干扰环境下基于博弈论的无人机群部署与组网方法

韩晨; 刘爱军; 安康; 童新海; 梁小虎

doi:10.11999/JEIT210992

干扰环境下基于博弈论的无人机群部署与组网方法

doi: 10.11999/JEIT210992

韩晨¹,
刘爱军^2, ,,
安康¹,
童新海²,
梁小虎^{2, 3}

1.
国防科技大学第六十三研究所南京 210007
2.
陆军工程大学通信工程学院南京 210007
3.
东南大学信息科学与工程学院南京 210018

基金项目: 国家重点研发计划(2018YFB1801103)，国家自然科学基金(61901502)，江苏省前沿引领技术基础研究专项(BK20192002)，人力资源与社会保障部博士后创新人才支持计划(BX20200101)

详细信息

作者简介:
韩晨：男，1993年生，工程师，研究方向为卫星通信、抗干扰、博弈论

刘爱军：男，1970年生，教授，研究方向为卫星通信、信号处理

安康：男，1989年生，高级工程师，研究方向为空天地网络、智能超表面、通信抗干扰

童新海：男，1974年生，教授，研究方向为卫星通信、信号处理

梁小虎：男，1989年生，讲师，研究方向为信号处理、卫星通信

通讯作者:
刘爱军　liuaj.cn@163.com

¹⁾ 为便于描述，此处略去干扰项\begin{document}${\delta _u}\left( {{{{J}}_g} = 1} \right) $\end{document}，若\begin{document}${\delta _u}\left( {{{{J}}_g} = 1} \right) $\end{document}\begin{document}$ = 0 $\end{document}，则可令\begin{document}$\varGamma \left( g \right) = 0 $\end{document}。
中图分类号: TN915.08
计量
- 文章访问数: 1653
- HTML全文浏览量: 934
- PDF下载量: 294
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-16
- 修回日期: 2022-02-16
- 录用日期: 2022-02-16
- 网络出版日期: 2022-02-28
- 刊出日期: 2022-03-28

Deployment and Networking Methods of UAV Swarm in Jamming Environments Based on Game Theory

HAN Chen¹,
LIU Aijun^{2
, ,},
AN Kang¹,
TONG Xinhai²,
LIANG Xiaohu^{2, 3}

1.
The 63th Institute, National University of Defense Technology, Nanjing 210007, China
2.
College of Communications Engineering, Army Engineering University, Nanjing 210007, China
3.
The School of Information Science and Engineering, Southeast University, Nanjing 210018, China

Funds: The National Key Research and Development Program of China (2018YFB1801103), The National Natural Science Foundation of China (61901502), The Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu Province (BK20192002), The National Postdoctoral Program for Innovative Talents (BX20200101)

摘要

摘要: 该文研究了干扰环境下基于博弈论的无人机(UAV)群部署与组网方法。首先，基于拥塞博弈，提出一种分布式无人机群部署算法(CUD)。每架无人机可以通过与邻近无人机的有限交互，实现自主的位置优化，以提高数据采集量，并增强干扰躲避能力。其次，基于联盟形成博弈，提出一种无人机群动态组网算法(USACF)，可使无人机群在干扰威胁下实现分布式动态子网形成，提高数据传输质量，并增强无人机网络的鲁棒性和可靠性。此外，借助精确势能博弈，从理论上证明了所提博弈模型可以获得稳定的纳什均衡解。最后，仿真结果表明，所提算法相较于传统算法有明显的性能提升。
- 无人机群 /
- 抗干扰 /
- 博弈理论
Abstract: A deployment and networking methods of Unmanned Aerial Vehicle (UAV) swarm based on game theory in the jamming environments is investigated in this paper. Firstly, a Congestion-game based UAV swarm Deployment algorithm (CUD）is proposed. Each UAV can autonomously optimize its position through limited interaction with adjacent UAVs to increase the amount of collected data and enhance the anti-jamming capabilities. Secondly, a UAV Swarm Anti-jamming Coalition Formation algorithm (USACF) is proposed, which enables the UAV swarm to form dynamic sub-networks in a distributed way under the threat of hostile jamming, thus improving the transmission performance and enhancing the robustness and reliability of the UAV networks. Furthermore, it is proved theoretically that the proposed game model can achieve a stable Nash equilibrium with the aid of exact potential game theory. Finally, simulation results verify that the proposed algorithms have obvious performance improvement compared with the conventional algorithms.
- Unmanned Aerial Vehicle (UAV) swarm /
- Anti-jamming /
- Game theory
¹⁾ 为便于描述，此处略去干扰项${\delta _u}\left( {{{{J}}_g} = 1} \right) $，若${\delta _u}\left( {{{{J}}_g} = 1} \right) $$ = 0 $，则可令$\varGamma \left( g \right) = 0 $。

