Unified analysis of a class of blind feedforward symbol timing estimators employing second-order statistics

In this paper, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.