University of Oulu

Q. Zhang, M. Mozaffari, W. Saad, M. Bennis and M. Debbah, "Machine Learning for Predictive On-Demand Deployment of Uavs for Wireless Communications," 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6. doi: 10.1109/GLOCOM.2018.8647209

Machine learning for predictive on-demand deployment of UAVs for wireless communications

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Author: Zhang, Qianqian1; Mozaffari, Mohammad2; Saad, Walid1;
Organizations: 1Bradley Department of Electrical and Computer Engineering, Virginia Tech, VA, USA
2Center for Wireless Communications, University of Oulu, Finland
3Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France
4Large Systems and Networks Group, CentraleSupélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-06-10


In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular users. In order to have a comprehensive analysis of cellular traffic, an ML framework based on a Gaussian mixture model and a weighted expectation maximization algorithm is introduced to predict the potential network congestion. Then, the optimal deployment of UAVs is studied with the objective of minimizing the power needed for UAV transmission and mobility, given the predicted traffic. To this end, first, the optimal partition of service areas of each UAV is derived, based on a fairness principle. Next, the optimal location of each UAV that minimizes the total power consumption is derived. Simulation results show that the proposed ML approach can reduce power needed for downlink transmission and mobility by over 20% and 80%, respectively, compared with an optimal deployment of UAVs with no ML prediction.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-5386-4727-1
ISBN Print: 978-1-5386-4728-8
Article number: 8647209
DOI: 10.1109/GLOCOM.2018.8647209
Host publication: 2018 IEEE Global Communications Conference, GLOBECOM 2018
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the U.S. National Science Foundation under Grant IIS-1633363, by the Academy of Finland project CARMA, and 6Genesis Flagship (grant no. 318927), by the INFOTECH project NOOR, and in part by the Kvantum Institute strategic project SAFARI.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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