Cyclostationary autocorrelation based CFO estimators

We derive three novel data-aided CFO estimators based on a new autocorrelation function which is defined using cyclostationary properties of the repetitive preamble. The first estimator is a generalization of a classic estimator, and the other two take advantage of the structure of the new autocorrelation function to improve the estimation for low CFO. In addition, the performance of the proposed schemes is studied using simulations and compared with classical implementations. Comparing to the state-of-the-art CFO estimators, the proposed algorithms improve the performance, in terms of mean-squared error (MSE), by 6 % at an SNR of 2 dB and by 100 % at an SNR of -5dB.