UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning
UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning
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DOI：
10.1109/ojcoms.2021.3067001
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发表时间：
2021-04
影响因子：
7.9
通讯作者：
Alireza Shamsoshoara;F. Afghah;Erik Blasch;J. Ashdown;M. Bennis
中科院分区：
文献类型：
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作者：
Alireza Shamsoshoara;F. Afghah;Erik Blasch;J. Ashdown;M. Bennis
在自然和人为灾害期间对蜂窝塔的损坏可能会干扰蜂窝用户的通信服务。该问题的一个解决方案是使用无人驾驶飞行器来增强所需的通信网络。本文介绍了一种无人机辅助模仿学习（UnVAIL）通信系统的设计，该系统将蜂窝用户的信息中继到相邻基站。由于用户设备（UE）配备有容量有限的缓冲器来保存分组，因此UnVAIL在不同UE之间交替以减少缓冲器溢出的机会，将其自身最佳地定位在所选择的UE附近以减少服务时间，并且通过充当中继节点来揭开网络路径。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调度解决方案。结果表明，UnVAIL执行类似于人类专家的知识为基础的规划，在通信的及时性，位置精度，和能源消耗的准确性为97.52%时，在开发的模拟器上进行评估，以训练无人机。
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.