The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users’ information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

中文翻译：

---

模仿学习的无人机在偏远灾害地区的通信

在自然和人为灾难期间对蜂窝塔的损坏可能会干扰蜂窝用户的通信服务。解决该问题的一种方法是使用无人飞行器来增加所需的通信网络。本文演示了一种将无人机用户的信息中继到邻居基站的UAV辅助模仿学习（UnVAIL）通信系统的设计。由于用户设备（UE）配备了容量有限的缓冲区来容纳数据包，因此UnVAIL在不同的UE之间进行切换以减少缓冲区溢出的机会，将自身最佳地定位在所选UE附近以减少服务时间，并通过以下方式发现网络路径充当中继节点。UnVAIL利用模仿学习（IL）作为数据驱动的行为克隆方法来实现最佳计划解决方案。