In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

翻译：在车联网技术快速发展的背景下，蜂窝车联网通信因其在覆盖范围、时延和吞吐量方面的优越性能而备受关注。车联网通信中的资源分配对于保障安全信息传输、满足车车间通信对超低时延和高可靠性的严苛要求至关重要。本文提出一种融合图神经网络与深度强化学习的方法以应对这一挑战。该方法以通信链路为节点构建动态图，并采用GraphSAGE模型适应图结构变化，旨在确保车车间通信高成功率的同时，最小化对车路通信链路的干扰，从而保障车车间链路信息的成功传输并维持车路链路的高传输速率。所提方法保留了图神经网络的全局特征学习能力，并支持分布式网络部署，使得车辆能够基于局部观测从图网络中提取包含结构信息的低维特征，并做出独立的资源分配决策。仿真结果表明，图神经网络的引入以适度的计算负载增加为代价，有效提升了智能体的决策质量，展现出相较于其他方法的优越性。本研究不仅为车车间与车路通信提供了理论上的高效资源分配策略，也为实际车联网环境中的资源管理开辟了新的技术路径。