基于多域联合的无人机集群认知抗干扰算法

doi:10.19678/j.issn.1000-3428.0056784

计算机工程 ›› 2020, Vol. 46 ›› Issue (12): 193-200. doi: 10.19678/j.issn.1000-3428.0056784

基于多域联合的无人机集群认知抗干扰算法

刘春玲^a, 刘敏提^a,b, 丁元明^a,b

大连大学 a. 信息工程学院;b. 通信与网络重点实验室, 辽宁大连 116622

收稿日期:2019-12-02 修回日期:2020-01-07 发布日期:2020-01-20
作者简介:刘春玲(1971-),女,教授、博士,主研方向为智能信号处理与检测;刘敏提,硕士研究生;丁元明,教授、博士、博士生导师。
基金资助:
装备预先研究领域基金（61403110308，61405180402）；辽宁省自然科学基金指导计划项目（2019-ZD-0311）。

Cognitive Anti-Jamming Algorithm for UAV Cluster Based on Multiple Domain Combination

LIU Chunling^a, LIU Minti^a,b, DING Yuanming^a,b

a. College of Information Engineering;b. Key Laboratory of Communication and Network, Dalian University, Dalian, Liaoning 116622, China

Received:2019-12-02 Revised:2020-01-07 Published:2020-01-20

摘要/Abstract

摘要： 为解决无人机集群网络在复杂通信环境中对抗智能性干扰能力较弱的问题，基于智能决策理论，提出一种多域联合的认知抗干扰算法。该算法在优势演员-评论家算法的基础上，将无人机视作智能体，并由感知到的环境频谱状态决策出干扰信道。基于Stackelberg博弈理论，利用功率域压制中度干扰等级的信道干扰信号，减少切换信道的时间开销。通过引入簇头协助的方法，解决由于单个智能体局部频谱感知能力较弱而导致信道决策成功率较低的问题。仿真结果表明，相比QL-AJ算法与AC-AJ算法，该算法能够给出簇内最佳节点个数，提高接收信号信干噪比，且网络整体抗干扰性能较好。

关键词: 认知抗干扰算法, 优势演员-评论家算法, Stackelberg博弈, 无人机集群, 分布式网络

Abstract: To solve the problem of weak ability of Unmanned Aerial Vehicle(UAV) cluster network to resist intelligent interference in complex communication environment,this paper proposes a multiple domain cognitive anti-jamming algorithm based on the intelligent decision-making theory.Based on the dominant actor-critic algorithm,the algorithm regards UAV as an agent and the jamming channel is determined by the perceived environmental spectrum state.Based on the Stackelberg game theory,the interference signals of the middle-level interference channels are suppressed from the power domain to reduce the time cost of channel switching.Cluster head assistance is introduced to solve the problem of low success rate of channel decision-making due to weak local spectrum sensing ability of a single agent.Simulation results show that compared with the QL-AJ algorithm and AC-AJ algorithm,the proposed algorithm can give the optimal number of nodes in the cluster,improves the Signal to Interference-plus Noise Ratio(SINR) of the received signals,and provides better overall anti-jamming performance for networks.

Key words: cognitive anti-jamming algorithm, dominant actor-critic algorithm, Stackelberg game, Unmanned Aerial Vehicle(UAV) cluster, distributed network

中图分类号:

TN975

刘春玲, 刘敏提, 丁元明. 基于多域联合的无人机集群认知抗干扰算法[J]. 计算机工程, 2020, 46(12): 193-200.

LIU Chunling, LIU Minti, DING Yuanming. Cognitive Anti-Jamming Algorithm for UAV Cluster Based on Multiple Domain Combination[J]. Computer Engineering, 2020, 46(12): 193-200.

