Integrating Graph Neural Networks with Multi-Agent Deep Reinforcement Learning for Dynamic V2X Communication

In the rapidly evolving field of Internet of Vehicles technology, Cellular Vehicle-to-Everything (C-V2X) communication has garnered considerable attention due to its superior performance in terms of coverage, latency, and throughput. Efficient resource allocation in C-V2X networks is crucial for ensuring the transmission of safety-critical information in Vehicle-to-Vehicle (V2V) communication. In this paper, we propose Dynamic-aware Graph-based Proximal Policy Optimization (DGPPO), a novel approach that integrates Graph Neural Networks (GNNs) with Deep Reinforcement Learning to effectively tackle the challenges of joint spectrum and power allocation in C-V2X networks. The proposed approach constructs a dynamic graph representation where communication links are modeled as nodes, allowing for flexible modeling of network topology changes. Next, we utilize an adaptive GNN to extract essential low-dimensional global features from the dynamically evolving graph network topology, which is constructed based on local observations. Finally, we propose a multi-agent proximal policy optimization (MAPPO) approach that leverages these extracted features to promote cooperative behavior and adaptability among agents. The simulation results validate the effectiveness of the DGPPO algorithm, outperforming the state-of-the-art algorithms in highly dynamic vehicular network environments.