Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.