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基于非正交多址的多无人机协同计算与任务卸载策略OACSTPCD

Multi-UAV Collaborative Computing and Task Offloading Strategy Based on Non-orthogonal Multiple Access

中文摘要英文摘要

人群密集的应用场景下存在计算资源需求量大、无人机计算任务分配不均等计算需求问题.针对该类场景,论文提出一种基于非正交多址的多无人机辅助的移动边缘计算任务卸载方案.首先,利用非正交多址和串行干扰删除技术提升用户的传输速率,并利用多无人机的相互协作防止任务分配不均.在符合用户设备能耗、计算资源的前提下,通过联合优化无人机部署位置和卸载策略,构建一个使系统能耗最小化的优化问题.并将该优化问题分解为两个子问题,利用深度强化学习中的DDQN网络求出用户的卸载决策,利用差分进化算法确定此卸载决策下的无人机部署,然后交替迭代两种方法得到问题的优化解.仿真结果表明相较于时分多址技术,非正交多址有效地降低了用户任务的传输时延.相较于DQN网络、贪婪算法,论文所提卸载决策算法有效降低了系统总能耗.

In crowded application scenarios,there is a large demand for computing resources and unequal distribution of un-manned aerial vehicle computing tasks.For this kind of scenario,this paper presents a multi-UAV-assisted mobile edge computing task uninstallation scheme based on non-orthogonal multiple access.First,the non-orthogonal multiple access and serial interfer-ence deletion technologies are used to increase the transmission rate of users,and the cooperation of multiple UAVs is used to pre-vent uneven task distribution.Under the constraints of energy consumption and computing resources of user equipment,a non-con-vex optimization problem,the minimization of system energy consumption,is established through joint optimization of user uninstal-lation decision and unmanned deployment location.Subsequently,the optimization issue is divided into two subsections,the DDQN network is used to determine the user's uninstallation decision,the differential evolution algorithm is used to determine the un-manned aerial vehicle deployment under the uninstallation decision,and the two methods are iterated alternately to obtain the opti-mal solution of the problem.The simulation results show that non-orthogonal multiple access effectively reduces the transmission de-lay of user tasks compared with time-division multiple access technology.Compared with DQN network and greedy algorithm,the offload decision algorithm proposed in this paper reduces the system's total energy consumption.

夏景明;王亮

南京信息工程大学人工智能学院 南京 210044

电子信息工程

移动边缘计算无人机深度强化学习非正交多址差分进化

mobile edge computingunmanned aerial vehicledeep reinforcement learningnon-orthogonal multiple ac-cessdifferential evolution

《计算机与数字工程》 2024 (005)

1298-1303 / 6

国家重点研发计划(编号:2021YFB2901900)资助.

10.3969/j.issn.1672-9722.2024.05.007

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