高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法

闫莉 岳涛 方旭明

闫莉, 岳涛, 方旭明. 铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法[J]. 电子与信息学报, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254
引用本文: 闫莉, 岳涛, 方旭明. 铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法[J]. 电子与信息学报, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254
YAN Li, YUE Tao, FANG Xuming. Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254
Citation: YAN Li, YUE Tao, FANG Xuming. Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3510-3519. doi: 10.11999/JEIT240254

铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法

doi: 10.11999/JEIT240254
基金项目: 国家自然科学基金(62101460, 62071393, U2268201)
详细信息
    作者简介:

    闫莉:女,副教授,研究方向为铁路5G-R移动通信

    岳涛:男,硕士生,研究方向为毫米波通信感知一体化

    方旭明:男,教授,研究方向为无线与移动通信网络、交通通信与信息系统等

    通讯作者:

    岳涛 atlantisparkling@gmail.com

  • 中图分类号: TN928

Intelligent Wireless Resource Allocation Algorithm for Unmanned Aerial Vehicle Integrated Communication and Sensing Networks in Railway Emergency Scenarios

Funds: The National Natural Science Foundation of China (62101460, 62071393, U2268201)
  • 摘要: 面向恶劣自然环境下地面基础设施受损的铁路场景,该文提出一种无人机(UAV)通信感知一体化无线接入网络架构,实现对列车运行环境的实时感知及应急信息回传。考虑到无人机的续航能力有限,通过建立列车制动距离模型与无人机能耗模型,在满足信息回传通信性能与列车环境感知需求的情况下,联合调整无人机飞行速度和通信发射功率以优化无人机整体能耗。通过分析发现,该优化问题符合马尔可夫决策过程(MDP),基于此,提出一种基于深度双Q网络(DDQN)的无人机通信感知一体化无线资源智能分配算法解决上述问题。最后,该文对所提算法的收敛性能、无人机环境感知距离和无人机能耗进行了仿真实验。仿真结果显示,所提算法具有良好的收敛性能,在满足铁路应急场景环境感知及信息回传需求的同时,能够最大化无人机通信作业时长。
  • 图  1  铁路应急场景下无人机通信感知一体化无线接入网络架构

    图  2  无人机通信感知一体化网络近程模式

    图  3  无人机通信感知一体化网络远程模式

    图  4  不同算法收敛效果比较

    图  5  不同资源分配算法下无人机与列车位置变化比较

    图  6  不同资源分配算法下无人机与列车相对速度变化比较

    图  7  不同资源分配算法下无人机感知距离与列车所需安全制动距离比较

    图  8  不同资源分配算法下无人机剩余能量比较

    图  9  不同资源分配算法下回传信号的通信性能比较

    表  1  仿真参数设置

    参数名参数值参数名参数值
    无人机重量(Newton)20近程无人机初始速度(m/s)15
    转子半径(m)0.4远程无人机初始位置(m)(800,0)
    叶片角速度(rad/s)300远程无人机初始速度(m/s)15
    转子叶片的叶尖速度(m/s)120tacc(s)60
    悬停时平均转子诱导速度(m/s)4.03tdec(s)80
    感应功率增量修正系数0.1tstop(s)100
    剖面阻力系数0.012列车最大速度(m/s)45
    机身阻力比0.6无人机最大速度(m/s)50
    空气密度(kg/m3)1.225感知模式切换速度(m/s)35
    转子坚固度0.005载波频率(GHz)28
    转子盘面积(m2)0.503带宽(MHz)200
    无人机飞行高度(m)60噪声功率密度(dBm/Hz)–174[11]
    列车初速位置(m)(0,0)通信性能门限值(dB)10
    列车初始速度(m/s)15发射增益(dB)6
    近程无人机初始位置(m)(500,0)接收增益 (dB)6
    $ \eta_1 $0.4$\eta_{2} $0.6
    下载: 导出CSV
  • [1] 何励励. 高铁毫米波通信与雷达探测波束赋形技术[D]. [硕士论文], 西南交通大学, 2017.

    HE Lili. Millimeter wave beamforming in communication and disaster detection for HSR[D]. [Master dissertation], Southwest Jiaotong University, 2017.
    [2] 谢庆楚. 一体化综合视频监控技术在南崇高速铁路的应用[J]. 铁路通信信号工程技术, 2023, 20(9): 54–60. doi: 10.3969/j.issn.1673-4440.2023.09.011.

    XIE Qingchu. Application of integrated video monitoring technology in nanning-chongzuo high-speed railway[J]. Railway Signalling & Communication Engineering, 2023, 20(9): 54–60. doi: 10.3969/j.issn.1673-4440.2023.09.011.
    [3] SHI Hongtong, ZHAO Jimin, and MU Ruihou. Design and implementation of laser radar-based railway foreign object intrusion detection system[C]. Proceedings of the 2023 5th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT), Guangzhou, China, 2023: 304–307. doi: 10.1109/ECNCT59757.2023.10280974.
    [4] FOSALAU C, ZET C, and PETRISOR D. Implementation of a landslide monitoring system as a wireless sensor network[C]. Proceedings of the 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, USA, 2016: 1–6. doi: 10.1109/UEMCON.2016.7777813.
    [5] 胡昊. 面向高铁运行环境安全的侵限监测关键技术研究[D]. [博士论文], 中国铁道科学研究院, 2022. doi: 10.27369/d.cnki.gtdky.2022.000005.

