Abstract-In this paper we address the problem of blind carrier frequency offset (CFO) estimation in OFDM systems in the case of frequency selective channels. CFO destroys the orthogonality between the carriers leading to nondiagonal signal covariance matrices in frequency domain. The proposed blind method enforces a diagonal structure by minimizing the power of non-diagonal elements. Hence, the orthogonality property inherent to OFDM transmission with cyclic prefix is restored. The method is blind since it does not require a priori knowledge of the transmitted data or the channel, and does not need any virtual subcarriers. A closed-form solution is derived, which leads to accurate and computationally efficient CFO estimation in multipath fading environments. Consistency of the estimator is proved and the convergence rate as a function of the sample size is analyzed as well. To assess the large sample performance, we derive the Cramér-Rao bound (CRB) for the blind CFO estimation problem. The CRB is derived assuming a general Gaussian model for the OFDM signal, which may be applied to both circular and non-circular modulations. Finally, simulation results on CFO estimation are reported using a realistic channel model.

In this paper we address the problem of joint channel and frequency offset estimation and tracking in multiple-input multiple-output (MIMO) OFDM systems for mobile users. The proposed method stems from extended Kalman filtering and is suitable for time-frequency-space selective channels. Separate offset for each MIMO channel branch is considered because of the mobility and rich scattering. The channel taps and the frequency offsets are estimated in time-domain while the equalization is performed in frequency domain. Simulation results demonstrate that the proposed method tracks time-varying channels and frequency offsets with high fidelity. Realistic channel models are used in mobile scenarios. The proposed time-domain approach has improved performance and robustness in comparison to purely frequency domain processing. Computational complexity is lower as well.

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