Blind equalization based on information theoretic learning for impulsive noise environments

Authors:
Namyong Kim;Hyung-Gi Byun
Affiliations:
Dep. of Information & Communication Engineering, Kangwon National University, Samcheok, Kangwon Do, Korea;Dep. of Information & Communication Engineering, Kangwon National University, Samcheok, Kangwon Do, Korea
Venue:
ECC'10 Proceedings of the 4th conference on European computing conference
Year:
2010

Citing 2
Cited 1

Multicarrier communication in presence of biased-Gaussian noise sources

Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization

IEEE Transactions on Signal Processing - Part I

Blind signal processing based on information theoretic learning with kernel-size modification for impulsive noise channel equalization

WSEAS TRANSACTIONS on COMMUNICATIONS

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the development and performance study of information-theoretic learning (ITL) based blind equalizer algorithms for inter-symbol interference communication channel environments with a mixture of white Gaussian noise and impulsive noise are presented. A new developed blind equalizer algorithm based on matching two probability density functions of outputs and desired symbols employs assignment of transmitted source levels to the places of desired symbols evenly by utilizing the modulation schemes produces significant performance enhancement.. Gaussian kernel of the proposed algorithm has the effect of becoming insensitive to the large differences between impulse-infected outputs and desired level values. From the simulation results, the proposed blind algorithm shows superior performance to the CMA and/or correntropy blind algorithm in impulsive noise environments.