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

Kim, Nam-Yong (강원대학교 공학대학 전자정보통신공학부)
Kang, Sung-Jin (한국기술교육대학교 정보기술공학부)
Hong, Dae-Ki (상명대학교 공과대학 정보통신공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.35, no.1C, 2010 , pp. 33-39 More about this Journal
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.
Keywords
Correntropy; MCC; Blind Equalizer; PDF; Euclidian Distance; Parzen Window;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 F. Mazzenga, "Channel estimation and equalization for M-QAM transmission with a hidden pilot sequence," IEEE Trans. on Broadcasting, Vol.46, pp.170-176, June, 2000.   DOI   ScienceOn
2 L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics, 2005.
3 D. Erdogmus, Y. Rao and J. C. Principe, "Supervised Training of Adaptive Systems with Partially Labeled Data," Proceedings of the International Conference on ASSP, Apr. pp.v321-v324, 2005.
4 K. H. Jeong, J. W. Xu, D. Erdogmus, and J. C. Principe, "A new classifier based on information theoretic learning with unlabeled data," Neural Networks, 18, pp.719-726, 2005.   DOI   ScienceOn
5 S. Haykin, Adaptive Filter Theory. Prentice Hall. (Upper Saddle River), 4th edition, 2001.
6 J. C. Principe, D. Xu and J. Fisher, Information Theoretic Learning in: S. Haykin, Unsupervised Adaptive Filtering, Wiley, (New York, USA), pp.265-319, 2000.
7 S. Kullback, Information Theory and Statistics, Dover Publications. (New York, USA), 1968.
8 N. Kim and L. Yang, "A New Criterion of Information Theoretic Optimization and Application to Blind Channel Equalization," Journal of Korean Society for Internet Information, Vol.10, No.1, pp.11-17, Feb., 2009.
9 L. M. Garth, "A dynamic convergence analysis of blind equalization algorithms," IEEE Trans. on Comm., Vol.49, pp.624-634. April, 2001.   DOI   ScienceOn
10 I. Santamaria, P. P. Pokharel, and J. C. Principe, "Generalized Correlation Function: Definition, Properties, and Application to Blind Equalization," Trans. on Signal Processing, Vol.54, No.6, pp.2187-2198, Jun., 2006.   DOI
11 D. Erdogmus, and J.C. Principe, "An Entropy Minimization algorithm for Supervised Training of Nonlinear Systems," IEEE Trans. Signal Processing, Vol.50, pp.1780-1786, July, 2002.   DOI   ScienceOn
12 W. M. Moh and Y. Chen, "Multicasting flow control for hybrid wired/wireless ATM networks," Performance Evaluation, Vol.40, pp.161-194, Mar., 2000.   DOI   ScienceOn
13 Weifeng Liu, P. P. Pokharel, and J. C. Principe, "Correntropy: Properties and Applications in Non-Gaussian Signal Processing," IEEE Trans. Signal Processing, Vol.55, No.11, pp.5286- 5298, Nov., 2007.   DOI