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A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network by Peng, Haoran; Chen, Chao; Lai, Chuan-Chi; Wang, Li-Chun; Han, Zhu is a scholarly article available to read on EtoBox.

What is A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network about?

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

Author

Peng, Haoran; Chen, Chao; Lai, Chuan-Chi; Wang, Li-Chun; Han, Zhu

Published

2019

Language

EN