基于DDQN和GNN的分布式无人机路由资源联合优化OA
Joint Optimization of Routing and Resource Allocation in Decentralized UAV Networks Based on DDQN and GNN
由于干扰及网络拓扑的快速变化,在去中心化的无人机网络中实现路由与资源分配的优化仍面临诸多挑战.提出了一种新颖的框架,融合了双深度Q网络与图神经网络,以实现路由和无线资源分配的联合优化.该框架采用图神经网络对网络拓扑进行建模,并利用双深度Q网络自适应调控路由和资源分配,从而有效解决干扰问题并提高系统性能.仿真结果表明,与传统方法(如目的地最近法、最大信噪比法及基于多层感知器的模型)相比,所提方法吞吐量提升约 23.5%,连接概率提高约50%,跳数减少约17.6%,验证了其在动态无人机网络中的有效性.
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.
Nawaf Q.H.Othman;杨清海;蒋昕沛
西安电子科技大学 通信工程学院,西安 710071西安电子科技大学 通信工程学院,西安 710071伊利诺伊大学厄本那-香槟分校电气与计算机工程系,伊利诺伊州 厄巴纳-香槟 61801
信息技术与安全科学
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
decentralized UAV networkresource allocationrouting algorithmGNNDDQNDRL
《电讯技术》 2026 (1)
1-10,10
国家自然科学基金资助项目(61971327)广州市重点科技研究与开发计划(202206030003)
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