Gaussian Weighted MFCM for Nonlinear Blind Channel Equalization

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.