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Non-Terrestrial Networks (NTNs) are widely recognized as a potential solutionto achieve ubiquitous connections of Narrow Bandwidth Internet of Things(NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is theuplink synchronization of Narrowband Physical Random Access procedure whichrefers to the estimation of time of arrival (ToA) and carrier frequency offset(CFO). Due to the large propagation delay and Doppler shift in NTNs,traditional estimation methods for Terrestrial Networks (TNs) can not beapplied in NTNs directly. In this context, we design a two stage ToA and CFOestimation scheme including coarse estimation and fine estimation based onabrupt change point detection (CPD) of phase series with machine learning. Ourmethod achieves high estimation accuracy of ToA and CFO under the lowsignal-noise ratio (SNR) and large Doppler shift conditions and extends theestimation range without enhancing Random Access preambles.

NB-IoT Uplink Synchronization by Change Point Detection of Phase Series in NTNs