  Deep learning (DL) methods have been shown to improve the performance of
several use cases for the fifth-generation (5G) New radio (NR) air interface.
In this paper we investigate user equipment (UE) positioning using the channel
state information (CSI) fingerprints between a UE and multiple base stations
(BSs). In such a setup, a single DL model can be trained for UE positioning
using the CSI fingerprints of the multiple BSs as input. Alternatively, based
on the CSI at each BS, a separate DL model can be trained at each BS and then
the output of the different models are combined to determine the UE's position.
In this work we compare these different fusion techniques and show that fusing
the output of separate models achieves higher positioning accuracy, especially
in a dynamic scenario. We also show that the fusion of multiple outputs further
benefits from considering the uncertainty of the output of the DL model at each
BS. For a more efficient training of the DL model across BSs, we additionally
propose a multi-task learning (MTL) scheme by sharing some parameters across
the models while jointly training all models. This method, not only improves
the accuracy of the individual models, but also of the final combined estimate.
Lastly, we evaluate the reliability of the uncertainty estimation to ascertain
which of the fusion methods provides the highest quality of uncertainty
estimates.
