A NB-IoT Random Access Scheme Based on Change Point Detection in NTNs

In order to satisfy the higher demand raised by 6G, the major part of which refers to further extension of the coverage and achievements of ubiquitously massive connections, the integration of Non-Terrestrial Networks (NTNs) and the evolved version of Narrow Bandwidth Internet of Things (NB-IoT) are introduced as a potential solution. However, one of the main challenges still open is the Random Access (RA) procedure which refers to preamble detection and uplink synchronization. With the large Time of arrival (ToA) and Carrier frequency offset (CFO), traditional methods designed for Terrestrial Networks (TNs) can not provide accurate estimation performance. Some studies adopt Global Navigation Satellite System (GNSS) to pre-compensate the large ToA and CFO but it may not work well for NB-IoT devices in practice. Consequently, a complete system without GNSS is designed for RA in this paper. A system-level method is firstly applied to pre-compensate the CFO. Then we focus on tackling the ToA and residual CFO. A pre-process method for received signal is proposed and its phase series are analyzed. Then we propose a novel random access scheme based on change point detection (CPD) of phase series with machine learning to estimate ToA and CFO. Simulation results demonstrate that the designed overall system is practical in NTNs. Meanwhile, the proposed random access scheme achieves higher estimation accuracy of ToA and CFO compared with existing methods with the same range of ToA and CFO under low signal-noise ratio (SNR) conditions.