  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 (GMM) and a weighted
expectation maximization (WEM) algorithm is introduced to predict the potential
network congestion. Then, the optimal deployment of UAVs is studied to minimize
the transmit power needed to satisfy the communication demand of users in the
downlink, while also minimizing the power needed for UAV mobility, based on the
predicted cellular 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 the
required downlink transmit power and improve the power efficiency by over 20%,
compared with an optimal deployment of UAVs with no ML prediction.
