Currently, Unmanned Aerial Vehicles (UAVs)
are gaining signifcant attention due to their potential to
effectively carry out a variety of tasks with superior performance through the use of ffth-generation (5G) and sixthgeneration (6G) networks. Non-orthogonal multiple access
(NOMA) techniques can further improve the performance
and effciency while reducing the interference. In this paper, we propose the application of machine learning (ML)
techniques to evaluate the outage performance of a NOMAenabled UAV network. Specifcally, this study investigates the
optimal UAV height that allows two users on the ground to
receive the best service when they are simultaneously served
by one UAV. We generated our own dataset which included
several network parameters. We then trained various machine
learning techniques on this dataset, including artifcial neural
networks (ANN), support vector regression (SVR), and linear
regression (LR). Our results indicate that ANN provides the
best accuracy compared with SVR and LR, with an average
root mean squared error (RMSE) of 0.0931.
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