摘要
随着无人机在各种应用中的广泛应用,其通信网络的安全性、频谱和能源效率问题逐渐凸显.该研究针对无人机群通信网络提出了一种基于深度强化学习的联合优化策略.首先构建了一个考虑安全威胁、频谱共享和能源消耗的模型.然后,通过深度强化学习训练了智能代理来动态地选择最佳的频谱分配和能源策略,以在保证网络安全的同时提高频谱和能源效率.通过大量的仿真实验,证明了该方法在提高通信安全性、频谱利用率和能源效率方面均表现出色,且相比传统基线和平均分配DQN-wopa[15]方法有明显的优势.
Abstract
With the widespread application of UAVs in various applications,the security,spectrum and energy efficiency of their communication networks have gradually become prominent.In this study,a joint optimization strategy based on deep reinforce-ment learning is proposed for UAV swarm communication networks.First,this paper builds a model that takes into account security threats,spectrum sharing,and energy consumption.Then,through deep reinforcement learning,this paper trains intelligent agents to dynamically select the best spectrum allocation and energy strategies to improve spectrum and energy efficiency while main-taining cybersecurity.Through a large number of simulation experiments,it shows that this method performs well in improving com-munication security,spectrum utilization and energy efficiency,and has obvious advantages over the traditional baseline and aver-age allocation DQN-wopa[15]method.
国家科技期刊平台
NSTL
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