Blind deconvolution by simple adaptive activation function neuron

Abstract

The ‘Bussgang’ algorithm is one among the most known blind deconvolution techniques in the adaptive signal processing literature. It relies on a Bayesian estimator of the source signal that requires the prior knowledge of the source statistics as well as the deconvolution noise characteristics. In this paper, we propose to implement the estimator with a simple adaptive activation function neuron, whose activation function is endowed with one learnable parameter; in this way, the algorithm does not require to hypothesize deconvolution noise level. Neuron's weights adapt through an unsupervised learning rule that closely recalls non-linear minor component analysis. In order to assess the effectiveness of the proposed method, computer simulations are presented and discussed.