An approach to Blind Deconvolution based on Second-Order "Soft" Statistics

We present a new blind equalizer that achieves identification of a channel by exploiting only second-order statistics of the observations. The novelty of the proposed approach is that the receiver accomplishes channel identification by using soft statistics; roughly speaking, it consists of an Abend-Fritchman (1970) type maximum a posteriori (MAP) equalizer that feeds a nonlinear Kalman-like channel-estimator with the soft statistics constituted by the a posteriori probabilities (APPs) of the channel-state sequence. So, since the receiver employs second-order statistics only, it achieves channel identification with fewer symbols than most techniques based on higher-order statistics.