Neural equalization applied to systems with bidimensional digital modulation

摘要

A new equalization model for digital communication systems is proposed, based on a multi-layer perceptron (MLP) artificial neural network with a back-propagation algorithm. Unlike earlier techniques, the proposed model, called the bidimensional neural equalizer, is composed of two independent MLP networks that operate in parallel for each dimension of the digital modulation scheme. A heuristic method to combine the errors of the two MLP networks is also proposed, with the aim of reducing the convergence time. Simulations performed for linear and nonlinear channels demonstrated that the new model could improve performance in terms of the bit error rate and the convergence time, compared to existing models.