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User equipment (UE) positioning accuracy is of paramount importance incurrent and future communications standard. However, traditional methods tendto perform poorly in non line of sight (NLoS) scenarios. As a result, deeplearning is a candidate to enhance the UE positioning accuracy in NLoSenvironments. In this paper, we study the efficiency of deep learning on the3GPP indoor factory (InF) statistical channel. More specifically, we analysethe impacts of several key elements on the positioning accuracy: the type ofradio data used, the number of base stations (BS), the size of the trainingdataset, and the generalization ability of a trained model.

Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning