Robustness of blind fractionally-spaced identification/equalization to loss of channel disparity

In this contribution, we address the comparison of Subspace (SS), Linear Prediction (LP) and Constant Modulus (CM) identi caton/equalization algorithms in terms of robustness to loss of Fractionally-Spaced channel disparity. We show that SS procedure leads to an inconsistent channel estimation. Investigating a left-inverse channel estimation, we show that LP results in the estimation of the so-called minimum-phase multivariate channel factorization. We show that CM criterion still perform reasonable channel estimation, even if proper algorithm initialization is still a critical subject.