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

We consider time-of-arrival (ToA) estimation of a first arrival-path for a device working in narrowband Internetof-Things (NB-IoT) systems. Due to a limited 180 KHz bandwidth used in NB-IoT, 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, under multiplepath channel environments, the NPRS corresponding to different received paths are superimposed on each other, and so are the cross-correlations corresponding to them. Secondly, the measured peak-to-average-power-ratio (PAPR) used for detecting the presence of NPRS is inaccurate. Therefore, in this paper we propose a space-alternating generalized expectation-maximization (SAGE) based method to jointly estimate the number of channel taps, the channel coefficients and the corresponding delays in NB-IoT systems, with considering the imperfect ACF of NPRS. Such a proposed method only uses the time-domain cross-correlations between the received signal and the transmitted NPRS, and has a low complexity. We show through simulations that, the ToA estimation of the proposed method performs close to the maximum likelihood (ML) estimation for a single-path channel, and significantly outperforms a traditional ToA estimator that uses signal-to-noise (SNR) or power thresholds based estimation.