Machine Learning for Predictive On-Demand Deployment of UAVs for Wireless Communications

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.

Zhang Qianqian, Mozaffari Mohammad, Saad Walid, Bennis Mehdi, Debbah Mérouane

A4 Article in conference proceedings

2018 IEEE Global Communications Conference, GLOBECOM 2018

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