In this paper, we consider deploying multiple Unmanned Aerial Vehicles (UAVs) to enhance the computation service of Mobile Edge Computing (MEC) through collaborative computation among UAVs. In particular, the tasks of different types and service requirements in MEC network are offloaded from one UAV to another. To pursue the goal of low-carbon edge computing, we study the problem of minimizing system energy consumption by jointly optimizing computation resource allocation, task scheduling, service placement, and UAV trajectories. Considering the inherent unpredictability associated with task generation and the dynamic nature of wireless fading channels, addressing this problem presents a significant challenge. To overcome this issue, we reformulate the complicated non-convex problem as a Markov decision process and propose a soft actor-critic-based trajectory optimization and resource allocation algorithm to implement a flexible learning strategy. Numerical results illustrate that within a multi-UAV-enabled MEC network, the proposed algorithm effectively reduces the system energy consumption in heterogeneous tasks and services scenarios compared to other baseline solutions.

翻译：本文考虑部署多架无人机，通过无人机间的协同计算来增强移动边缘计算的计算服务能力。具体而言，移动边缘计算网络中不同类型及服务需求的任务可在无人机间进行卸载。为实现低碳边缘计算的目标，我们研究了通过联合优化计算资源分配、任务调度、服务部署和无人机轨迹以最小化系统能耗的问题。考虑到任务生成固有的不可预测性以及无线衰落信道的动态特性，解决该问题面临重大挑战。为克服此困难，我们将复杂的非凸问题重新表述为马尔可夫决策过程，并提出一种基于软演员-评论家的轨迹优化与资源分配算法，以实现灵活的学习策略。数值结果表明，在基于多无人机的移动边缘计算网络中，与其他基线方案相比，所提算法在异构任务和服务场景下能有效降低系统能耗。