Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such an ad hoc network. Such a physical model is quick and easy to deploy and the underlying air-to-ground (ATG) propagation models are both resilient and scalable and may use a wide range of link budget, grade of service (GoS), and quality of service (QoS) parameters to optimise their performance and in turn the effectiveness of the physical model. The prediction results of a simulated deployment of such a physical model and the evolved propagation model in an ad hoc network offers much promise in restoring communication links during emergency relief operations.

中文翻译：

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部署用于灾后快速恢复通信链路的空中平台系统：一种机器学习方法

在灾难性事件发生后立即拥有可靠的电信系统对地方当局、地方消防和警察部门以及救援队拯救生命的共同努力产生巨大影响。本文提出了一种物理模型，该模型将仍在运行的基站与空中平台连接起来，然后使用机器学习框架为这种自组织网络演化出地对空传播模型。这种物理模型快速且易于部署，并且底层的空对地 (ATG) 传播模型具有弹性和可扩展性，并且可以使用广泛的链路预算、服务等级 (GoS) 和服务质量（ QoS) 参数来优化它们的性能，进而优化物理模型的有效性。