在本文中，我們的目標是在高密度網絡中開發一個支持無人機的 MEC 聚類網路。 我們應用 K-mean聚類算法妥善管理在地面上且擁擠的user equipment (UE) 的計算任務。 我們的重點在於，只需部署一架無人機來卸載地面網絡的計算任務。 為了使用最小功率並在有限時間內完成所有UE的計算任務，每個任務可以由UE自己在本地執行，或者以其他方式轉移到其關聯的無人機上。
基於這個場景，我們將開發聚類 clustering UAVBS enabled (CUBE) MEC 算法來解決這個問題，我們有以下子問題，即（1）集群的數量； (2) UE和無人機之間的計算關聯； (3) 傳輸數據的傳輸功率； (4)無人機的飛行路線。 基於仿真結果，所提出的算法可以有效的維持高密度網路中UAV-enabled MEC網絡的運行。

In this article, we studied clusters and unmanned aerial vehicle (UAV) trajectories, using enabled Mobile Edge Computing (MEC) networks to complete computing tasks with minimal energy consumption. In order to achieve this goal, energy efficient unmanned aerial vehicle assisted mobile edge computing (EUA-MEC) scheme has been developed to solve three sub-problems: (1) the computing associations between the UE and the UAV; (2) the transmission power of the transmitted data; (3) The flight path of the UAV.
The simulation results verify that the proposed scheme can effectively reduce system energy consumption and improve the operating capability of the UAV-MEC network.

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