https://www.ecice06.com/CN/Y2020/V46/I12/193

图/表 11

20201216141829

20201216141838

20201216141842

20201216141845

20201216141848

20201216141852

20201216141855

20201216141859

20201216141902

20201216141908

20201216141911

参考文献

[1] GUPTA L,JAIN R,VASZKUN G.Survey of important issues in UAV communication networks[J].IEEE Communications Surveys & Tutorials,2016,18(2):1123-1152.
[2] LI Haitao,LUO Jiawei,LIU Changjun.Selfish bandit-based cognitive anti-jamming strategy for aeronautic swarm network in presence of multiple jammer[J].IEEE Access,2019,7:30234-30243.
[3] LIN Yu,WANG Tianyu.UAV-assisted emergency communica-tions:an extended multi-armed bandit perspective[J].IEEE Communications Letters,2019,23(5):938-941.
[4] SLIMENI F,CHTOUROU Z,SCHEERS B,et al.Cooperative Q-learning based channel selection for cognitive radio networks[J].Wireless Networks,2019,25(7):4161-4171.
[5] MNIH V,KAVUKCUOGLU K,SILVER D,et al.Human-level control through deep reinforcement learning[J].Nature,2015,518(7540):529-533.
[6] SILVER D,HUANG A,MADDISON C J,et al.Mastering the game of go with deep neural networks and tree search[J].Nature,2016,529(7587):484-489.
[7] WANG S X,LIU H P,GOMES P H,et al.Deep reinforcement learning for dynamic multichannel access in wireless networks[J].IEEE Transactions on Cognitive Communications and Networking,2018,4(2):257-265.
[8] BHOWMIK M,MALATHI P.Spectrum sensing in cognitive radio using actor-critic neural network with krill herd-whale optimization algorithm[J].Wireless Personal Communications,2019,105(1):335-354.
[9] WEI Y F,YU F R,SONG M,et al.User scheduling and resource allocation in HetNets with hybrid energy supply:an actor-critic reinforcement learning approach[J].IEEE Transactions on Wireless Communications,2018,17(1):680-692.
[10] XIAO Liang,CHEN Tianhua,LIU Jinliang,et al.Anti-jamming transmission stackelberg game with observation errors[J].IEEE Communications Letters,2015,19(6):949-952.
[11] AHMED I K,FAPOJUWO A O.Stackelberg equilibria of an anti-jamming game in cooperative cognitive radio networks[J].IEEE Transactions on Cognitive Communications and Networking,2018,4(1):121-134.
[12] XU Yifan,REN Guochun,CHEN Jin,et al.A one-leader multi-follower Bayesian-stackelberg game for anti-jamming transmission in UAV communication networks[J].IEEE Access,2018,6:21697-21709.
[13] JIA Luliang,XU Yuhua,SUN Youming,et al.A multi-domain anti-jamming defense scheme in heterogeneous wireless networks[J].IEEE Access,2018,6:40177-40188.
[14] PARISI S,TANGKARATT V,PETERS J,et al.TD-regularized actor-critic methods[J].Machine Learning,2019,108(8/9):1467-1501.
[15] TINNIRELLO I,BIANCHI G,XIAO Y.Refinements on IEEE 802.11 distributed coordination function modeling approaches[J].IEEE Transactions on Vehicular Tech-nology,2010,59(3):1055-1067.
[16] NAPARSTEK O,COHEN K.Deep multi-user reinforcement learning for distributed dynamic spectrum access[J].IEEE Transactions on Wireless Communications,2019,18(1):310-323.
[17] FANG Xiaojie,ZHANG Ning,ZHANG Shan,et al.On physical layer security:weighted fractional Fourier transform based user cooperation[J].IEEE Transactions on Wireless Communications,2017,16(8):5498-5510.
[18] HAARNOJA T,ZHOU A,ABBEEL P,et al.Soft actor-critic:off-policy maximum entropy deep reinforcement learning with a stochastic actor[EB/OL].[2019-11-01].https://arxiv.org/abs/1801.01290.
[19] O'DONOGHUE B,MUNOS R,KAVUKCUOGLU K,et al.PQG:combining policy gradient and Q-learning[EB/OL].[2019-11-01].https://deepmind.com/research/publicatio ns/pgq-combining-policy-gradient-and-q-learning.
[20] MAO H Z,NETRAVALI R,ALIZADEH M.Neural adaptive video streaming with pensieve[C]//Proceedings of the Conference of the ACM Special Interest Group on Data Communication.New York,USA:ACM Press,2017:197-210.

选择文件类型/文献管理软件名称

选择包含的内容

基于多域联合的无人机集群认知抗干扰算法

Cognitive Anti-Jamming Algorithm for UAV Cluster Based on Multiple Domain Combination

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	秦媛, 张杭, 朱宏鹏, 李炯, 胡航. 卫星MIMO通信系统抗移动无人机集群干扰的在线BSS算法[J]. 计算机工程, 2024, 50(6): 65-76.
[2]	曹栋发, 李勇, 胡创业, 丁男. 基于用户画像与Stackelberg博弈的交通环岛通行策略[J]. 计算机工程, 2023, 49(9): 208-216.
[3]	郑振康, 周金和. 面向多租户网络资源分配的博弈优化策略[J]. 计算机工程, 2022, 48(5): 170-177.
[4]	赵文君, 周金和, 王晶. 面向5G网络云原生应用资源调度的博弈优化策略[J]. 计算机工程, 2021, 47(4): 40-47.
[5]	张鹏飞, 张月霞. UUDN中双层Stackelberg博弈功率控制算法研究[J]. 计算机工程, 2020, 46(9): 186-192.
[6]	韩晨,牛英滔. 基于分层Q学习的联合抗干扰算法[J]. 计算机工程, 2019, 45(5): 279-284.
[7]	胡逸飞,熊焰,黄文超. 基于区块链审计的公钥分发方案[J]. 计算机工程, 2019, 45(5): 29-34.
[8]	赵文君, 周金和. 面向5G网络的Stackelberg博弈缓存优化策略[J]. 计算机工程, 2019, 45(12): 52-57,78.
[9]	张磊,周金和,张元. 基于Stackelberg博弈的云CDN缓存资源分配算法[J]. 计算机工程, 2017, 43(8): 15-20,25.
[10]	杨浩,林喜军,曲海鹏. 分布式网络下改进的Top-k查询算法[J]. 计算机工程, 2017, 43(2): 79-84.
[11]	申彦;杜建国. 分布式故障诊断在SNMP中的模拟与应用[J]. 计算机工程, 2009, 35(8): 137-139.
[12]	黄佳庆;武娟;陈清文;陈京文. 基于PDNS的BitTorrent算法仿真研究[J]. 计算机工程, 2009, 35(12): 20-21.
[13]	谷虤;钱江;应明峰;. 基于证据的代码访问控制机制研究[J]. 计算机工程, 2007, 33(13): 161-163,.
[14]	王蕾;方滨兴. 大规模并行网络模拟系统[J]. 计算机工程, 2007, 33(03): 133-135.
[15]	李强;王宏;王乐春. 基于P2P的分布式网络管理模型研究[J]. 计算机工程, 2006, 32(13): 150-152.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于多域联合的无人机集群认知抗干扰算法

Cognitive Anti-Jamming Algorithm for UAV Cluster Based on Multiple Domain Combination

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献

相关文章 15

编辑推荐

Metrics

本文评价