A new approach based on “soft statistics” to the nonlinear blind-deconvolution of unknown data channels

In this paper, we present a new nonlinear receiver for the blind deconvolution of intersymbol interference (ISI) impaired data. The proposed receiver achieves fast identification of an unknown transmission channel using only one channel estimator and requiring the computation of only the second-order conditional statistics of the baud-rate sampled received signal and the knowledge of the transmitted constellation. The main novelty of the proposed approach is that the receiver accomplishes fast channel-identification by using soft-statistics. In particular, the receiver consists of a symbol-by-symbol maximum a posteriori (SbS-MAP) detector that feeds a nonlinear Kalman-like channel estimator with the soft statistics constituted by the a posteriori probabilities (APPs) of the state sequence of the ISI channel. Several numerical results confirm that the proposed blind detector achieves the identification of nonminimum phase channels with deep spectral notches within 300 symbols, even at low signal-to-noise ratios (SNRs). Furthermore, an attractive feature of the proposed blind channel estimator is that it directly estimates the discrete-time impulse response of the unknown channel so that, in principle, any equalization technique for known channels may be performed after channel identification has been achieved