  One strategy to obtain user location information in a wireless network
operating at millimeter wave (mmWave) is based on the exploitation of the
geometric relationships between the channel parameters and the user position.
These relationships can be easily built from the LoS path and/or first order
reflections, but high resolution channel estimates are required for high
accuracy. In this paper, we consider a mmWave MIMO system based on a hybrid
architecture, and develop first a low complexity channel estimation strategy
based on MOMP suitable for high dimensional channels, as those associated to
operating with large planar arrays. Then, a deep neural network (DNN) called
PathNet is designed to classify the order of the estimated channel paths, so
that only the line-of-sight (LOS) path and first order reflections are selected
for localization purposes. Next, a 3D localization strategy exploiting the
geometry of the environment is developed to operate in both LOS and
non-line-of-sight (NLOS) conditions, while considering the unknown clock offset
between the transmitter (TX) and the receiver (RX). Finally, a Transformer
based network exploiting attention mechanisms called ChanFormer is proposed to
refine the initial position estimate obtained from the geometric system of
equations that connects user position and channel parameters. Simulation
results obtained with realistic vehicular channels generated by ray tracing
indicate that sub-meter accuracy (<= 0.45 m) can be achieved for 95% of the
users in LOS channels, and for 50% of the users in NLOS conditions.
