  Low complexity joint estimation of synchronization impairments and channel in
a single-user MIMO-OFDM system is presented in this letter. Based on a system
model that takes into account the effects of synchronization impairments such
as carrier frequency offset, sampling frequency offset, and symbol timing
error, and channel, a Maximum Likelihood (ML) algorithm for the joint
estimation is proposed. To reduce the complexity of ML grid search, the number
of received signal samples used for estimation need to be reduced. The
conventional channel estimation methods using Least-Squares (LS) fail for the
reduced sample under-determined system, which results in poor performance of
the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS)
based channel estimation method in a sparse fading scenario, where the received
samples used for estimation are less than that required for an LS based
estimation. The performance of the estimation method is studied through
numerical simulations, and it is observed that CS based joint estimator
performs better than LS based joint estimator
