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Synchronization is a fundamental procedure in cellular systems whereby an UEacquires the time and frequency information required to decode the datatransmitted by a BS. Due to the necessity of using large antenna arrays toobtain the beamforming gain required to compensate for small antenna aperture,synchronization must be performed either jointly with beam training as in 5GNR, or at the low SNR regime if the high-dimensional mmWave MIMO channel is tobe estimated. To circumvent this problem, this work proposes the firstsynchronization framework for mmWave MIMO that is robust to both TO, CFO, andPN synchronization errors and, unlike prior work, implicitly considers the useof multiple RF chains at both transmitter and receiver. I provide a theoreticalanalysis of the estimation problem and derive the HCRLB for the estimation ofboth the CFO, PN, and equivalent beamformed channels seen by the differentreceive RF chains. I also propose two novel algorithms to estimate thedifferent unknown parameters, which rely on approximating the MMSE estimatorfor the PN and the ML estimators for both the CFO and the equivalent beamformedchannels. Thereafter, I propose to use the estimates for the equivalentbeamformed channels to perform compressive estimation of the high-dimensionalfrequency-selective mmWave MIMO channel and thus undergo data transmission. Forperformance evaluation, I consider the QuaDRiGa channel simulator, whichimplements the 5G NR channel model, and show that both compressive channelestimation without prior synchronization is possible, and the proposedapproaches outperform current solutions for joint beam training andsynchronization currently considered in 5G NR.

Broadband Synchronization and Compressive Channel Estimation for Hybrid mmWave MIMO Systems