TY - JOUR
T1 - High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications
AU - Pan, Guangjin
AU - Wang, Tao
AU - Zhang, Shunqing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Internet-of-Things
KW - multipath channels
KW - narrowband
KW - neural networks
KW - Time of arrival estimation
UR - http://www.scopus.com/inward/record.url?scp=85096160809&partnerID=8YFLogxK
U2 - 10.1109/LWC.2020.3010251
DO - 10.1109/LWC.2020.3010251
M3 - Article
AN - SCOPUS:85096160809
SN - 2162-2337
VL - 9
SP - 1980
EP - 1984
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 11
M1 - 9144285
ER -