On Time-of-Arrival Estimation in NB-IoT Systems

We consider time-of-arrival (ToA) estimation for a device working in narrowband Internet-of-Things (NB-IoT) systems. Due to a limited 180 kHz bandwidth, the time-domain auto-correlation function (ACF) of transmitted NB positioning reference signal (NPRS) has a wide main-lobe. Without considering that, the performance of ToA estimation can be degraded for two reasons. Firstly, the NPRS corresponding to different received paths are superimposed on each other under multipath propagation. Secondly, the measured peak-to-average power-ratio (PAPR) for detecting the presence of NPRS is inaccurate. Therefore, in this letter we propose a space-alternating generalized expectation-maximization (SAGE) based method to estimate the number of channel taps, coefficients, and corresponding delays, with taking the imperfect ACF of NPRS into consideration. The proposed ToA estimator only uses time-domain cross-correlations between the received signal and the transmitted NPRS, which yields a low computational-cost. We show through simulations that, it performs close to maximum likelihood (ML) estimator under flat-fading channels, and is superior than traditional estimators under frequency-selective fading channels.