In this study, we introduce a multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, with UAVs as the computing server for the task offloading of ground users. The energy consumption for ground users is minimized by jointly optimizing the UAV task scheduling, bit allocation, and UAV trajectory in a unified framework. To accomplish such goal, we propose a two-layer optimization strategy, where the upper layer optimizes the UAV task scheduling based on a dynamic programming-based bidding optimization method, while the lower one solves the bit allocation and UAV trajectory. In particular, the lower layer is decoupled into several subproblems to reduce the computational complexity, which can be easily solved using an alternating direction method of multipliers. However, the UAV trajectories optimized by solving the decoupled subproblems may lead to path conflicts. As such, we further propose a re-optimization strategy to eliminate such conflicts. Experimental results demonstrate that the proposed strategy achieves a favorable performance than those of greedy and random strategies in terms of total user energy consumption, the trajectory conflicts can be eliminated effectively, and the UAV trajectory can satisfy the safety constraints.

中文翻译：

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多无人机移动边缘计算系统任务调度与动态服务策略优化

在这项研究中，我们介绍了一种支持多无人机 (multi-UAV) 的移动边缘计算 (MEC) 系统，其中无人机作为地面用户任务卸载的计算服务器。通过在统一框架内联合优化无人机任务调度、比特分配和无人机轨迹，使地面用户的能耗最小化。为了实现这一目标，我们提出了一种两层优化策略，上层基于基于动态规划的投标优化方法优化无人机任务调度，而下层解决比特分配和无人机轨迹。特别是，将下层解耦为多个子问题以降低计算复杂度，这可以使用乘法器的交替方向方法轻松解决。然而，通过解耦子问题优化的无人机轨迹可能会导致路径冲突。因此，我们进一步提出了一种重新优化策略来消除此类冲突。实验结果表明，所提出的策略在用户总能耗方面优于贪婪和随机策略，可以有效消除轨迹冲突，无人机轨迹满足安全约束。