  To address the ever-growing connectivity demands of wireless communications,
the adoption of ingenious solutions, such as Unmanned Aerial Vehicles (UAVs) as
mobile Base Stations (BSs), is imperative. In general, the location of a UAV
Base Station (UAV-BS) is determined by optimization algorithms, which have high
computationally complexities and place heavy demands on UAV resources. In this
paper, we show that a Convolutional Neural Network (CNN) model can be trained
to infer the location of a UAV-BS in real time. In so doing, we create a
framework to determine the UAV locations that considers the deployment of
Mobile Users (MUs) to generate labels by using the data obtained from an
optimization algorithm. Performance evaluations reveal that once the CNN model
is trained with the given labels and locations of MUs, the proposed approach is
capable of approximating the results given by the adopted optimization
algorithm with high fidelity, outperforming Reinforcement Learning (RL)-based
approaches. We also explore future research challenges and highlight key
issues.
