Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (04): 70-79.doi: 10.12052/gdutxb.230068

• Information and Communication Engineering • Previous Articles     Next Articles

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   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-05-17 Online:2024-07-25 Published:2024-05-25

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.

Key words: unmanned aerial vehicles, mobile edge computing, ultra reliable and low latency communication, computation delay

CLC Number: 

  • TN929.5
[1] 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.
[2] ZHAN C, HU H, SUI X F, et al. Completion time and energy optimization in the UAV-enabled mobile-edge computing system [J]. IEEE Internet of Things Journal, 2020, 7(8): 7808-7822.
[3] 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.
[4] 吴庆捷, 崔苗, 张广驰, 等. 无人机信息采集系统的端到端吞吐量最大化研究[J]. 广东工业大学学报, 2022, 39(6): 53-61.
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.
[5] 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.
[6] 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.
[7] 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.
[8] XU Y, ZHANG 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] BOYD S, VANDENBERGHE L. Convex optimization[M]. Cambridge: Cambridge University Press, 2004.
[17] 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.
[18] 3GPP. Study on scenarios and requirements for next generation access technologies: TR 38.913[S]. Sophia Antipolis: 3GPP, 2017.
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