An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

中文翻译：

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基于多智能体深度强化学习的多无人机辅助移动边缘计算轨迹规划

提出了一种无人机（UAV）辅助移动边缘计算（MEC）框架，其中多架具有不同轨迹的无人机飞越目标区域并支持地面上的用户设备（UE）。我们旨在共同优化所有 UE 之间的地理公平性、每个 UAV 的 UE 负载的公平性以及 UE 的整体能耗。上述优化问题包括整数和连续变量，解决起来具有挑战性。为了解决上述问题，提出了一种基于多智能体深度强化学习的轨迹控制算法，用于独立管理每个无人机的轨迹，其中应用了流行的多智能体深度确定性策略梯度（MADDPG）方法。鉴于无人机的轨迹，引入了一种低复杂度的方法来优化 UE 的卸载决策。我们表明，我们提出的解决方案在服务 UE 的公平性、每个 UAV 上 UE 负载的公平性和所有 UE 的能耗方面都比其他传统算法具有相当大的性能。