Asymptotically Efficient Reduced Complexity Frequency Offset and Channel Estimators for Uplink MIMO-OFDMA Systems

In this paper, we address the joint data-aided estimation of frequency offsets and channel coefficients in uplink multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) systems. As the maximum-likelihood (ML) estimator is impractical in this context, we introduce a family of suboptimal estimators with the aim of exhibiting an attractive tradeoff between performance and complexity. The estimators do not rely on a particular subcarrier assignment scheme and are, thus, valid for a large number of OFDMA systems. As far as complexity is concerned, the computational cost of the proposed estimators is shown to be significantly reduced compared to existing estimators based on ML. As far as performance is concerned, the proposed suboptimal estimators are shown to be asymptotically efficient, i.e., the covariance matrix of the estimation error achieves the Cramer-Rao bound when the total number of subcarriers increases. Simulation results sustain our claims.