  Fifth Generation (5G) cellular networks are expected to provide cellular
connectivity for vehicular users, including Unmanned Aerial Vehicles (UAVs).
When flying in the air, these users experience strong, unobstructed channel
conditions to a large number of Base Stations (BSs) on the ground. This creates
very strong interference conditions for the UAV users, while at the same time
offering them a large number of BSs to potentially associate with for cellular
service. Therefore, to maximise the performance of the UAV-BS wireless link,
the UAV user needs to be able to choose which BSs to connect to, based on the
observed environmental conditions. This paper proposes a supervised
learning-based association scheme, using which a UAV can intelligently
associate with the most appropriate BS. We train a Neural Network (NN) to
identify the most suitable BS from several candidate BSs, based on the received
signal powers from the BSs, known distances to the BSs, as well as the known
locations of potential interferers. We then compare the performance of the
NN-based association scheme against strongest-signal and closest-neighbour
association schemes, and demonstrate that the NN scheme significantly
outperforms the simple heuristic schemes.
