UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations
Abstract
Because of disaster, terrestrial base stations (TBS) would be partly crashed. Some user equipments (UE) would be unserved. Deploying unmanned aerial vehicles (UAV) as aerial base stations is a method to cover UEs quickly. But existing methods solely refer to the coverage of UAVs. In those scenarios, they focus on the deployment of UAVs in the post-disaster area where all TBSs do not work any longer. There is limited research about the combination of available TBSs and UAVs. We propose the method to deploy UAVs cooperating with available TBSs as aerial base stations. And improve the coverage by reinforcement learning. Besides, in the experiments, we cluster UEs with balanced iterative reducing and clustering using hierarchies (BIRCH) at first. Finally, achieve base stations' better coverage to UEs through Q-learning.
Cite
@article{arxiv.2202.10338,
title = {UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations},
author = {Shiye Zhao and Kaoru Ota and Mianxiong Dong},
journal= {arXiv preprint arXiv:2202.10338},
year = {2022}
}