Since the traditional mobile edge computing (MEC) server is fixed at the edge of the network, it is likely to cause severe propagation loss between the edge server and users. However, ground users can be served when the MEC server is integrated into the unmanned aerial vehicle (UAV) base station (BS). Nevertheless, there are three main factors, including the limited system radio resources, the finite UAV energy and severe interference between the uplink and downlink, that make it challenging for the system to guarantee quality-of-experience of users in the downlink while ensuring the minimum amount of offloading data for users in the uplink. Therefore, this paper investigates computation-efficiency maximization. We jointly optimize computing scheduling, UAV 3D trajectory, bandwidth allocation and transmission power control to maximize the amount of offloaded data, minimize UAV energy consumption, and simultaneously guarantee the QoE of users in the downlink. Due to the nonconvex and nonconcave objective function and the coupling between variables, a multi-stage alternative optimization algorithm is proposed to solve the problem. The simulation results have demonstrated that the computation-efficiency obtained by the proposed scheme is higher than that of other benchmark schemes and meets the QoE demands of users under insufficient resources.

中文翻译：

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无人机支持的 ​​MEC 通信系统中的计算效率最大化和 QoE 供应

由于传统 移动边缘计算（MEC）服务器固定在网络边缘，容易造成边缘服务器与用户之间严重的传播损失。但是，当 MEC 服务器集成到无人驾驶的航空机 (无人机) 基站（BS）。然而，由于系统无线电资源有限、无人机能量有限、上下行干扰严重等三个主要因素，使得系统在保证下行用户体验质量的同时保证下行链路的用户体验质量具有挑战性。上行用户的最小分流数据量。因此，本文研究了计算效率最大化。我们联合优化计算调度、无人机3D轨迹、带宽分配和传输功率控制，最大化卸载数据量，最小化无人机能耗，同时保证下行用户的QoE。由于目标函数的非凸非凹以及变量之间的耦合性，提出了一种多阶段交替优化算法来解决该问题。