文章基本信息

标题：Gaussian Weighted MFCM for Nonlinear Blind Channel Equalization
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作者：Soowhan Han, Sungdae Park
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2008
卷号：8
期号：5
页码：44-50
出版社：International Journal of Computer Science and Network Security
摘要：In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partiton matrix is exploited in this method. In the search procedure, all of the possible desired channel states are constructed by considering the combinations of estimated channel output states. The desired state characterized by the maximal value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the final desired state is placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, the relatively high accuracy and fast search speed of the method are observed.
关键词：Modified Fuzzy C-Means, Gaussian Weighted Partition Matrix, Bayesian Likelihood, Nonlinear Blind Channel Equalizer