舰船电子工程2024，Vol.44Issue(6)：60-64,5.DOI:10.3969/j.issn.1672-9730.2024.06.013
基于深度强化学习的无人机通信网络效率优化
Optimization of UAV Communication Network Efficiency Based on Deep Reinforcement Learning
摘要
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.关键词
无人机/安全性/频谱能效优化/深度强化学习Key words
UAV/security/spectrum-energy efficiency optimization/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
伍亮,习彤,汤巍,姜军,陈昂..基于深度强化学习的无人机通信网络效率优化[J].舰船电子工程,2024,44(6):60-64,5.基金项目
国家自然科学基金项目"融合语义信息的非法发射源检测与实时协同定位"(编号:62261051)资助. （编号:62261051）