HTML全文

图 1 干扰威胁下的无人机集群部署及组网问题

下载: 全尺寸图片幻灯片

图 2 无人机群部署位置

下载: 全尺寸图片幻灯片

图 3 拥塞博弈过程中无人机群采集数据总量变化情况

下载: 全尺寸图片幻灯片

图 4 拥塞博弈算法性能对比

下载: 全尺寸图片幻灯片

图 5 各无人机采集数据量及数据上传能力

下载: 全尺寸图片幻灯片

图 6 无人机群联盟分组结果

下载: 全尺寸图片幻灯片

图 7 联盟形成算法对比

下载: 全尺寸图片幻灯片

图 8 组网阶段与数据上传阶段分配时间的影响

下载: 全尺寸图片幻灯片

表 1 基于拥塞博弈的无人机群部署算法(Congestion-game based UAV swarm Deployment algorithm, CUD)

输入：无人机群初始位置、地面干扰机位置、网格化目标区域及各网格的数据量
输出：无人机群位置部署
(1) FOR $t = 1:T$
(2) 随机选择1架无人机，计算当前的可采集数据量
(3) 从其动作空间中选择1个运动方向，更新各个网格的可采集数据量，重复覆盖的网格可采集数据量降低，并计算变更位置后该无人机可　　采集的数据量
(4) 如果可采集数据量增加，则更新位置，反之则维持现状
(5) END

下载: 导出CSV

表 2 无人机群联盟形成算法(UAV Swarm Anti-jamming Coalition Formation algorithm, USACF)

输入：无人机群的当前位置和采集数据量、空中干扰机位置、各无人机的数据上传能力
输出：无人机群的联盟分组结果及簇头无人机的选择
(1) 初始化联盟分组，各组仅包含1架无人机
(2) FOR $t = 1:\Delta t$
(3) 随机选择1架无人机，在其所属联盟中选择簇头无人机，将采集数据传输给簇头无人机，簇头无人机上传汇总数据，并计算该无人机的　　联盟效用
(4) 该无人机选择加入邻近的其他联盟，并计算新的联盟效用
(5) 按照双赢准则，如果该无人机可以获得更高的联盟效用，则改变联盟，反之则维持当前联盟不变
(6) END

下载: 导出CSV

表 3 仿真参数

参数	数值	参数	数值
目标区域	$10 {\text{ km} }\times 10{\text{ km} }$	离散网格数	$X = 100$
热点网格平均流量	${\lambda ^h} \in [5,10]{\text{ Mbit}}$	升空平台数量	$A = 3$
其他网格平均流量	${\lambda ^l} \in [3,6]{\text{ Mbit}}$	路径损耗系数	$\alpha = 2$
无人机数量	$M{\text{ = 12}}$	阴影莱斯信道参数	0.126, 10.1, 0.835
无人机感知范围	$F = 1{\text{ km}}$	升空平台高度	${H_u} = 15{\text{ km}}$
无人机群通信距离	${d_{{\rm{th}}} } = 4{\text{ km} }$	地面干扰机数量	${J_g} = 3$
无人机群高度	${H_u} = 1{\text{ km}}$	空中干扰机数量	${J_a} = 3$
通信功率（信噪比）	${{{p_u}} \mathord{\left/ {\vphantom {{{p_u}} {{\sigma ^2}}}} \right. } {{\sigma ^2}}} = 20{\text{ dBW}}$	空中干扰机高度	${H_J} \in \left[ {5,8} \right]{\text{ km}}$
信道带宽	$B = 2{\text{ MHz}}$	干扰功率（干噪比）	${{{p_J}} \mathord{\left/ {\vphantom {{{p_J}} {{\sigma ^2}}}} \right. } {{\sigma ^2}}} = 23{\text{ dBW}}$

