MIMO antennas with orthogonal frequency-division multiplexing (OFDM) provide high data rates and are robust to multipath delay in wireless communications. In this paper, we propose a complete algorithm capable of jointly estimating the CFO and the path CA by taking into account the fast variation of each path CA in a MIMO environment. In olden approach, an algorithm based on extended Kalman ?ltering (EKF) and the equivalent discrete-time channel model, but the fast time variation of the channel was not taken into account. The extended Kalman filter in general is not an optimal estimator (of course it is optimal if the measurement and the state transition model are both linear, as in that case the extended Kalman filter is identical to the regular one). In addition, if the initial estimate of the state is wrong, or if the process is modeled incorrectly, the filter may quickly diverge, owing to its linearization. An improvement to the extended Kalman filter led to the development of the Unscented Kalman filter (UKF), also a nonlinear filter. In the UKF, the probability density is approximated by the nonlinear transformation of a random variable, which returns much more accurate results than the first-order Taylor expansion of the nonlinear functions in the EKF. The approximation utilizes a set of sample points, which guarantees accuracy with the posterior mean and covariance to the second order for any nonlinearity. The UKF tends to be more robust and more accurate than the EKF in its estimation of error.