Carrier frequency offset （CFO） in MIMO-OFDM systems can be decoupled into two parts： fraction frequency offset （FFO） and integer frequency offset （IFO）. The problem of IFO estimation is addressed and a new IFO estimator based on the Bayesian philosophy is proposed. Also, it is shown that the Bayesian IFO estimator is optimal among all the IFO estimators. Furthermore, the Bayesian estimator can take advantage of oversampling so that better performance can be obtained. Finally, numerical results show the optimality of the Bayesian estimator and validate the theoretical analysis.
This paper describes a Least Squares (LS) channel estimation scheme for MIMO OFDM systems based on time-domain training sequence. We first compute the minimum mean square error (MSE) of the LS channel estimation, and then derive the optimal criteria of the training sequence with respect to the minimum MSE. It is shown that optimal time-domain training sequence should satisfy two criteria. First, the autocorrelation of the sequence transmitted from the same antenna is an impulse function in a region longer than the channel maximum delay. Second, the cross-correlation between sequences transmitted from different antennas is zero in this region. Simulation results show that the estimator using optimal time-domain training sequences has better performance than that using optimal frequency training sequence at low signal-to-noise ratio (SNR). To reduce the training overhead, a suboptimal training sequence is also proposed. Comparing with optimal training sequence, it has low computation complexity and high transmission efficiency at the expense of little performance degradation.
