In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computation-intensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI.

翻译：本文构建了一个由高空平台（HAP）和无人机（UAV）组成的分层空中计算框架，用于处理通过上行链路非正交多址接入（UL-NOMA）连接的地面移动用户的完全卸载任务。为了更好地评估计算密集型应用中的信息新鲜度，本文考虑了信息年龄（AoI）准则。具体而言，问题被建模为通过调整无人机轨迹以及无人机和HAP上的资源分配，在信道状态信息（CSI）不确定性和多资源约束下，最小化具有弹性任务的用户的平均AoI。为求解这一非凸优化问题，提出了基于多智能体深度确定性策略梯度（MADDPG）和联邦强化学习（FRL）的两种方法，用于设计无人机轨迹并获取信道、功率和CPU分配。结果表明，任务调度显著降低了平均AoI，且对于较大任务规模这一改善更为明显。一方面，与对所有用户使用全发射功率相比，功率分配对平均AoI的影响微乎其微。与传统的传输方案相比，仿真结果表明我们的调度方案在平均AoI方面实现了显著改进。