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Based on the sparse property of the Cyclic Autocorrelation Function (CAF) in the cyclic frequency domain, we propose in this paper a new blind estimator using compressed sensing, which better estimates the Cyclic Autocorrelation Function at a given delay compared to the classical non blind estimator when using the same number of samples. Two metrics are used to evaluate this estimation: the Mean Square Error of type one and that of type two ( MSE  1  and MSE  2 ). The first metric compares the estimated Cyclic Autocorrelation Vector (CAV) which is a particular vector of the CAF for a fixed delay τ , with the theoretical reference vector obtained using the CAF. The second metric compares the value of the estimated cyclic frequency with the theoretical one. Simulation results show lower ( MSE  1  and MSE  2  for the new proposed estimator compared to the classical one under the same conditions, from the simplest scenario to the more realistic one that uses a filter at the transmission side, a reception noise, and fading channel.

On the Use of the Sparse Property of the Cyclic Autocorrelation Function to Blindly Estimate the Cyclostationarity