福建电脑2025，Vol.41Issue(7)：37-41,5.DOI:10.16707/j.cnki.fjpc.2025.07.008
车联网抗干扰资源分配的DRL方法
DRL-Based Anti-Jamming Resource Allocation for Vehicular Networks
王永龙1
作者信息
- 1. 福州大学电气工程与自动化学院 福州 350108
- 折叠
摘要
Abstract
To enhance the security and efficiency of inter vehicle communication and effectively combat malicious interference attacks,this paper proposes a multi-agent deep reinforcement learning scheme based on adversarial dual deep recursive Q-network.By integrating cognitive radio technology to optimize resource allocation in V2V communication,the transmission success rate of V2V links is maximized in the event of malicious interference,ensuring efficient data transmission even when the network is subjected to malicious interference.The simulation results have verified that the proposed algorithm has good convergence,significantly improving the transmission success rate and anti-interference performance of V2V links.关键词
认知无线电/车联网/抗干扰/资源分配/深度强化学习Key words
Cognitive Radio/Vehicular Networks/Anti-Jamming/Deep Reinforcement Learning分类
信息技术与安全科学引用本文复制引用
王永龙..车联网抗干扰资源分配的DRL方法[J].福建电脑,2025,41(7):37-41,5.
