Fingerprint-based Localization using Commercial LTE Signals: A Field-Trial Study

TL;DR

This paper presents a deep learning-based fingerprint localization method using commercial LTE signals, achieving high accuracy in indoor and outdoor environments through real-time CSI data and a fusion approach.

Contribution

It introduces a novel LTE fingerprint localization technique with deep learning and a time domain fusion method, improving accuracy and robustness in challenging environments.

Findings

Indoor MDE of 0.47 meters

Outdoor MDE of 19.9 meters

Significant accuracy improvement over existing methods

Abstract

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.