MFCM for nonlinear blind channel equalization

In this study, we present a modified Fuzzy C-Means (MFCM) algorithm for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed by the combinations of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, 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 augment by simulated annealing (SA), GASA). It is shown that a relatively high accuracy and fast search speed has been achieved.