Using The Constant Modulus and Kullback-Leibler Cost Functions on a Nonlinear Predictive Structure for Blind Equalization

"The use of a nonlinear structure of filtering for blind equalization is presented. The structure neural network-based is used in order to provide nonlinearity on the filter structure and the learning strategy is then divided in two stages. The Kullback-Leibler divergence is used as the base for the cost function of a self-organized rule and constant modulus criterion for the supervised one. Simulation results illustrate the performance of the strategy compared with classical ones for adaptive equalization. The results show that the proposed strategy outperforms even trained DFE for some cases of channels."