This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed multi-agent reinforcement learning (MARL) algorithm, SAMRAMARL enables autonomous decision-making for each vehicle, channel assignment optimization, power allocation, and semantic symbol length based on the contextual importance of the transmitted information. This semantic-awareness ensures that both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications prioritize data that is critical for maintaining safe and efficient platoon operations. The framework also introduces a tailored quality of experience (QoE) metric for semantic communication, aiming to maximize QoE in V2V links while improving the success rate of semantic information transmission (SRS). Extensive simulations has demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE and communication efficiency in C-V2X platooning scenarios.

翻译：本文提出一种基于多智能体强化学习（MARL）的语义感知多模态资源分配方案（SAMRA），称为SAMRAMARL，应用于采用蜂窝车联网（C-V2X）通信的编队行驶系统。该方法利用语义信息优化通信资源分配。通过集成分布式多智能体强化学习算法，SAMRAMARL使每辆车能够根据传输信息的上下文重要性，自主进行决策优化、信道分配、功率配置及语义符号长度调整。这种语义感知机制确保车对车（V2V）与车对基础设施（V2I）通信均优先传输对维持编队安全高效运行至关重要的数据。该框架还针对语义通信设计了定制化的体验质量（QoE）度量指标，旨在最大化V2V链路的QoE，同时提升语义信息传输成功率（SRS）。大量仿真实验表明，SAMRAMARL在C-V2X编队场景中显著优于现有方法，在QoE与通信效率方面取得实质性提升。