Browse > Article
http://dx.doi.org/10.5762/KAIS.2012.13.1.343

Blind Equalizer Algorithms using Random Symbols and Decision Feedback  

Kim, Nam-Yong (School of Electronics, Info. & Comm. Engineering, Kangwon National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.1, 2012 , pp. 343-347 More about this Journal
Abstract
Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart.
Keywords
Decision feedback; Blind equalization; PDF; Random symbols; ITL;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 L. Garth, "A dynamic convergence analysis of blind equalization algorithms," IEEE Trans. on Comm., Vol. 49, pp. 624-634, April, 2001.   DOI   ScienceOn
2 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
3 J. Principe, D. Xu and J. Fisher, Information Theoretic Learning, in: S. Haykin, Unsupervised Adaptive Filtering, Wiley, New York, Vol. I, pp. 265-319, 2000.
4 D. Erdogmus, K. Hild, M. Lazaro, I. Santamaria, and J. Principe, "Aaptive Blind Deconvolution of Linear Channels Using Renyi's Entropy with Parzen Estimation," IEEE. Trans. on Signal Processing, Vol. 52, pp. 1489-1498, June, 2004.   DOI   ScienceOn
5 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.   과학기술학회마을
6 E. Parzen, "On the estimation of a probability density function and the mode," Ann. Math. Stat. Vol.33, p. 1065, 1962.   DOI   ScienceOn
7 S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, 4th edition, 2001.