下载: 导出CSV

参考文献(36)

[1]	JIA Ziye, SHENG Min, LI Jiandong, et al. LEO-satellite-assisted UAV: Joint trajectory and data collection for internet of remote things in 6G aerial access networks[J]. IEEE Internet of Things Journal, 2021, 8(12): 9814–9826. doi: 10.1109/JIOT.2020.3021255
[2]	ZHAO Nan, LI Yanxin, ZHANG Shun, et al. Security enhancement for NOMA-UAV networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 3994–4005. doi: 10.1109/TVT.2020.2972617
[3]	李晓辉, 方坤, 樊韬, 等. 基于支持向量机的无人机定位信号分离算法研究[J]. 电子与信息学报, 2021, 43(9): 2601–2607. doi: 10.11999/JEIT200725 LI Xiaohui, FANG Kun, FAN Tao, et al. Research on unmanned aerial vehicle location signal separation algorithm based on support vector machines[J]. Journal of Electronics &Information Technology, 2021, 43(9): 2601–2607. doi: 10.11999/JEIT200725
[4]	ZHAO Nan, LU Weidang, SHENG Min, et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019, 26(1): 45–51. doi: 10.1109/MWC.2018.1800160
[5]	高杨, 李东生, 程泽新. 无人机分布式集群态势感知模型研究[J]. 电子与信息学报, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877 GAO Yang, LI Dongsheng, and CHENG Zexin. UAV distributed swarm situation awareness model[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877
[6]	WANG Xue, JIN Tao, HU Liangshuai, et al. Energy-efficient power allocation and Q-learning-based relay selection for relay-aided D2D communication[J]. IEEE Transactions on Vehicular Technology, 2020, 69(6): 6452–6462. doi: 10.1109/TVT.2020.2985873
[7]	WANG Haichao, WANG Jinlong, DING Guoru, et al. Robust spectrum sharing in air-ground integrated networks: Opportunities and challenges[J]. IEEE Wireless Communications, 2020, 27(3): 148–155. doi: 10.1109/MWC.001.1900398
[8]	FOTOUHI A, QIANG Haoran, DING Ming, et al. Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges[J]. IEEE Communications Surveys & Tutorials, 2019, 21(4): 3417–3442. doi: 10.1109/COMST.2019.2906228
[9]	BHATTACHARYA S and BAŞAR T. Game-theoretic analysis of an aerial jamming attack on a UAV communication network[C]. 2010 American Control Conference, Baltimore, USA, 2010: 818–823.
[10]	张孟杰, 赵睿, 王培臣, 等. 基于强化学习的无人机辅助物联网抗敌意干扰算法[J]. 信号处理, 2021, 37(1): 11–18. doi: 10.16798/j.issn.1003-0530.2021.01.002 ZHANG Mengjie, ZHAO Rui, WANG Peichen, et al. Anti-jamming algorithm with reinforcement learning in UAV-aided internet of things[J]. Journal of Signal Processing, 2021, 37(1): 11–18. doi: 10.16798/j.issn.1003-0530.2021.01.002
[11]	LV Shichao, XIAO Liang, HU Qing, et al. Anti-jamming power control game in unmanned aerial vehicle networks[C]. 2017 IEEE Global Communications Conference, Singapore, 2017: 1–6.
[12]	李明, 任清华, 吴佳隆. 无人机多域联合抗干扰智能决策算法研究[J]. 西北工业大学学报, 2021, 39(2): 367–374. doi: 10.3969/j.issn.1000-2758.2021.02.017 LI Ming, REN Qinghua, and WU Jialong. Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm[J]. Journal of Northwestern Polytechnical University, 2021, 39(2): 367–374. doi: 10.3969/j.issn.1000-2758.2021.02.017
[13]	XIAO Liang, LU Xiaozhen, XU Dongjin, et al. UAV Relay in VANETs against smart jamming with reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5): 4087–4097. doi: 10.1109/TVT.2018.2789466
