Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading

Yi Ya-qian; Wu Qing-jie; Cui Miao; Zhang Guang-chi

doi:10.12052/gdutxb.230068

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading

更新时间：2026-05-27

- Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading
- Vol. 41, Issue 4, Pages: 70-79(2024)
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230068
  CLC： TN929.5
- Online First：25 July 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
易雅倩, 吴庆捷, 崔苗, 张广驰. 具有URLLC任务卸载的无人机辅助移动边缘计算系统的计算时延最小化研究[J]. 广东工业大学学报, 2024, 41(4): 70-79.

Yi Ya-qian, Wu Qing-jie, Cui Miao, et al. Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading[J]. 2024, 41(4): 70-79.
易雅倩, 吴庆捷, 崔苗, 张广驰. 具有URLLC任务卸载的无人机辅助移动边缘计算系统的计算时延最小化研究[J]. 广东工业大学学报, 2024, 41(4): 70-79. DOI： 10.12052/gdutxb.230068.

Yi Ya-qian, Wu Qing-jie, Cui Miao, et al. Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading[J]. 2024, 41(4): 70-79. DOI： 10.12052/gdutxb.230068.

摘要

无人机辅助移动边缘计算(Mobile Edge Computing

MEC) 系统利用无人机部署灵活和能与地面设备建立起高质量通信链路的优势，可以提高边缘计算的性能。已有的无人机辅助MEC系统研究通常假设计算任务卸载通信的数据包较长，其结果无法直接用于对计算时延具有严格要求的MEC应用场景。本文考虑了一个具有超可靠低时延通信(Ultra-Reliable and Low-Latency Communication

URLLC) 任务卸载的无人机辅助MEC系统，其中一架配备计算服务器的无人机为多个地面设备提供MEC服务，地面设备将部分计算任务通过URLLC的方式向无人机卸载。通过联合优化无人机的部署位置、卸载通信的带宽和无人机与地面设备的中央处理器(Central Processing Unit

CPU) 计算频率来最小化系统的计算时延。针对形成的非凸优化问题，采用块坐标下降方法将该问题分解为无人机部署位置优化子问题和任务卸载带宽与CPU频率优化子问题，并交替求解这两个子问题直到收敛。在求解两个子问题的过程中，使用对数函数非线性近似方法对URLLC卸载通信速率表达式进行化简，并通过连续凸逼近方法将每个子问题转换成易于求解的凸优化问题。仿真结果表明，与其他基准算法相比，所提算法能够有效平衡系统的通信能力和计算能力，从而降低系统时延。

Abstract

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can improve their edge computing performance by taking the advantage of flexible deployment of UAVs and the ability to establish high-quality communication links between UAVs and ground users. The existing works on UAV-enabled MEC systems usually assume that the blocklength of the offloading transmission is long

and their results cannot be directly applied to MEC scenarios with strict requirements on computing delay. A UAV-enabled MEC system with ultra-reliable and low-latency communication (URLLC)-based task offloading is considered

in which a UAV carrying a computing server provides MEC service for multiple ground users and the users offload parts of their computing tasks to the UAV through URLLC. The UAV's deployment location

the offloading bandwidths of the users

and the computing central processing unit (CPU) frequencies of the UAV and users are jointly optimized to minimize the computation delay of the system. To solve the resulting non-convex optimization problem

the block coordinate descent method is used to decompose the problem into two subproblems that optimize the UAV's deployment location

and the offloading bandwidths and CPU frequencies

respectively

and the two subproblems are solved alternately. In solving the two subproblems

logarithmic functions are used to make nonlinear approximation to the expression of URLLC offloading rate to simplify the subproblems

and the two subproblems are both transformed into convex optimization problems by applying the successive convex approximation method. Simulation results show that the proposed algorithm can effectively balance the communication capability and computing capability of the system and reduce the system’s computing delay compared to other benchmark schemes.

关键词

Keywords

references

HU X Y, WONG K K, ZHANG Y Y. Wireless-powered edge computing with cooperative UAV: task, time scheduling and trajectory design [J]. IEEE Transactions on Wireless Communications, 2020, 19(12): 8083-8098.

