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User localization and tracking in the upcoming generation of wirelessnetworks have the potential to be revolutionized by technologies such as theDynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approachesrely on assumptions about relatively dominant Line-of-Sight (LoS) paths, orrequire pilot transmission sequences whose length is comparable to the numberof DMA elements, thus, leading to limited effectiveness and considerablemeasurement overheads in blocked LoS and dynamic multipath environments. Inthis paper, we present a two-stage machine-learning-based approach for usertracking, specifically designed for non-LoS multipath settings. A newlyproposed attention-based Neural Network (NN) is first trained to map noisychannel responses to potential user positions, regardless of user mobilitypatterns. This architecture constitutes a modification of the prominent visiontransformer, specifically modified for extracting information fromhigh-dimensional frequency response signals. As a second stage, the NN'spredictions for the past user positions are passed through a learnableautoregressive model to exploit the time-correlated channel information andobtain the final position predictions. The channel estimation procedureleverages a DMA receive architecture with partially-connected radio frequencychains, which results to reduced numbers of pilots. The numerical evaluationover an outdoor ray-tracing scenario illustrates that despite LoS blockage,this methodology is capable of achieving high position accuracy across variousmultipath settings.

Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas