重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：277-286,10.DOI:10.3979/j.issn.1673-825X.202302190043
基于机器学习链路权重优化的无人机网络路由算法
Research on UAV networks routing algorithm based on machine learning-enabled link weight optimization
摘要
Abstract
To accelerate communication procedure of OODA loop in the scenario of distributed mosaic warfare,we propose a novel unmanned aerial vehicle(UAV)routing algorithm based on machine learning-enabled link weight optimization in this paper.Our algorithm first constructs the communication completion time model for the procedure of OODA loop.Then,to minimize the communication completion time of OODA loop,the algorithm leverages the gradient descent method of ma-chine learning to achieve link weight optimization for UAV cluster network distributed routing.Computer simulation results show that,compared with the current UAV routing algorithm,our algorithm can observably shorten the communication time of OODA loop,and increase the rate of packet success transmission.关键词
无人机集群网络/侦察-判断-决策-行动(OODA)/路由算法/机器学习Key words
UAV cluster networks/observe-orient-decide-act loop(OODA)/routing algorithm/machine learning分类
信息技术与安全科学引用本文复制引用
乔冠华..基于机器学习链路权重优化的无人机网络路由算法[J].重庆邮电大学学报（自然科学版）,2024,36(2):277-286,10.基金项目
中国电子科技集团公司第十研究所创新理论技术群基金项目(2021JSQ0201) The 10th Research Institute Fund of China Electronics Technology Group Corporation(2021JSQ0201) （2021JSQ0201）
