用于高机动性自动驾驶的信道知识赋能有限块长速率分割传输
Channel Knowledge Empowered Finite-Blocklength Rate-Splitting Transmission for High-Mobility Autonomous Driving
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中文总结 AI 辅助
针对自动驾驶系统的xURLLC需求，提出CKM赋能的FBL RSMA。利用CKM信息进行精细速率分割设计，分析最小速率性能，推导私有流遍历速率界并制定优化设计。该方案在高机动性场景中性能优于SDMA和NOMA，凸显准确CKM信息对速率分割的重要性。
中文摘要 AI 辅助
为满足自动驾驶系统对超超低延迟和高可靠性（xURLLC）的要求，多址接入方案须在高机动性和复杂传播环境中可靠运行。近期，速率分割多址接入（RSMA）成为有前景的多用户传输框架。自动驾驶车辆的先进感知、定位和车载计算能力有助于构建信道知识地图（CKM）。本文提出用于下行自动驾驶系统的CKM赋能有限块长（FBL）RSMA。利用CKM提供的位置相关大规模信道信息进行RSMA的精细速率分割设计，明确分析FBL速率分割的最小速率性能以确保用户公平性，推导私有流遍历速率的紧密闭式界，结合闭式公共流表达式制定速率分割比的高效优化设计。数值结果表明，CKM赋能的FBL RSMA优于空分多址接入（SDMA）和非正交多址接入（NOMA），基于数据的CKM能提升性能，RSMA对大规模信道知识误差敏感。
英文摘要
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emerged as a promis-ing multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent.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.
发表机构
- College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）
- School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
- School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
- National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China（电子科技大学通信科学与技术国家重点实验室）
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