Joint Synchronization, Phase Noise and Compressive Channel Estimation in Hybrid Frequency-Selective mmWave MIMO Systems

The large beamforming gain used to operate at millimeter wave (mmWave) frequencies requires obtaining channel information to configure hybrid antenna arrays. Previously proposed wideband channel estimation strategies, however, assume perfect time-frequency synchronization and neglect phase noise, making these approaches impractical. Consequently, achieving time-frequency synchronization between transmitter and receiver and correcting for phase noise (PN) as the channel is estimated, is the greatest challenge yet to be solved in order to configure hybrid precoders and combiners in practical settings. In this paper, building upon our prior work, we find the Maximum A Posteriori (MAP) solution to the joint problem of timing offset (TO), carrier frequency offset (CFO), PN and compressive channel estimation for broadband mmWave MIMO systems with hybrid architectures. Simulation results show that, using significantly less training symbols than in the beam training protocol in the 5G New Radio communications standard, joint synchronization and channel estimation at the low SNR regime can be achieved, and near-optimum data rates can be attained

Broadband Synchronization and Compressive Channel Estimation for Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby an UE acquires the time and frequency information required to decode the data transmitted by a BS. Due to the necessity of using large antenna arrays to obtain the beamforming gain required to compensate for small antenna aperture, synchronization must be performed either jointly with beam training as in 5G NR, or at the low SNR regime if the high-dimensional mmWave MIMO channel is to be estimated. To circumvent this problem, this work proposes the first synchronization framework for mmWave MIMO that is robust to both TO, CFO, and PN synchronization errors and, unlike prior work, implicitly considers the use of multiple RF chains at both transmitter and receiver. I provide a theoretical analysis of the estimation problem and derive the HCRLB for the estimation of both the CFO, PN, and equivalent beamformed channels seen by the different receive RF chains. I also propose two novel algorithms to estimate the different unknown parameters, which rely on approximating the MMSE estimator for the PN and the ML estimators for both the CFO and the equivalent beamformed channels. Thereafter, I propose to use the estimates for the equivalent beamformed channels to perform compressive estimation of the high-dimensional frequency-selective mmWave MIMO channel and thus undergo data transmission. For performance evaluation, I consider the QuaDRiGa channel simulator, which implements the 5G NR channel model, and show that both 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