[14]	LU Xiaozhen, XIAO Liang, DAI Canhuang, et al. UAV-aided cellular communications with deep reinforcement learning against jamming[J]. IEEE Wireless Communications, 2020, 27(4): 48–53. doi: 10.1109/MWC.001.1900207
[15]	DUO Bin, WU Qingqing, YUAN Xiaojun, et al. Anti-jamming 3D trajectory design for UAV-enabled wireless sensor networks under probabilistic LoS channel[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 16288–16293. doi: 10.1109/TVT.2020.3040334
[16]	SANJAB A, SAAD W, and BAŞAR T. Prospect theory for enhanced cyber-physical security of drone delivery systems: A network interdiction game[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017: 1–6.
[17]	KUO Y C, CHIU J H, SHEU J P, et al. UAV deployment and IoT device association for energy-efficient data-gathering in fixed-wing multi-UAV networks[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(5): 1934–1946. doi: 10.1109/TGCN.2021.3093453
[18]	ZHANG Xiao and DUAN Lingjie. Fast deployment of UAV networks for optimal wireless coverage[J]. IEEE Transactions on Mobile Computing, 2019, 18(3): 588–601. doi: 10.1109/TMC.2018.2840143
[19]	ZHANG Xiao and DUAN Lingjie. Energy-saving deployment algorithms of UAV swarm for sustainable wireless coverage[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 10320–10335. doi: 10.1109/TVT.2020.3004855
[20]	KOYUNCU E, SHABANIGHAZIKELAYEH M, and SEFEROGLU H. Deployment and trajectory optimization of UAVs: A quantization theory approach[J]. IEEE Transactions on Wireless Communications, 2018, 17(12): 8531–8546. doi: 10.1109/TWC.2018.2878021
[21]	SUN Sujunjie, ZHANG Guopeng, MEI Haibo, et al. Optimizing Multi-UAV deployment in 3-D space to minimize task completion time in UAV-enabled mobile edge computing systems[J]. IEEE Communications Letters, 2021, 25(2): 579–583. doi: 10.1109/LCOMM.2020.3029144
[22]	MOZAFFARI M, SAAD W, BENNIS M, et al. Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage[J]. IEEE Communications Letters, 2016, 20(8): 1647–1650. doi: 10.1109/LCOMM.2016.2578312
[23]	WANG Jie, LIU Miao, SUN Jinlong, et al. Multiple unmanned-aerial-vehicles deployment and user pairing for nonorthogonal multiple access schemes[J]. IEEE Internet of Things Journal, 2021, 8(3): 1883–1895. doi: 10.1109/JIOT.2020.3015702
[24]	HAN Chen, LIU Aijun, WANG Haichao, et al. Dynamic anti-jamming coalition for satellite-enabled army IoT: A distributed game approach[J]. IEEE Internet of Things Journal, 2020, 7(11): 10932–10944. doi: 10.1109/JIOT.2020.2991585
[25]	赵太飞, 宫春杰, 张港, 等. 一种无人机集群安全高效的分区集结控制策略[J]. 电子与信息学报, 2021, 43(8): 2181–2188. doi: 10.11999/JEIT200601 ZHAO Taifei, GONG Chunjie, ZHANG Gang, et al. A safe and high efficiency control strategy of unmanned aerial vehicles partition rendezvous[J]. Journal of Electronics &Information Technology, 2021, 43(8): 2181–2188. doi: 10.11999/JEIT200601
