Unmanned Aerial Vehicles (UAVs) can be used to provide wireless connectivity to support the existing infrastructure in hot-spots or replace it in cases of destruction. UAV-enabled wireless provides several advantages in network performance due to drone small cells (DSCs) mobility despite the limited onboard energy. However, the problem of resource allocation has added complexity. In this paper, we propose an energy-efficient user clustering mechanism based on Gaussian mixture models (GMM) using a modified Expected-Maximization (EM) algorithm. The algorithm is intended to provide the initial user clustering and drone deployment upon which additional mechanisms can be employed to further enhance the system performance. The proposed algorithm improves the energy efficiency of the system by 25% and link reliability by 18.3% compared to other baseline methods.

翻译：无人机可为热点区域提供无线连接以增强现有基础设施，或在基础设施损毁时代替其功能。尽管机载能量有限，但无人机小型基站的移动性使无人机无线网络在性能上具有多重优势，然而资源分配问题的复杂性也随之增加。本文提出一种基于高斯混合模型的能效用户分簇机制，采用改进的期望最大化算法。该算法旨在提供初始用户分簇与无人机部署方案，在此基础上可结合其他机制进一步提升系统性能。相较于其他基准方法，所提算法使系统能效提升25%，链路可靠性提高18.3%。