ZHAN C, HU H, SUI X F, et al . Completion time and energy optimization in t he UAV-enabled mobile-edge computing system [J ] . IEEE Internet of Things Journal, 2020, 7(8): 7808-7822.

ZHAO N, CHENG F, YU F R, et al . Caching UAV assisted secure transmission in hyper-dense networks based on interference alignment [J ] . IEEE Transactions on Communications, 2018, 66(5): 2281-2294.

WU Q J, CUI M, ZHANG G C, et al . End-to-end throughput maximization for UAV-enabled data collection systems [J ] . Journal of Guangdong University of Technology, 2022, 39(6): 53-61.

ZHANG J W, ZENG Y, ZHANG R. UAV-enabled radio access network: multi-mode communication and trajectory design [J]. IEEE Transactions on Signal Processing, 2018, 66(20): 5269-5284.

JOHANSSON N A, WANG Y P E, ERIKSSON E, et al . Radio access for ultra-reliable and low-latency 5G communications[C ] // 2015 IEEE International Conference on Communication Workshop. London: IEEE, 2015: 1184-1189.

REN H, PAN C H, DENG Y S, et al . Resource allocation for secure URLLC in mission-critical IoT scenarios [J ] . IEEE Transactions on Communications, 2020, 68(9): 5793-5807.

XU Y, ZHA NG T K, YANG D C, et al . UAV-assisted relaying and MEC networks: resource allocation and 3D deployment[C ] //2021 IEEE International Conference on Communications Workshops. Montreal: IEEE, 2021: 1-6.

LIU Y, YAN J J, ZHAO X H. Deep reinforcement learning based latency minimization for mobile edge computing with virtualization in maritime UAV communication network [J]. IEEE Transactions on Vehicular Technology, 2022, 71(4): 4225-4236.

ZHENG T, XIA W W, YAN F, et al . A joint trajectory design, offloading and resource allocation scheme in a multi-UAV-assisted MEC system[C ] //2022 IEEE 8th International Conference on Computer and Communications. Chengdu: IEEE, 2022: 521-527.

POLYANSKIY Y, POOR H V, VERDU S. Channel coding rate in the finite blocklength regime [J]. IEEE Transactions on Information Theory, 2010, 56(5): 2307-2359.

REN H, WANG K Z, PAN C H. Intelligent reflecting surface-aided URLLC in a factory automation scenario [J]. IEEE Transactions on Communications, 2022, 70(1): 707-723.

CAI Y M, JIANG X, LIU M Q, et al . Resource allocation for URLLC-oriented two-way UAV relaying [J ] . IEEE Transactions on Vehicular Technology, 2022, 71(3): 3344-3349.

EOM H, JUSTE P S, FIGUEIREDO R, et al . Machine learning-based runtime scheduler for mobile offloading framework[C ] //2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. Dresden: IEEE, 2013: 17-25.

YANG Z H, PAN C H, WANG K Z, et al . Energy efficient resource allocation in UAV-enabled mobile edge computing networks [J ] . IEEE Transactions on Wireless Communications, 2019, 18(9): 4576-4589.

BOYD S, VANDENBERGHE L. Convex optimization[M]. Cambridge: Cambridge University Press, 2004.

REN H, PAN C H, DENG Y S, et al . Joint pilot and payload power allocation for massive-MIMO-enabled URLLC IoT networks [J ] . IEEE Journal on Selected Areas in Communications, 2020, 38(5): 816-830.

3GPP. Study on scenarios and requirements for next generation access technologies: TR 38.913[S]. Sophia Antipolis: 3GPP, 2017.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

Optimized Design and Resource Allocation for Dual-server Mobile Edge Computing Systems

Optimized Design for Multiuser Cache-enabled Mobile Edge Computing

An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems

An Improved Multi-population Evolutionary Algorithm for Task Scheduling in a Mobile Edge Computing Environment

Related Author

Xu Cifu

Li Yiqing

Li Yu-long

Liang Jing-xuan

Wang Feng

Liang Jing-xuan

Wang Feng

Lin Zhi-fei

Related Institution

School of Computer Science and Technology, Guangdong University of Technology

AI问答

⁰