  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.
