广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 70-79.doi: 10.12052/gdutxb.230068
易雅倩, 吴庆捷, 崔苗, 张广驰
Yi Ya-qian, Wu Qing-jie, Cui Miao, Zhang Guang-chi
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。
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[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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