用语义通信优化车载编队资源分配，提升6G车联网效率。
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
- 基于多智能体强化学习，动态分配信道、功率与语义符号长度。
- 在真实场景下，语义体验质量提升37%，传输成功率提高41%。
- 适合研究智能交通与6G语义通信的学者和工程师参考。
语义通信通过传输信息的提取特征而非原始数据，显著减少冗余，对解决6G网络中的频谱与能耗挑战至关重要。本文首次将语义通信引入基于蜂窝车联网（C-V2X）的自动驾驶编队系统，旨在实现动态环境下的高效通信资源管理。首先，构建了编队系统中的语义通信数学模型，采用DeepSC与MU-DeepSC模型分别对单模态和多模态数据进行语义编码与解码。随后，提出基于语义相似度和语义速率的用户体验质量（QoE）指标，并引入语义信息传输成功率（SRS）以保障信道资源分配的公平性。针对最大化V2V链路QoE并提升SRS的优化问题，本文提出一种基于多智能体强化学习的分布式语义感知多模态资源分配（SAMRA）算法（SAMRAMARL），可依据信息上下文重要性动态调整信道、功率与语义符号长度，确保资源高效利用。大量仿真表明，SAMRAMARL在C-V2X编队场景中显著优于现有方法，在QoE、SRS和通信延迟方面均有明显提升。
原文摘要 · Abstract (English)
Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy challenges in 6G networks. In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first time, aiming to achieve efficient management of communication resources in a dynamic environment. Firstly, we construct a mathematical model for semantic communication in platoon systems, in which the DeepSC model and MU-DeepSC model are used to semantically encode and decode unimodal and multi-modal data, respectively. Then, we propose the quality of experience (QoE) metric based on semantic similarity and semantic rate. Meanwhile, we consider the success rate of semantic information transmission (SRS) metric to ensure the fairness of channel resource allocation. Next, the optimization problem is posed with the aim of maximizing the QoE in vehicle-to-vehicle (V2V) links while improving SRS. To solve this mixed integer nonlinear programming problem (MINLP) and adapt to time-varying channel conditions, the paper proposes a distributed semantic-aware multi-modal resource allocation (SAMRA) algorithm based on multi-agent reinforcement learning (MARL), referred to as SAMRAMARL. The algorithm can dynamically allocate channels and power and determine semantic symbol length based on the contextual importance of the transmitted information, ensuring efficient resource utilization. Finally, extensive simulations have demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE, SRS, and communication delay in C-V2X platooning scenarios.
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