TY - GEN
T1 - A predictive on-demand placement of UAV base stations using echo state network
AU - Peng, Haoran
AU - Chen, Chao
AU - Lai, Chuan Chi
AU - Wang, Li Chun
AU - Han, Zhu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Base Station
KW - Echo State Network
KW - Kuhn-Munkres Algorithm
KW - Prediction
KW - Unmanned Aerial Vehicle
UR - http://www.scopus.com/inward/record.url?scp=85074063554&partnerID=8YFLogxK
U2 - 10.1109/ICCChina.2019.8855868
DO - 10.1109/ICCChina.2019.8855868
M3 - Conference contribution
AN - SCOPUS:85074063554
T3 - 2019 IEEE/CIC International Conference on Communications in China, ICCC 2019
SP - 36
EP - 41
BT - 2019 IEEE/CIC International Conference on Communications in China, ICCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/CIC International Conference on Communications in China, ICCC 2019
Y2 - 11 August 2019 through 13 August 2019
ER -