无人机边缘计算: 架构、多址接入与计算卸载

doi:10.12305/j.issn.1001-506X.2024.09.31

摘要/Abstract

摘要：

无人机(unmanned aerial vehicle, UAV)边缘计算技术将UAV平台与移动边缘计算技术相结合, 充分利用UAV的灵活性和机动性, 为用户设备提供及时有效的计算服务。从UAV边缘计算的网络架构入手, 提出基于网络功能虚拟化和软件定义网络的技术架构。针对UAV边缘计算的关键技术, 总结对比UAV边缘计算中不同的多址接入方案, 并从不同的优化目标出发, 对基于经典非凸优化、博弈论以及人工智能方法的计算卸载策略进行总结和分析。最后, 探讨和展望未来的研究方向。

关键词: 无人机, 移动边缘计算, 网络架构, 多址接入, 计算卸载

Abstract:

Unmanned aerial vehicle (UAV)-enabled edge computing combines mobile edge computing technology with UAV platform. It can provide timely and effective computing services for wireless devices by taking advantages of the flexible deployment and mobility of UAV. Starting with the network architecture of UAV-enabled edge computing, the technical architecture based on network function virtualization and software-defined networks is proposed. For the key technologies of UAV-enabled edge computing, different multiple access schemes in UAV-enabled edge computing network are compared. Furthermore, according to different optimization objectives, the computation offloading strategies based on classical non-convex optimization, game theory, and artificial intelligence are summarized and analyzed. Finally, the future research direction is discussed and prospected.

Key words: unmanned aerial vehicle (UAV), mobile edge computing, network architecture, multiple access, computation offloading

中图分类号:

TN92

刘鹏涛, 雷菁, 刘伟. 无人机边缘计算: 架构、多址接入与计算卸载[J]. 系统工程与电子技术, 2024, 46(9): 3198-3210.

Pengtao LIU, Jing LEI, Wei LIU. Unmanned aerial vehicle-enabled edge computing: architecture, multiple access and computation offloading[J]. Systems Engineering and Electronics, 2024, 46(9): 3198-3210.

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表1

UAV-MEC网络中多址接入技术总结对比"

文献	年份	多址接入	研究内容
[20]	2019	TDMA	提出基于TDMA接入方案的工作排队模型, 允许UAV-MEC系统中不同用户并行传输和执行任务
[21]	2021	TDMA	从物理层安全的角度研究基于TDMA方案的UAV-MEC系统
[22]	2021	FDMA	说明在以强视距路径为主、频率选择性小的空对地信道时更适合FDMA接入
[23]	2020	FDMA	研究UAV与基站协同提供MEC服务, 使用FDMA方案以带宽区分多用户设备传输的任务
[24]	2021	FDMA	考虑具备时间敏感性任务的设备需求, 研究基于FDMA的多UAV-MEC网络
[25]	2020	OFDMA	研究OFDMA接入方案下UAV-MEC系统中的延迟感知调度问题
[26]	2020	OFDMA	讨论OFDMA框架下MEC网络中计算效率加权和最大化问题
[27]	2021	OFDMA	通过优化用户设备占用OFDMA方案的正交频带、发射功率使得设备与UAV的传输速率最大化
[28]	2020	CDMA	将CDMA方案引入IEEE 802.11ah标准, 以适应UAV群体通信场景下的多个用户设备多路访问
[29]	2021	CDMA	设备通过CDMA接入UAV的MEC服务器, 不同的正交码允许多个用户同时有效地共享频谱资源
[30]	2020	SDMA	建立基于SDMA接入的UAV-MEC网络的联合通信计算优化模型以及实物平台
[34]	2018	NOMA	证明UAV-MEC网络中NOMA方案节省了更多的UAV能耗, 但会导致额外的复杂度
[35]	2018	NOMA	研究UAV-MEC系统能量最小化的问题, NOMA方案的能量消耗降低了18.75%
[36]	2021	NOMA	为了支持UAV-MEC网络中众多设备接入, 采用NOMA接入方案, 能耗降低了16.66%
[37]	2020	NOMA	利用NOMA方案, 在8个用户的UAV-MEC网络中, 加权能耗减少了约20%
[38]	2022	RSMA	将RSMA引入UAV-MEC网络, 优化RSMA的卸载决策、分拆比, 系统能耗优于PD-NOMA方案
[39]	2019	NOMA	将NOMA引入UAV-MEC网络, 联合轨迹和计算卸载优化, 最小化所有用户任务的总延迟
[40]	2020	NOMA	建模一对PD-NOMA用户组的上传时间和同信道干扰的关系, 确定NOMA用户配对和卸载决策
[41]	2022	SCMA	验证在MEC网络中SCMA方案比OFDMA接入可以减少数据的上传时间, 降低任务计算时延
[43]	2021	NOMA	研究基于NOMA接入方案的UAV-MEC系统, 最大化所有物联网设备的计算率问题
[44]	2020	RSMA	优化RSMA的预编码、分拆速率, 最大化用户加权和数据速率
[45]	2023	RSMA	求出成功计算概率的封闭表达式, 相比OMA与NOMA方案, RSMA能够提高成功计算概率

