Blind adaptive equalization using cost function that measures dissimilarity between the probability distributions of source and equalized signals

摘要

A technique for the blind equalization of digital communications channels relies on the iterative minimization of a cost function known as the J-divergence between a known or assumed probability density function (PDF) of the source data signal and an estimated PDF of a receiver decision output signal derived from the equalizer output signal by minimum-distance mapping. The J-divergence function is defined in terms of the Kullback-Leibler distance between the two PDFs. Minimization is achieved by continually updating both an equalizer tap coefficient vector and the estimated PDF of the decision output signal using a stochastic gradient algorithm applied to the J-divergence cost function.