[26]	程潇, 董超, 陈贵海, 等. 面向无人机自组网编队控制的通信组网技术[J]. 计算机科学, 2018, 45(11): 1–12,51. doi: 10.11896/j.issn.1002-137X.2018.11.001 CHENG Xiao, DONG Chao, CHEN Guihai, et al. Communication and networking techniques for formation control in UAV Ad hoc networks[J]. Computer Science, 2018, 45(11): 1–12,51. doi: 10.11896/j.issn.1002-137X.2018.11.001
[27]	钟剑峰, 王红军. 基于5G和无人机智能组网的应急通信技术[J]. 电讯技术, 2020, 60(11): 1290–1296. doi: 10.3969/j.issn.1001-893x.2020.11.005 ZHONG Jianfeng and WANG Hongjun. Emergency communication technology based on 5G and drone intelligent networking[J]. Telecommunication Engineering, 2020, 60(11): 1290–1296. doi: 10.3969/j.issn.1001-893x.2020.11.005
[28]	逯建琦, 南建国, 李雪. 改进的贪婪算法在无人机组网中的研究与应用[J]. 空军工程大学学报:自然科学版, 2020, 21(2): 41–46. doi: 10.3969/j.issn.1009-3516.2020.02.006 LU Jianqi, NAN Jianguo, and LI Xue. Research and application of improved greedy algorithm in UAV network[J]. Journal of Air Force Engineering University:Natural Science Edition, 2020, 21(2): 41–46. doi: 10.3969/j.issn.1009-3516.2020.02.006
[29]	张建东, 李丹, 任齐凤, 等. 基于复杂网络有人机/无人机组网系统同步性分析[J]. 计算机应用, 2016, 36(S1): 12–15,24. ZHANG Jiandong, LI Dan, REN Qifeng, et al. Synchronization analysis for manned/unmanned aerial vehicle networking system based on complex network[J]. Journal of Computer Applications, 2016, 36(S1): 12–15,24.
[30]	周子为, 段海滨, 范彦铭. 仿雁群行为机制的多无人机紧密编队[J]. 中国科学:技术科学, 2017, 47(3): 230–238. doi: 10.1360/N006-00138 ZHOU Ziwei, DUAN Haibin, and FAN Yanming. Unmanned aerial vehicle close formation control based on the behavior mechanism in wild geese[J]. Scientia Sinica Technologica, 2017, 47(3): 230–238. doi: 10.1360/N006-00138
[31]	王泊涵, 吴婷钰, 李文浩, 等. 基于多智能体强化学习的大规模无人机集群对抗[J]. 系统仿真学报, 2021, 33(8): 1739–1753. doi: 10.16182/j.issn1004731x.joss.21-0476 WANG Bohan, WU Tingyu, LI Wenhao, et al. Large-scale UAVs confrontation based on multi-agent reinforcement learning[J]. Journal of System Simulation, 2021, 33(8): 1739–1753. doi: 10.16182/j.issn1004731x.joss.21-0476
[32]	WANG Liming and CHI Yuejie. Stochastic approximation and memory-limited subspace tracking for poisson streaming data[J]. IEEE Transactions on Signal Processing, 2018, 66(4): 1051–1064. doi: 10.1109/TSP.2017.2780041
[33]	PANG Xiaowei, LIU Mingqian, ZHAO Nan, et al. Secrecy analysis of UAV-based mmwave relaying networks[J]. IEEE Transactions on Wireless Communications, 2021, 20(8): 4990–5002. doi: 10.1109/TWC.2021.3064365
[34]	罗睿辞, 叶蔚, 刘学洋, 等. 基于拥塞博弈的微服务运行时资源管理方法[J]. 电子学报, 2019, 47(7): 1497–1505. doi: 10.3969/j.issn.0372-2112.2019.07.013 LUO Ruici, YE Wei, Liu Xueyang, et al. A runtime resource management approach of microservices based on congestion game[J]. Acta Electronica Sinica, 2019, 47(7): 1497–1505. doi: 10.3969/j.issn.0372-2112.2019.07.013
[35]	LIU Dianxiong, WANG Jinlong, XU Kun, et al. Task-driven relay assignment in distributed UAV communication networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(11): 11003–11017. doi: 10.1109/TVT.2019.2942095
[36]	李翠莲, 杨震, 李君. 分组多用户检测联盟模型与联盟形成算法研究[J]. 电子学报, 2010, 38(10): 2447–2452. LI Cuilian, YANG Zhen, and LI Jun. Research on group multiuser detection coalition models and coalition formation algorithm[J]. Acta Electronica Sinica, 2010, 38(10): 2447–2452.