  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.
