Blind Equalization based on Maximum Cross-Correntropy Criterion using a Set of Randomly Generated Symbol 


Vol. 35,  No. 1, pp. 33-39, Jan.  2010


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  Abstract

Correntropy is a generalized correlation function that contains higher order moments of the probability density function (PDF) than the conventional moment expansions. The criterion maximizing cross-correntropy (MCC) of two different random variables has yielded superior performance particularly in nonlinear, non-Gaussian signal processing comparing to mean squared error criterion. In this paper we propose a new blind equalization algorithm based on cross-correntropy criterion which uses, as two variables, equalizer output PDF and Parzen PDF estimate of a set of randomly generated symbols that complies with the transmitted symbol PDF. The performance of the proposed algorithm based on MCC is compared with the Euclidian distance minimization.

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  Cite this article

[IEEE Style]

N. Kim, S. Kang, D. Hong, "Blind Equalization based on Maximum Cross-Correntropy Criterion using a Set of Randomly Generated Symbol," The Journal of Korean Institute of Communications and Information Sciences, vol. 35, no. 1, pp. 33-39, 2010. DOI: .

[ACM Style]

Namyong Kim, Sung-jin Kang, and Dae-ki Hong. 2010. Blind Equalization based on Maximum Cross-Correntropy Criterion using a Set of Randomly Generated Symbol. The Journal of Korean Institute of Communications and Information Sciences, 35, 1, (2010), 33-39. DOI: .

[KICS Style]

Namyong Kim, Sung-jin Kang, Dae-ki Hong, "Blind Equalization based on Maximum Cross-Correntropy Criterion using a Set of Randomly Generated Symbol," The Journal of Korean Institute of Communications and Information Sciences, vol. 35, no. 1, pp. 33-39, 1. 2010.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880