表1

表2

UAV-MEC网络中计算卸载策略求解方法总结对比"

文献	场景	优化目标	方法分类	计算卸载策略求解方法
[47]	单无人机	降低时延	非凸优化	在引入辅助变量的基础上, 提出一种改进的基于惩罚对偶分解的算法
[48]	单无人机	降低时延	非凸优化	采用ADMM、Dinkelbach算法和SCA算法得到最优的卸载策略
[49]	多无人机	降低时延	非凸优化	利用SCA算法解决在无人机能量受限的情况下进行计算卸载决策的问题
[50]	多无人机	降低时延	非凸优化	多无人机协同提供计算服务, 基于BCD和SCA技术最小化任务时延
[51]	多无人机	降低时延	非凸优化	多无人机边缘云协同卸载, 利用李雅普诺夫优化方法进行在线任务卸载决策
[52]	多无人机	降低时延	非凸优化	将计算任务卸载问题转化为双边匹配问题, 使每架无人机与设备相匹配
[53]	多无人机	降低时延	博弈论	提出一种基于Stackelberg博弈方法求解UAV-MEC网络中计算卸载决策问题
[54]	多无人机	降低时延	智能算法	提出基于MARL算法的计算卸载策略, 选择合适的无人机, 实现协同任务卸载
[55]	单无人机	降低能耗	非凸优化	在通信计算容量资源的限制下, 使用BCD算法交替求解得出最佳的任务分配策略
[56]	单无人机	降低能耗	非凸优化	提出一种基于BCD和SCA技术的迭代优化算法最小化UAV-MEC网络能量消耗
[57]	单无人机	降低能耗	非凸优化	利用李雅普诺夫优化法和交替迭代优化法, 分析任务队列、求解出计算卸载策略
[58]	多无人机	降低能耗	博弈论	针对分层卸载特征, 构建离散的多领导者多跟随者的能量最小化Stackelberg博弈
[59]	多无人机	降低能耗	智能算法	利用DRL网络进行任务卸载决策并通过DNN网络进行计算任务预测
[60]	单无人机	降低能耗	智能算法	利用LSTM网络进行计算任务预测, 提出了能耗最优的3层计算卸载算法
[61]	多无人机	降低能耗	智能算法	提出基于博弈理论和DRL算法框架用于多无人机与地面基站协同的计算卸载
[62]	单无人机	权衡时延和能耗	非凸优化	利用拉格朗日对偶法和SCA算法获得近似最优解、求解计算卸载策略
[63]	单无人机	权衡时延和能耗	非凸优化	通过交替优化算法求解平衡无人机能量和任务完成时间的帕累托最优解
[29]	单无人机	权衡时延和能耗	博弈论	提出一个基于博弈论的方案来寻找最优计算卸载策略并证明纳什均衡的存在性
[64]	单无人机	权衡时延和能耗	智能算法	将优化问题表述为半马尔可夫过程, 使用动态规划和DRL算法求解最优卸载决策
[65]	单无人机	权衡时延和能耗	智能算法	提出基于DQN网络调整计算卸载比例的计算卸载策略
[66]	多无人机	权衡时延和能耗	智能算法	提出基于DDPG网络的计算卸载策略, 并通过公平性指数检查各无人机的状态
[67]	多无人机	权衡时延和能耗	智能算法	研究多无人机的协同MEC系统, 提出一种协作式MARL框架求解任务卸载策略
[68]	多无人机	权衡时延和能耗	智能算法	利用MADDPG框架, 优化多无人机的协同卸载决策, 降低时延与设备能量消耗

表2

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