电讯技术2026，Vol.66Issue(1)：1-10,10.DOI:10.20079/j.issn.1001-893x.250208001
基于DDQN和GNN的分布式无人机路由资源联合优化
Joint Optimization of Routing and Resource Allocation in Decentralized UAV Networks Based on DDQN and GNN
摘要
Abstract
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5%improvement in throughput,50%increase in connection probability,and 17.6%reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.关键词
分布式无人机网络/资源分配/路由算法/图神经网络/双深度Q网络/深度强化学习Key words
decentralized UAV network/resource allocation/routing algorithm/GNN/DDQN/DRL分类
信息技术与安全科学引用本文复制引用
Nawaf Q.H.Othman,杨清海,蒋昕沛..基于DDQN和GNN的分布式无人机路由资源联合优化[J].电讯技术,2026,66(1):1-10,10.基金项目
国家自然科学基金资助项目(61971327) （61971327）
广州市重点科技研究与开发计划(202206030003) （202206030003）
