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：多接入边缘计算（MEC）被视为第六代通信中的一项有前景技术。然而，当无人机（UAV）作为MEC平台时，天线增益常受环境影响，导致意外信道误差。为应对该问题并降低基于UAV的MEC中的功耗，我们联合优化了分层UAV-MEC中的接入方案与功率分配。具体而言，低层部署无人机收集地面用户数据，上层部署具有强大计算能力的无人机辅助计算。目标是保障服务质量并最小化总功耗。我们考虑实际环境中多种扰动引起的误差，构建了含不确定集的分布鲁棒机会约束优化问题。该机会约束问题难以直接求解。为此，我们利用条件风险价值方法将问题重构为半定规划形式，进而设计了接入方案与功率分配的联合算法。最后通过仿真验证了所提算法的有效性。