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无人机辅助边缘计算网络轨迹规划与资源分配研究综述

王侃 曹铁林 李旭杰 李红艳 李萌 周墨淼

王侃, 曹铁林, 李旭杰, 李红艳, 李萌, 周墨淼. 无人机辅助边缘计算网络轨迹规划与资源分配研究综述[J]. 电子与信息学报, 2025, 47(5): 1266-1281. doi: 10.11999/JEIT241071
引用本文: 王侃, 曹铁林, 李旭杰, 李红艳, 李萌, 周墨淼. 无人机辅助边缘计算网络轨迹规划与资源分配研究综述[J]. 电子与信息学报, 2025, 47(5): 1266-1281. doi: 10.11999/JEIT241071
WANG Kan, CAO Tielin, LI Xujie, LI Hongyan, LI Meng, ZHOU Momiao. A Survey on Trajectory Planning and Resource Allocation in Unmanned Aerial Vehicle-assisted Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1266-1281. doi: 10.11999/JEIT241071
Citation: WANG Kan, CAO Tielin, LI Xujie, LI Hongyan, LI Meng, ZHOU Momiao. A Survey on Trajectory Planning and Resource Allocation in Unmanned Aerial Vehicle-assisted Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1266-1281. doi: 10.11999/JEIT241071

无人机辅助边缘计算网络轨迹规划与资源分配研究综述

doi: 10.11999/JEIT241071
基金项目: 国家自然科学基金(61801379, 62371374, 62371012),北京市自然科学基金(4252001),安徽省自然科学基金(2408085MF160)
详细信息
    作者简介:

    王侃:男,副教授,博士,研究方向为算力网络、边缘计算、生成式AI与无线通信、随机优化

    曹铁林:男,硕士生,研究方向为算力网络、边缘计算、神经网络

    李旭杰:男,教授,博士,研究方向为无线网络、人工智能、边缘计算、车联网、水利信息交叉

    李红艳:女,教授,博士,研究方向为异构融合网络、大规模时间确定网络、无线移动自组织网络、无线局域网

    李萌:男,副教授博士研究方向为边缘智能与算力网络、区块链技术、网络资源优化管理

    周墨淼:男,副教授,博士,研究方向未低时延高可靠车联网、交通信息视觉感知、强化学习辅助智慧交通

    通讯作者:

    李红艳 hyli@xidian.edu.cn

  • 中图分类号: TN929.5

A Survey on Trajectory Planning and Resource Allocation in Unmanned Aerial Vehicle-assisted Edge Computing Networks

Funds: The National Natural Science Foundation of China (61801379, 62371374, 62371012), The Natural Science Foundation of Beijing (4252001), The Natural Science Foundation of Anhui Province (2408085MF160)
  • 摘要: 无人机辅助移动边缘计算(MEC)具有灵活部署、快速响应、广域覆盖、分布计算和可扩展性等优势,在智慧城市、环境监测和应急救援等领域具有广阔应用前景,是提升低空智能网联服务质量的重要研究方向。该文围绕无人机辅助MEC场景的飞行轨迹与资源分配联合优化,从离线优化和在线优化两个维度展开分析:针对离线联合优化,以不同优化性能指标为切入点,从网络场景、性能控制方法和算法设计3个方面梳理研究现状;针对在线联合优化,以优化框架为基础,从网络场景、性能指标和控制方法3个方面梳理研究现状;针对离线与在线混合优化,阐述当前研究成果。最后,聚焦无人机辅助MEC网络与其它网络制式融合时产生的新问题,讨论离线优化环境状态收集、离线优化智能化求解、在线优化多无人机实时协同、在线优化实时信息反馈、无人机能效优化和空-地通信安全保障等关键技术挑战及其未来研究方向。
  • 图  1  无人机辅助MEC架构

    图  2  基于离散时间的系统状态转移

    图  3  结合WPT技术的无人机辅助MEC架构

    图  4  结合RIS技术的无人机辅助MEC架构

    图  5  基于无人机辅助MEC的物理层安全通信系统

    图  6  数字孪生辅助的无人机MEC架构

    图  7  云-边-端3层MEC集群架构

    表  1  离线联合优化研究现状

    性能指标 性能优化控制方法 无人机数量 文献编号 优化算法
    用户能耗 计算卸载、无人机轨迹、悬停时间 单无人机 [13,14] 启发式、动态规划、差分进化、
    贪婪算法
    能耗 无人机能耗 无人机轨迹、计算卸载、资源分配
    (传输功率、CPU频率等)、用户协同
    单无人机 [12,15] 启发式、SCA、拉格朗日对偶法、凸优化
    用户与无人机加权能耗 无人机轨迹、计算卸载、资源分配、RIS相位偏移、内容缓存、
    传输波束赋形、用户分簇
    单无人机 [1621] 动态规划、凸优化、CCCP, BCD, Dinkelbach
    时延 资源分配、任务调度、无人机轨迹、
    计算卸载
    文献[22,23]为单无人机
    文献[2426]为多无人机
    [2226] BCD、差分凸规划、贪心、SCA、惩罚CCCP、启发式、聚类
    时延与能耗
    加权和
    资源分配、计算卸载、无人机轨迹 多无人机 [27] 博弈论、注水法、梯度下降
    计算效率 资源分配(传输功率、CPU频率等)、
    计算卸载、计算调度、无人机轨迹
    单无人机 [2831] Dinkelbach, BCD, SCA、凸优化
    吞吐量与
    信息年龄
    无人机轨迹、资源分配、计算卸载 文献[32,34]为单无人机
    文献[35]为多无人机
    [32,34,35] 启发式、动态规划、差分进化、
    贪婪算法、DDPG, CQL
    其它 活跃设备的
    计算数据量
    无人机轨迹、资源分配 单无人机 [33] 启发式、SCA、拉格朗日对偶法、凸优化
    安全容量 无人机轨迹、资源分配(卸载功率、
    噪声发射功率)、计算卸载
    单无人机 [36,37] 动态规划、CCCP, BCD, Dinkelbach
    下载: 导出CSV

    表  2  在线优化研究现状

    优化框架 性能优化控制方法 性能指标 无人机数量 文献
    Lyapunov优化 无人机轨迹、任务卸载、
    资源分配、能量收集
    用户体验质量、能耗、
    吞吐量、时延
    文献[42,44]为多无人机,
    其余为单无人机
    [3845]
    强化学习优化 价值学习 无人机轨迹、资源分配、
    任务卸载、信息年龄、用户调度
    安全容量、能耗、时延、
    卸载任务量
    文献[4649]为单无人机,
    其余为多无人机
    [4651]
    强化学习优化 策略学习 无人机轨迹、资源分配、
    任务卸载、用户-无人机关联、
    无人机飞行时间
    能耗、时延、公平性、
    处理成功率、能效
    文献[5355,58,63,65]为多无人机,
    其余为单无人机
    [5265]
    混合时间尺度机制 无人机轨迹、资源分配、
    任务卸载、内容缓存
    时延、能耗、更新年龄 多无人机 [6668]
    在线拍卖框架 无人机轨迹、用户-无人机关联、支付代价 社会成本 多无人机 [69]
    下载: 导出CSV
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  • 收稿日期:  2024-12-04
  • 修回日期:  2025-03-28
  • 网络出版日期:  2025-04-11
  • 刊出日期:  2025-05-01

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