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空中智能反射面增强的URLLC多无人机网络

崔亚平 应兆朋 何鹏 郑玉峰 吴大鹏 王汝言 陈烙

崔亚平, 应兆朋, 何鹏, 郑玉峰, 吴大鹏, 王汝言, 陈烙. 空中智能反射面增强的URLLC多无人机网络[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230288
引用本文: 崔亚平, 应兆朋, 何鹏, 郑玉峰, 吴大鹏, 王汝言, 陈烙. 空中智能反射面增强的URLLC多无人机网络[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230288
CUI Yaping, YING Zhaopeng, HE Peng, ZHENG Yufeng, WU Dapeng, WANG Ruyan, CHEN Luo. Ultra-Reliable Low-Latency Communication Multi-Unmanned Aerial Vehicle Network Assisted by Intelligent Reflecting Surface in Air[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230288
Citation: CUI Yaping, YING Zhaopeng, HE Peng, ZHENG Yufeng, WU Dapeng, WANG Ruyan, CHEN Luo. Ultra-Reliable Low-Latency Communication Multi-Unmanned Aerial Vehicle Network Assisted by Intelligent Reflecting Surface in Air[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230288

空中智能反射面增强的URLLC多无人机网络

doi: 10.3969/j.issn.0258-2724.20230288
基金项目: 国家自然科学基金(61901070,61801065,62271096,61871062,U20A20157,62061007);重庆市教委科学技术研究项目(KJQN202000603,KJQN202300621);重庆市自然科学基金(CSTB2022NSCQ-MSX0468,cstc2020jcyjzdxmX0024,cstc2021jcyjmsxmX0892,CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体(CXQT20017);重庆市研究生科研创新项目(CYB22246)
详细信息
    作者简介:

    崔亚平(1986—),男,副教授,博士,研究方向为车辆及无人机通信,E-mail:cuiyp@cqupt.edu.c

  • 中图分类号: TN92

Ultra-Reliable Low-Latency Communication Multi-Unmanned Aerial Vehicle Network Assisted by Intelligent Reflecting Surface in Air

  • 摘要:

    在多无人机超可靠低时延通信(ultra-reliable low-latency communications,URLLC)网络中,为满足超可靠低时延要求,引入空中智能反射面(intelligent reflecting surface,IRS)辅助通信,提出一种多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient,MADDPG)方法. 首先,建立URLLC多无人机系统模型,其中,多架主无人机作为空中基站为多个地面用户提供服务,一架辅无人机携带IRS作为空中无源中继,辅助主无人机与地面用户通信;然后,考虑多种信道条件和能耗,分别建立复合信道模型和总能耗模型;然后,对问题进行分析,在满足有限块长、无人机能量以及IRS相移的约束下,通过联合优化通信调度、IRS相移以及块长,达到总解码错误率最小化的目标;最后,考虑集中式训练在URLLC场景下的时延敏感限制以及分布式训练在无人机资源限制下的能量限制,设计集中式训练、分布式执行的MADDPG框架. 研究结果表明:总解码错误率随着IRS反射单元的增加而急剧下降;同时,总解码错误率随着块长和发射功率的增大而减小,具体来说,块长每增加20 个符号,总解码错误率减小91.1%.

     

  • 图 1  空中智能反射面辅助的多无人机网络

    Figure 1.  Multi-UAV network assisted by IRS

    图 2  多智能体深度确定性策略梯度框架

    Figure 2.  MADDPG framework

    图 3  多智能体深度确定性策略梯度算法

    Figure 3.  Algorithm of MADDPG

    图 4  总解码错误率随迭代次数的变化曲线

    Figure 4.  Variation of total decoding error rate with number of iterations

    图 5  每个智能体平均Q值随迭代次数的变化曲线

    Figure 5.  Variation of average Q value of each agent with number of iterations

    图 6  无人机飞行轨迹

    Figure 6.  UAV flight trajectory

    图 7  总解码错误率随IRS反射单元数变化的曲线

    Figure 7.  Variation of total decoding error rate with number of IRS units

    图 8  不同发射功率下总解码错误率随块长的变化曲线

    Figure 8.  Variation of total decoding error rate with block length under different transmitted power

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出版历程
  • 收稿日期:  2023-06-21
  • 修回日期:  2023-09-21
  • 网络出版日期:  2024-05-11

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