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One strategy to obtain user location information in a wireless networkoperating at millimeter wave (mmWave) is based on the exploitation of thegeometric relationships between the channel parameters and the user position.These relationships can be easily built from the LoS path and/or first orderreflections, but high resolution channel estimates are required for highaccuracy. In this paper, we consider a mmWave MIMO system based on a hybridarchitecture, and develop first a low complexity channel estimation strategybased on MOMP suitable for high dimensional channels, as those associated tooperating with large planar arrays. Then, a deep neural network (DNN) calledPathNet is designed to classify the order of the estimated channel paths, sothat only the line-of-sight (LOS) path and first order reflections are selectedfor localization purposes. Next, a 3D localization strategy exploiting thegeometry of the environment is developed to operate in both LOS andnon-line-of-sight (NLOS) conditions, while considering the unknown clock offsetbetween the transmitter (TX) and the receiver (RX). Finally, a Transformerbased network exploiting attention mechanisms called ChanFormer is proposed torefine the initial position estimate obtained from the geometric system ofequations that connects user position and channel parameters. Simulationresults obtained with realistic vehicular channels generated by ray tracingindicate that sub-meter accuracy (<= 0.45 m) can be achieved for 95% of theusers in LOS channels, and for 50% of the users in NLOS conditions.

Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location