Asymptotically Efficient Reduced-Complexity Frequency Offset Estimation for Uplink MIMO-OFDMA Systems

In this paper, we address the joint data-aided estimation of the frequency offsets and channel coefficients in the uplink MIMO-OFDMA systems. A novel low complexity ML-based joint estimator is proposed which is applicable for any subcarrier assignment scheme (SAS). It is shown that the proposed estimator is asymptotically efficient, i.e., the variance of the estimates achieves the performance bounds for large number of subcarriers. Moreover, this estimator is generalized in such a way that the resulting estimators achieve the performance bounds for reasonable training sizes. Simulation results sustain all our claims.