Parametric approach to blind deconvolution of nonlinear channels

Abstract A parametric procedure for blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Two parametric models are developed: a polynomial model and a neural model. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of the input distribution (score function). Each algorithm consists of three adaptive blocks: one for estimating the score function, and two other blocks for estimating the inverses of the linear and nonlinear parts of the channel. Experiments show that the algorithms perform efficiently, even for hard nonlinear distortion.