate. Combined with the closed-form expression for the common-stream ergodic rate, an optimization design of splitting ratios is formulated to maximize the min-rate performance among multiple users. Numerical results show that the proposed scheme outperforms the considered space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) schemes in high-mobility scenarios. Comparisons across different CKMs further highlight the benefit of accurate channel information, demonstrating how environment-aware channel knowledge complements flexible in-terference management of RSMA in high-mobility short-packet communications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.10138",
  "author_total": 8,
  "authors": [
    {
      "name": "Yingyang Chen"
    },
    {
      "name": "Yi Wang"
    },
    {
      "name": "Yijie Mao"
    },
    {
      "name": "Feng Bai"
    },
    {
      "name": "Xia Wu"
    },
    {
      "name": "Feiyan Min"
    },
    {
      "name": "Li Wang"
    },
    {
      "name": "Gang Feng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c4zcw78s43n8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.10138",
  "fulltext_url": null,
  "id": "1c4zcw78s43n8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.10138",
  "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",
      "2208|Advanced Wireless Communication Technologies"
    ]
  ],
  "openalex": "W7168241271",
  "pdf_url": "https://arxiv.org/pdf/2607.10138",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1c4zcw78s43n8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:35Z",
      "native_id": "2607.10138",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.10138"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Channel Knowledge Empowered Finite-Blocklength Rate-Splitting Transmission for High-Mobility Autonomous Driving",
  "title_text": "Channel Knowledge Empowered Finite-Blocklength Rate-Splitting Transmission for High-Mobility Autonomous Driving",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:35Z",
  "url": "https://research.draconitic.com/paper/1c4zcw78s43n8",
  "venue": "arXiv",
  "versions": []
}