Research on Vehicle Joint Radar Communication Resource Optimization Method Based on GNN-DRL

To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods and pure DRL methods, the proposed framework achieves improvements in performance metrics such as communication success rate, Average Age of Information (AoI), and policy convergence speed, effectively enabling resource management in complex environments. Moreover, the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.