通信学报 ›› 2020, Vol. 41 ›› Issue (6): 26-33.doi: 10.11959/j.issn.1000-436x.2020114

• 学术论文 • 上一篇    下一篇

无人机网络中基于分层博弈的干扰对抗频谱接入优化

范超琼,赵成林,李斌   

  1. 北京邮电大学信息与通信工程学院,北京 100876
  • 修回日期:2020-05-20 出版日期:2020-06-25 发布日期:2020-07-04
  • 作者简介:范超琼(1992- ),女,河北石家庄人,北京邮电大学博士生,主要研究方向为无线资源管理、博弈论、强化学习等|赵成林(1964- ),男,河北石家庄人,博士,北京邮电大学教授,主要研究方向为信号处理、无线通信技术等|李斌(1985- ),男,甘肃天水人,博士,北京邮电大学副教授,主要研究方向为统计信号估计与检测、认知无线电、60 GHz毫米波通信、高性能优化算法设计等
  • 基金资助:
    国家自然科学基金资助项目(U1805262);国家自然科学基金资助项目(61971050)

Hierarchical game based spectrum access optimization for anti-jamming in UAV network

Chaoqiong FAN,Chenglin ZHAO,Bin LI   

  1. School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Revised:2020-05-20 Online:2020-06-25 Published:2020-07-04
  • Supported by:
    The National Natural Science Foundation of China(U1805262);The National Natural Science Foundation of China(61971050)

摘要:

针对无人机通信网络中的干扰对抗问题,提出了一种基于分层博弈的自适应频谱接入优化机制。考虑到无人机网络节点的动态特性,将干扰器视为分层博弈领导者,无人机用户视为分层博弈跟随者,不同层级间博弈参与者具有不同的效应函数,采用斯坦伯格均衡分析所构建的博弈并证明了均衡解的存在性和唯一性。在此基础上设计一种分层信道选择学习算法来求解博弈的均衡解,并分析其收敛性能。仿真表明,所提算法能使网络节点智能地调整信道选择策略,从而获得良好的吞吐量性能。

关键词: 无人机网络, 干扰对抗, 频谱接入, 分层博弈, 强化学习

Abstract:

For the anti-jamming spectrum access optimization problem in unmanned aerial vehicle (UAV) communication networks,considering the complex and diverse malicious jamming from jammers,a Bayesian Stackelberg game was proposed to formulate the competitive relations between UAV users and jammers.Specifically,jammers acted as the leader,whereas users acted as followers of the proposed game.Based on their different utility functions,the jammer and users independently and selfishly selected their optimal strategies and obtained the optimal channels selection.Due to the NP-hard nature,it was challenging to obtain the Stackelberg Equilibrium of the proposed game.To this end,a hierarchical learning framework was formulated,and a hierarchical channel selection-learning algorithm was proposed.Simulations demonstrate that with the proposed hierarchical learning algorithm,UAV nodes can adjust their channel selection and obtain superior performance.

Key words: unmanned aerial vehicle network, anti-jamming, spectrum access, hierarchical game, reinforcement learning

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