Multi-access edge computing (MEC) is regarded as a promising technology in the sixth-generation communication. However, the antenna gain is always affected by the environment when unmanned aerial vehicles (UAVs) are served as MEC platforms, resulting in unexpected channel errors. In order to deal with the problem and reduce the power consumption in the UAV-based MEC, we jointly optimize the access scheme and power allocation in the hierarchical UAV-based MEC. Specifically, UAVs are deployed in the lower layer to collect data from ground users. Moreover, a UAV with powerful computation ability is deployed in the upper layer to assist with computing. The goal is to guarantee the quality of service and minimize the total power consumption. We consider the errors caused by various perturbations in realistic circumstances and formulate a distributionally robust chance-constrained optimization problem with an uncertainty set. The problem with chance constraints is intractable. To tackle this issue, we utilize the conditional value-at-risk method to reformulate the problem into a semidefinite programming form. Then, a joint algorithm for access scheme and power allocation is designed. Finally, we conduct simulations to demonstrate the efficiency of the proposed algorithm.

翻译：多存取边缘计算(MEC)被认为是第六代通信中的一种有希望的技术,然而,当无人驾驶飞行器(UAVs)被用作MEC平台时,天线增益总是受到环境的影响,造成意外的频道错误。为了解决这个问题并减少以UAV为基础的MEC的电力消耗量,我们共同优化了以UAV为基础的等级为基础的MEC的接入计划和电力分配。具体地说,在低层部署无人驾驶飞行器,以便从地面用户收集数据。此外,在上层部署一个具有强大计算能力的无人驾驶飞行器,以协助计算。目标是保证服务质量并尽量减少总电能消耗量。我们考虑了在现实环境中各种干扰造成的错误,并制定了具有不确定性的、具有分配性强的、受机会限制的优化问题。为解决这一问题,我们使用有条件的值风险方法将问题改写成一个半限定的编程表。然后,在上层部署一个具有强大计算能力的无人驾驶飞行器,以协助计算。最后,我们进行了模拟,以展示拟议算法的效率。</s>