Nonlinear Channel Equalization Using Multilayer Perceptrons with Information-theoretic Criterion

The minimum error entropy criterion was recently suggested in adaptive system training as an alternative to the mean-square-error criterion, and it was shown to produce better results in many tasks. In this paper, we apply a multiplayer perceptron scheme trained with this information theoretic criterion to the problem of nonlinear channel equalization. In our simulations, we use a realistic nonlinear channel model, which is encountered when practical power amplifiers are used in the transmitter. The bandwidthefficient 16-QAM scheme, which uses a dispersed constellation, is assumed.