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

Guangjin Pan*, Tao Wang, Shunqing Zhang, Shugong Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article number9144285
Pages (from-to)1980-1984
Number of pages5
JournalIEEE Wireless Communications Letters
Volume9
Issue number11
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes

Keywords

  • Internet-of-Things
  • multipath channels
  • narrowband
  • neural networks
  • Time of arrival estimation

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