  User equipment (UE) positioning accuracy is of paramount importance in
current and future communications standard. However, traditional methods tend
to perform poorly in non line of sight (NLoS) scenarios. As a result, deep
learning is a candidate to enhance the UE positioning accuracy in NLoS
environments. In this paper, we study the efficiency of deep learning on the
3GPP indoor factory (InF) statistical channel. More specifically, we analyse
the impacts of several key elements on the positioning accuracy: the type of
radio data used, the number of base stations (BS), the size of the training
dataset, and the generalization ability of a trained model.
