  This paper explores a novel Neural Network (NN) architecture suitable for
Beamformed Fingerprint (BFF) localization in a millimeter-wave (mmWave)
multiple-input multiple-output (MIMO) outdoor system. The mmWave frequency
bands have attracted significant attention due to their precise timing
measurements, making them appealing for applications demanding accurate device
localization and trajectory estimation. The proposed NN architecture captures
BFF sequences originating from various user paths, and through the application
of learning mechanisms, subsequently estimates these trajectories.
Specifically, we propose a method for trajectory estimation, employing a
transformer network (TN) that relies on attention mechanisms. This TN-based
approach estimates wireless device trajectories using BFF sequences recorded
within a mmWave MIMO outdoor system. To validate the efficacy of our proposed
approach, numerical experiments are conducted using a comprehensive dataset of
radio measurements in an outdoor setting, complemented with ray tracing to
simulate wireless signal propagation at 28 GHz. The results illustrate that the
TN-based trajectory estimator outperforms other methods from the existing
literature and possesses the ability to generalize effectively to new
trajectories outside the training dataset.
