Multicarrier communication in presence of biased-Gaussian noise sources
Signal Processing
Blind equalization based on information theoretic learning for impulsive noise environments
ECC'10 Proceedings of the 4th conference on European computing conference
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
An error-entropy minimization algorithm for supervised training ofnonlinear adaptive systems
IEEE Transactions on Signal Processing
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
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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. The spreading force is composed of the difference between output sample values themselves, and is directly related with the output information potential and the output entropy that leads to the output distribution spreading out. The proposed kernel-size modification scheme is to impose loose discipline on the spreading force by employing larger kernel-size so that the long distance between two outputs which are correct symbol-related and far-located is less likely to be treated as impulsive noise. From the simulation results, the proposed kernel-size modified blind algorithm not only outperforms correntropy blind algorithm, but also significantly enhance robustness against impulsive noise.