Adaptive decision feedback equalizer based neural network for nonlinear channels

This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer (MLPE) outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP DFE) trained with the back propagation algorithm. The capacity of the MLP DFE to deal with nonlinear channels is evaluated. It is shown from simulation results that performance of the MLP DFE surpass significantly the MLPE in term of eye pattern quality, steady state mean square error (MSE), and minimum Bit Error Rate (BER). The MLPE equalizer performs poorly on the severe nonlinear channel whereas the MLP DFE equalizer provides the best performance on the two nonlinear channels considered.