ARMA system identification based on second-order cyclostationarity

Previous work has presented novel techniques that exploit cyclostationarity for channel identification in data communication systems. The present authors investigate the identifiability of linear time-invariant (LTI) ARMA systems based on second-order cyclic statistics. They present a parametric and a nonparametric method. The parametric method directly identifies the zeros and poles of ARMA channels with a mixed phase. The nonparametric method estimates the channel phase based on the cyclic spectra alone. They analyze the phase estimation error of the nonparametric method for finite dimensional ARMA channels. For specific, finite dimensional ARMA channels, an improved method is given, which combines the parametric method with the nonparametric method. Computer simulation illustrates the effectiveness of the methods in identifying ARMA system impulse responses