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Open Access

ARTICLE

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

Zeyu Chen1, Jian Sun2,*, Zhengda Huan1, Ziyi Zhang1
1 School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
2 School of Electrical and Electronics Engineering, Shandong University of Technology, Zibo, 255000, China
* Corresponding Author: Jian Sun. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071182

Received 01 August 2025; Accepted 25 September 2025; Published online 03 November 2025

Abstract

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.

Keywords

Graph neural network; joint radar and communication; resource allocation; multi-agent collaboration

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