  Deep learning (DL) methods have been recently proposed for user equipment
(UE) localization in wireless communication networks, based on the channel
state information (CSI) between a UE and each base station (BS) in the uplink.
With the CSI from the available BSs, UE localization can be performed in
different ways. One the one hand, a single neural network (NN) can be trained
for the UE localization by considering the CSI from all the available BSs as
one overall fingerprint of the user's location. On the other hand, the CSI at
each BS can be used to obtain an estimate of the UE's position with a separate
NN at each BS, and then the position estimates of all BSs are combined to
obtain an overall estimate of the UE position. In this work, we show that UE
localization with the latter approach can achieve a higher positioning
accuracy. We propose to consider the uncertainty in the UE localization at each
BS, such that overall UE's position is determined by combining the position
estimates of the different BSs based on the uncertainty at each BS. With this
approach, a more reliable position estimate can be obtained in case of
variations in the channel.
