  The integration of high-precision cellular localization and machine learning
(ML) is considered a cornerstone technique in future cellular navigation
systems, offering unparalleled accuracy and functionality. This study focuses
on localization based on uplink channel measurements in a fifth-generation (5G)
new radio (NR) system. An attention-aided ML-based single-snapshot localization
pipeline is presented, which consists of several cascaded blocks, namely a
signal processing block, an attention-aided block, and an uncertainty
estimation block. Specifically, the signal processing block generates an
impulse response beam matrix for all beams. The attention-aided block trains on
the channel impulse responses using an attention-aided network, which captures
the correlation between impulse responses for different beams. The uncertainty
estimation block predicts the probability density function of the UE position,
thereby also indicating the confidence level of the localization result. Two
representative uncertainty estimation techniques, the negative log-likelihood
and the regression-by-classification techniques, are applied and compared.
Furthermore, for dynamic measurements with multiple snapshots available, we
combine the proposed pipeline with a Kalman filter to enhance localization
accuracy. To evaluate our approach, we extract channel impulse responses for
different beams from a commercial base station. The outdoor measurement
campaign covers Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a mix of LoS
and NLoS scenarios. The results show that sub-meter localization accuracy can
be achieved.
