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Deep learning (DL) methods have been recently proposed for user equipment(UE) localization in wireless communication networks, based on the channelstate 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 indifferent ways. One the one hand, a single neural network (NN) can be trainedfor the UE localization by considering the CSI from all the available BSs asone overall fingerprint of the user's location. On the other hand, the CSI ateach BS can be used to obtain an estimate of the UE's position with a separateNN at each BS, and then the position estimates of all BSs are combined toobtain an overall estimate of the UE position. In this work, we show that UElocalization with the latter approach can achieve a higher positioningaccuracy. We propose to consider the uncertainty in the UE localization at eachBS, such that overall UE's position is determined by combining the positionestimates of the different BSs based on the uncertainty at each BS. With thisapproach, a more reliable position estimate can be obtained in case ofvariations in the channel.

Reliable Deep Learning based Localization with CSI Fingerprints and Multiple Base Stations