Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel

Authors:
Soowhan Han;Imgeun Lee;Witold Pedrycz
Affiliations:
Dept. of Multimedia Eng., Dongeui Univ., Busan 614-714, Republic of Korea;Dept. of Visual Information Eng., Dongeui Univ., Busan 614-714, Republic of Korea;Department of Electrical and Computer Eng., Univ. of Alberta, Edmonton, Canada T6G 2G7
Venue:
Applied Soft Computing
Year:
2009

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Abstract

In this study, we present a modified fuzzy c-means (MFCM) clustering algorithm in the problem of nonlinear blind channel equalization. The proposed MFCM searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to the commonly exploited Euclidean distance, in this method we consider the usage of the Bayesian likelihood fitness function. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states and the set of desired states characterized by the maximal value of the Bayesian fitness is selected. By using these desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulation studies, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA) augmented by the mechanism of simulated annealing (SA), GASA for brief. It is demonstrated that a relatively high accuracy and a fast search speed have been achieved.