无人机网络中基于分层博弈的干扰对抗频谱接入优化
Hierarchical game based spectrum access optimization for anti-jamming in UAV network
- 通信学报 2020年41卷第6期 页码：26-33
- 作者机构：
北京邮电大学信息与通信工程学院，北京 100876
- 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" ]
- 基金信息：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)
DOI：10.11959/j.issn.1000-436x.2020114
中图分类号： TN92-
网络首发：2020-06，
纸质出版：2020-06-25
- 稿件说明：
移动端阅览
范超琼, 赵成林, 李斌. 无人机网络中基于分层博弈的干扰对抗频谱接入优化[J]. 通信学报, 2020,41(6):26-33.
Chaoqiong FAN, Chenglin ZHAO, Bin LI. Hierarchical game based spectrum access optimization for anti-jamming in UAV network[J]. Journal on Communications, 2020, 41(6): 26-33.
范超琼, 赵成林, 李斌. 无人机网络中基于分层博弈的干扰对抗频谱接入优化[J]. 通信学报, 2020,41(6):26-33. DOI： 10.11959/j.issn.1000-436x.2020114.
Chaoqiong FAN, Chenglin ZHAO, Bin LI. Hierarchical game based spectrum access optimization for anti-jamming in UAV network[J]. Journal on Communications, 2020, 41(6): 26-33. DOI： 10.11959/j.issn.1000-436x.2020114.
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。
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.
关键词
Keywords
references
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