  Conventional schemes often require extra reference signals or more
complicated algorithms to improve the time-of-arrival (TOA) estimation
accuracy. However, in this letter, we propose to generate fine-grained features
from the full band and resource block (RB) based reference signals, and
calculate the cross-correlations accordingly to improve the observation
resolution as well as the TOA estimation results. Using the spectrogram-like
cross-correlation feature map, we apply the machine learning technology with
decoupled feature extraction and fitting to understand the variations in the
time and frequency domains and project the features directly into TOA results.
Through numerical examples, we show that the proposed high accurate TOA
estimation with fine-grained feature generation can achieve at least 51% root
mean square error (RMSE) improvement in the static propagation environments and
38 ns median TOA estimation errors for multipath fading environments, which is
equivalently 36% and 25% improvement if compared with the existing MUSIC and
ESPRIT algorithms, respectively.
