This paper proposes a novel blind carrier frequency offset (CFO) estimator, namely the sparse recovery assisted CFO (SR-CFO) estimator, for the uplink orthogonal frequencydivision multiple access (OFDMA) systems. By exploiting the sparsity embedded in the OFDMA data, the CFO estimation is formulated as an optimization problem of sparse recovery with high-resolution. Meanwhile, in order to enhance the estimation accuracy of CFOs, background noise and sampling errors are mitigated by exploiting the structure of the noise covariances matrix in the transformed observation data, and the asymptotic distribution of the sampling errors. Furthermore, we propose an approach for deriving the regularization parameter used by the SR-CFO estimator, so as to control the trade-off between the data fitting error and the sparsity of solution. The performance of the proposed SR-CFO estimator along with other four existing estimators is investigated and compared. Numerical results show that the proposed SR-CFO estimator is superior to the state-of the-art estimators in terms of the estimation reliability.
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