Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to meet the computation demand and the stringent delay requirement. In this work, we investigate a multiple UAVs-assisted two-stage MEC system in which the computation-intensive and delay-sensitive tasks of mobile devices (MDs) are cooperatively executed on both MEC-enabled UAVs and terrestrial base station (TBS) attached with the MEC server. Specifically, UAVs provide the computing and relaying services to the mobile devices. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited communication resources for the uplink transmission, the computation resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. Thus, we relax the channel assignment variable from the binary to continuous values. However, the problem is still non-convex due to the coupling among the variables. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. In essence, the non-convex objective function is decomposed into multiple subproblems which are then solved in a block-by-block manner. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.
翻译:无人驾驶航空飞行器(UAV)协助的多接入边缘计算(MEC)已成为一个有希望的解决方案,用于满足计算需求和严格的延迟要求。在这项工作中,我们调查一个多个无人驾驶飞行器协助的两阶段MEC系统,在这个系统中,移动设备(MDs)的计算密集和延迟敏感任务在与MEC服务器相连的MEC驱动的无人驾驶飞行器和地面基地站(TBS)上都以合作方式执行。具体地说,无人驾驶飞行器为移动设备提供计算和中继服务。在这方面,我们制定了一个联合任务卸载、通信和计算资源分配问题,以尽量减少MDVs和UAVs的能源消耗,为此,我们考虑了升级传输传输的有限通信资源、UAVs的计算资源和任务的可耐受力。形成的问题是一个混杂的内分解非内置器问题。因此,我们将频道分配变量从二进到连续的值框架。然而,我们的问题仍然是非相互交替的,原因是将超级目标的分级性功能应用为最低级。