High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications

TL;DR

This paper introduces a novel method for highly accurate time-of-arrival estimation in IoT applications by generating fine-grained features from reference signals and applying machine learning, significantly outperforming traditional algorithms.

Contribution

The paper proposes a new fine-grained feature generation technique combined with machine learning for improved TOA estimation accuracy in IoT environments.

Findings

Achieves at least 51% RMSE improvement in static environments.

Attains 38 ns median TOA error in multipath fading environments.

Outperforms MUSIC and ESPRIT algorithms by 36% and 25%, respectively.

Abstract

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.