火力与指挥控制2024，Vol.49Issue(2)：42-47,6.DOI:10.3969/j.issn.1002-0640.2024.02.006
无人机辅助通信网络中基于强化学习的用户速率优化算法
Reinforcement Learning-Based User's Rate Optimizaton Algorithm for UAV-aided Communication Network
摘要
Abstract
Unmanned Aerial Vehicles(UAVs)aid ground cellular base station and form a hybrid communication network,which is expected to become an important means of improving rate of users.As for the communication network with UAV-aided base,Multi-armed Bandits-based Rate Optimization(MBRO)algorithm is proposed First,the joint optimization problem is established,and then the improved K-means clustering algorithm and multi-armed bandit algorithm are respectively used to solve the problem.MBRO algorithm uses K-means clustering algorithm to realize the deployment of UAV,and uses multi-armed bandit algorithm is utilized to complete channel allocation and transmission power allocation of UAV.The simulation results show that compared with the similar benchmark algorithms,MBRO algorithm increases the rate of the user terminal.关键词
无人机/基站/用户速率/K-means算法/多臂赌博机Key words
unmanned aerial vehicles/base station/rate of user/K-means algorithm/multi-armed bandit分类
信息技术与安全科学引用本文复制引用
张延年,吴昊,张云..无人机辅助通信网络中基于强化学习的用户速率优化算法[J].火力与指挥控制,2024,49(2):42-47,6.基金项目
江苏高校哲学社会科学研究一般项目(2021SJA0689) （2021SJA0689）
2021年江苏省高校"青蓝工程"培养对象优秀教学团队资助 （）
南京交通职业技术学院重大课题(JZ2103) （JZ2103）
