Blind Signal Processing Based on Information Theoretic Learning With Kernel-Size Modification for Impulsive Noise Channel Equalization
Source: Kangwon National University
This paper presents a new performance enhancement method of Information-Theoretic Learning (ITL) based blind equalizer algorithms for ISI communication channel environments with a mixture of AWGN and impulsive noise. The Gaussian kernel of Euclidian Distance (ED) minimizing blind algorithm using a set of evenly generated symbols has the net effect of reducing the contribution of samples that are far away from the mean value of the error distribution. The process of ED minimization between desired Probability Density Function (PDF) and output PDF is considered as a harmonious force interaction on PDF shaping between concentrating force and spreading force.