TY - GEN
T1 - Fingerprint-based localization using commercial LTE signals
T2 - 90th IEEE Vehicular Technology Conference, VTC 2019 Fall
AU - Zhang, Heng
AU - Zhang, Zhichao
AU - Zhang, Shunqing
AU - Xu, Shugong
AU - Cao, Shan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.
AB - Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.
KW - CSI
KW - Deep learning
KW - Fingerprinting
KW - Localization
KW - LTE
UR - http://www.scopus.com/inward/record.url?scp=85075257554&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2019.8891257
DO - 10.1109/VTCFall.2019.8891257
M3 - Conference Proceeding
AN - SCOPUS:85075257554
T3 - IEEE Vehicular Technology Conference
BT - 2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 September 2019 through 25 September 2019
ER -