  User localization and tracking in the upcoming generation of wireless
networks have the potential to be revolutionized by technologies such as the
Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches
rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or
require pilot transmission sequences whose length is comparable to the number
of DMA elements, thus, leading to limited effectiveness and considerable
measurement overheads in blocked LoS and dynamic multipath environments. In
this paper, we present a two-stage machine-learning-based approach for user
tracking, specifically designed for non-LoS multipath settings. A newly
proposed attention-based Neural Network (NN) is first trained to map noisy
channel responses to potential user positions, regardless of user mobility
patterns. This architecture constitutes a modification of the prominent vision
transformer, specifically modified for extracting information from
high-dimensional frequency response signals. As a second stage, the NN's
predictions for the past user positions are passed through a learnable
autoregressive model to exploit the time-correlated channel information and
obtain the final position predictions. The channel estimation procedure
leverages a DMA receive architecture with partially-connected radio frequency
chains, which results to reduced numbers of pilots. The numerical evaluation
over an outdoor ray-tracing scenario illustrates that despite LoS blockage,
this methodology is capable of achieving high position accuracy across various
multipath settings.
