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Joint Resource Allocation and Trajectory Design for UAV-assisted Mobile Edge Computing Systems

机译:无人机辅助移动边缘计算系统的联合资源分配和轨迹设计

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Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is an appealing concept, where a fixed-wing UAV equipped with computing resources is used to help local resource-limited user devices (UDs) compute their tasks. In this paper, each UD has separable computing tasks to complete, which can be divided into two parts: one portion is processed locally and the other part is offloaded to the UAV. The UAV moves around above UDs and provides computing service in an orthogonal frequency division multiple access (OFDMA) manner. This paper aims to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing resource allocation and UAV trajectory. The resulted optimization problem is nonconvex and challenging to solve directly. With that in mind, we develop an iterative algorithm for solving this problem based on the block coordinate descent method, which iteratively optimizes resource allocation variables and UAV trajectory variables till convergence. Simulation results show significant energy saving of our proposed solution compared to the benchmarks.
机译:无人驾驶飞行器(UAV) - 分配移动边缘计算(MEC)系统是一种吸引人的概念,其中配备有计算资源的固定翼UAV用于帮助本地资源限制的用户设备(UDS)计算其任务。在本文中,每个UD具有可分离的计算任务来完成,可以分为两部分:一个部分在本地处理,另一部分卸载到UAV。 UAV在UDS上方移动,并以正交频分多址(OFDMA)方式提供计算服务。本文旨在通过共同优化资源分配和UAV轨迹,尽量减少UAV和UDS的加权和能量消耗。由此产生的优化问题是直接解决和挑战性的。考虑到这一点,我们开发一种迭代算法,用于基于块坐标序列方法解决这个问题,它迭代地优化资源分配变量和UAV轨迹变量直到收敛。与基准相比,仿真结果显示了我们提出的解决方案的显着节能。

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