    HU Hao. Research on key technologies of intrusion monitoring for high-speed rail running environment safety[D]. [Ph. D. dissertation], China Academy of Railway Sciences, 2022. doi: 10.27369/d.cnki.gtdky.2022.000005.
    [6] ZENG Yong, ZHANG Rui, and LIM T J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36–42. doi: 10.1109/MCOM.2016.7470933.
    [7] WANG Yibing, NIU Yong, WU Hao, et al. Scheduling of UAV-assisted millimeter wave communications for high-speed railway[J]. IEEE Transactions on Vehicular Technology, 2022, 71(8): 8756–8767. doi: 10.1109/TVT.2022.3176855.
    [8] DAI Xingxia, XIAO Zhu, JIANG Hongbo, et al. UAV-assisted task offloading in vehicular edge computing networks[J]. IEEE Transactions on Mobile Computing, 2024, 23(4): 2520–2534. doi: 10.1109/TMC.2023.3259394.
    [9] DONG Jiong, OTA K, and DONG Mianxiong. UAV-based real-time survivor detection system in post-disaster search and rescue operations[J]. IEEE Journal on Miniaturization for Air and Space Systems, 2021, 2(4): 209–219. doi: 10.1109/JMASS.2021.3083659.
    [10] FENG Zhiyong, FANG Zixi, WEI Zhiqing, et al. Joint radar and communication: A survey[J]. China Communications, 2020, 17(1): 1–27. doi: 10.23919/JCC.2020.01.001.
    [11] ZHANG Qixun, WANG Xinna, LI Zhenhao, et al. Design and performance evaluation of joint sensing and communication integrated system for 5G mmwave enabled CAVs[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1500–1514. doi: 10.1109/JSTSP.2021.3109666.
    [12] YANG Jianfei, CHEN Xinyan, ZOU Han, et al. Efficientfi: Toward large-scale lightweight wifi sensing via CSI compression[J]. IEEE Internet of Things Journal, 2022, 9(15): 13086–13095. doi: 10.1109/JIOT.2021.3139958.
    [13] ZHOU Yige, LIU Xin, ZHAI Xiangping, et al. UAV-enabled integrated sensing, computing, and communication for internet of things: Joint resource allocation and trajectory design[J]. IEEE Internet of Things Journal, 2024, 11(7): 12717–12727. doi: 10.1109/JIOT.2023.3335937.
    [14] SUN Geng, LI Jiahui, LIU Yanheng, et al. Time and energy minimization communications based on collaborative beamforming for UAV networks: A multi-objective optimization method[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(11): 3555–3572. doi: 10.1109/JSAC.2021.3088720.
    [15] JU Ying, WANG Haoyu, CHEN Yuchao, et al. Deep reinforcement learning based joint beam allocation and relay selection in mmwave vehicular networks[J]. IEEE Transactions on Communications, 2023, 71(4): 1997–2012. doi: 10.1109/TCOMM.2023.3240754.
    [16] VAN HASSELT H, GUEZ A, and SILVER D. Deep reinforcement learning with double Q-learning[C]. Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, USA, 2016: 2094–2100. doi: 10.1609/aaai.v30i1.10295.
    [17] YAN Li, FANG Xuming, LI Saifei, et al. DRL based beam management for joint sensing and communications in HSR mmWave wireless networks[C]. Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022: 1–6. doi: 10.1109/VTC2022-Spring54318.2022.9860354.
    [18] 曹霞. CRH2-300型动车组的牵引/制动性能研究[D]. [硕士论文], 西南交通大学, 2010.

    CAO Xia. The research on CRH2-300 emu's traction/braking performance[D]. [Master dissertation], Southwest Jiaotong University, 2010.
    [19] ZENG Yong, XU Jie, and ZHANG Rui. Energy minimization for wireless communication with rotary-wing UAV[J]. IEEE Transactions on Wireless Communications, 2019, 18(4): 2329–2345. doi: 10.1109/TWC.2019.2902559.
    [20] 闫莉, 方旭明, 李毅, 等. 面向高铁毫米波通信智能资源管理研究综述[J]. 电子与信息学报, 2023, 45(8): 2806–2817. doi: 10.11999/JEIT220923.

    YAN Li, FANG Xuming, LI Yi, et al. Overview on intelligent wireless resource management of millimeter wave communications under high-speed railway[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2806–2817. doi: 10.11999/JEIT220923.
    [21] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529–533. doi: 10.1038/nature14236.
    [22] JEON H M, LIM J W, and RYOO C. Task assignment for multiple multi-purpose unmanned aerial vehicles using greedy algorithm[J]. International Journal of Aeronautical and Space Sciences, 2024. doi: 10.1007/s42405-024-00726-4.
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  122
  • HTML全文浏览量:  42
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-04-09
  • 修回日期:  2024-08-25
  • 网络出版日期:  2024-08-30
  • 刊出日期:  2024-09-26

目录

    /

    返回文章
    返回