针对大规模无人机自组网面临的任务需求多样性、电磁环境复杂性、节点高机动性等问题,充分考虑无人机节点高速移动的特点,基于无人机拓扑稳定度和链路通信容量指标设计了 一种无人机多点中继(multi-point relay,MPR)选择方法;为了减少网络路由更新时间,增加无人机自组网路由策略的稳定性和可靠性,提出了一种基于Q-learning的自适应链路状态路由协议(Q-learning based adaptive link state routing,QALSR).仿真结果表明,所提算法性能指标优于现有的主动路由协议.
Q-learning based adaptive link state routing protocol
Large-scale unmanned aerial vehicle(UAV)ad-hoc networks face challenges such as diverse task requirements,complex electromagnetic environments,and high node mobility.To this end,this paper considers the characteristics of high-speed movement of UAV nodes and designs a UAV multi-point relay(MPR)selection method based on UAV topology stability and link communication capacity indicators.Additionally,to reduce network routing update time and increase the stability and reliability of UAV ad-hoc network routing strategies,a Q-learning based adaptive link state routing protocol(QALSR)is proposed.Simulation results demonstrate that the proposed algorithm outperforms existing proactive routing protocols in terms of performance metrics.
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