• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 59 Issue 4
Jul.  2024
Turn off MathJax
Article Contents
GUO Yang, GAO Yuan, CHENG Shaochi, WANG Xiaonan. Optimization Control Strategy for Low-Altitude and Single-Layer Unmanned Aerial Vehicle Network Coverage[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 890-897. doi: 10.3969/j.issn.0258-2724.20230535
Citation: GUO Yang, GAO Yuan, CHENG Shaochi, WANG Xiaonan. Optimization Control Strategy for Low-Altitude and Single-Layer Unmanned Aerial Vehicle Network Coverage[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 890-897. doi: 10.3969/j.issn.0258-2724.20230535

Optimization Control Strategy for Low-Altitude and Single-Layer Unmanned Aerial Vehicle Network Coverage

doi: 10.3969/j.issn.0258-2724.20230535
  • Received Date: 13 Oct 2023
  • Rev Recd Date: 14 Dec 2023
  • Available Online: 29 Apr 2024
  • Publish Date: 15 Dec 2023
  • In order to study the autonomous control problem of unmanned aerial vehicle (UAV) networks in emergency communication scenarios, multiple rotary-wing UAVs equipped with 6G base stations were used, and a low-altitude and single-layer UAV network was formed through the interconnection between UAVs, thus providing wireless network services to users in the ground task area. A numerical model was constructed for typical scenarios, and a multi-agent reinforcement learning method was used to solve the optimization control strategy of the UAV network. The effects of the number of UAV base stations and the communication distance of UAV base stations on the wireless network coverage in the task area were investigated. The research results indicate that the optimization control strategy obtained by using reinforcement learning methods can converge well. The learning curves of the UAV network coverage score and fairness coverage index have similar trends. The curves rapidly increase between the 1000th and 2000th episode and then change slowly. Under the conditions of a communication coverage distance of 1 km and a flight altitude of 300 m for UAV base stations, the UAV network coverage score increases by 53.28%, and the fairness coverage index increases by 43.57% when the number of UAV base stations increases from 3 to 7. Under the condition of five UAV base stations and a flight altitude of 300 m, the UAV network coverage score increases by 86.01%, and the fairness coverage index increases by 41.47% when the communication distance of the base stations increases from 1.0 km to 2.5 km.

     

  • loading
  • [1]
    陈新颖,盛敏,李博,等. 面向6G的无人机通信综述[J]. 电子与信息学报,2022,44(3): 781-789. doi: 10.11999/JEIT210789

    CHEN Xinying, SHENG Min, LI Bo, et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics & Information Technology, 2022, 44(3): 781-789. doi: 10.11999/JEIT210789
    [2]
    向庭立,王红军,杨刚,等. 分布式无人机网络覆盖优化算法[J]. 空军工程大学学报(自然科学版),2019,20(4): 59-65.

    XIANG Tingli, WANG Hongjun, YANG Gang, et al. Research on distributed UAV network coverage optimization algorithm[J]. Journal of Air Force Engineering University (Natural Science Edition), 2019, 20(4): 59-65.
    [3]
    王巍,梁雅静,刘阳,等. 城市灾区无人机网络自适应覆盖优化算法[J]. 计算机工程与应用,2022,58(14): 258-268. doi: 10.3778/j.issn.1002-8331.2010-0211

    WANG Wei, LIANG Yajing, LIU Yang, et al. Adaptive coverage optimization algorithm for drone network in urban disaster areas[J]. Computer Engineering and Applications, 2022, 58(14): 258-268. doi: 10.3778/j.issn.1002-8331.2010-0211
    [4]
    王超. 基于强化学习的无线网络移动性管理技术研究[D]. 合肥: 中国科学技术大学,2021.
    [5]
    靳晓洁,石建迈,伍国华,等. 无人机基站部署问题综述:模型与算法[J]. 控制理论与应用,2022,39(12): 2219-2232.

    JIN Xiaojie, SHI Jianmai, WU Guohua, et al. Review of the UAV base station deployment problem: models and algorithms[J]. Control Theory & Applications, 2022, 39(12): 2219-2232.
    [6]
    郭艺轩,贾向东,曹胜男,等. 三维动态无人机网络覆盖性能与信道容量分析[J]. 重庆邮电大学学报(自然科学版),2022,34(4): 662-668. doi: 10.3979/j.issn.1673-825X.202101080012

    GUO Yixuan, JIA Xiangdong, CAO Shengnan, et al. Analysis of 3D dynamic UAV network coverage performance and channel capacity[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2022, 34(4): 662-668. doi: 10.3979/j.issn.1673-825X.202101080012
    [7]
    吕忠昊. 基于强化学习的无人机通信系统容量优化研究[D]. 合肥: 中国科学技术大学,2021.
    [8]
    蒋逸凡,郭婧,费泽松. 多层高度的无人机-地面异构网络的覆盖性能[J]. 无线电通信技术,2021,47(5): 649-654. doi: 10.3969/j.issn.1003-3114.2021.05.020

    JIANG Yifan, GUO Jing, FEI Zesong. Coverage performance of UAV-terrestrial heterogeneous network with multiple altitudes[J]. Radio Communications Technology, 2021, 47(5): 649-654. doi: 10.3969/j.issn.1003-3114.2021.05.020
    [9]
    崔壮壮. 面向空地通信的无线信道特性和建模研究[D]. 北京: 北京交通大学,2022.
    [10]
    马张枫. 面向B5G/6G无线网络的无人机信道建模研究[D]. 北京: 北京交通大学,2022.
    [11]
    王莉,魏青,徐连明,等. 面向通信-导航-感知一体化的应急无人机网络低能耗部署研究[J]. 通信学报,2022,43(7): 1-20.

    WANG Li, WEI Qing, XU Lianming, et al. Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing[J]. Journal on Communications, 2022, 43(7): 1-20.
    [12]
    夏景明,刘玉风,谈玲. 基于蜂窝网络的多无人机能量消耗最优化算法研究[J]. 通信学报,2023,44(2): 185-197.

    XIA Jingming, LIU Yufeng, TAN Ling. Research on multi-UAV energy consumption optimization algorithm for cellular-connected network[J]. Journal on Communications, 2023, 44(2): 185-197.
    [13]
    窦邵婷. 无人机辅助移动边缘计算的空地能耗折衷研究[D]. 南昌: 南昌大学,2022.
    [14]
    LIU C H, CHEN Z Y, TANG J, et al. Energy-efficient UAV control for effective and fair communication coverage: a deep reinforcement learning approach[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(9): 2059-2070. doi: 10.1109/JSAC.2018.2864373
    [15]
    LIU C H, MA X X, GAO X D, et al. Distributed energy-efficient multi-UAV navigation for long-term communication coverage by deep reinforcement learning[J]. IEEE Transactions on Mobile Computing, 2020, 19(6): 1274-1285. doi: 10.1109/TMC.2019.2908171
    [16]
    QI H, HU Z Q, HUANG H, et al. Energy efficient 3-D UAV control for persistent communication service and fairness: a deep reinforcement learning approach[J]. IEEE Access, 2020, 8: 53172-53184. doi: 10.1109/ACCESS.2020.2981403
    [17]
    JAIN R K, CHIU D M W, HAWE W R. A quantitative measure of fairness and discrimination for resource allocation in shared computer system[R]. Hudson MA: Digital Equipment Corporation, 1984.
    [18]
    LOWE R, WU Y, TAMAR A, et al. Multi-agent actor-critic for mixed cooperative-competitive environments [C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM, 2017: 6382-6393.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(2)

    Article views(306) PDF downloads(99) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return