Blind equalization of nonlinear communication channels using recurrent wavelet neural networks

Abstract

This paper investigates the application of a recurrent wavelet neural network (RWNN) to the blind equalization of nonlinear communication channels. We propose a RWNN based structure and a novel training approach for blind equalization, and we evaluate its performance via computer simulations for a nonlinear communication channel model. It is shown that the RWNN blind equalizer performs much better than the linear CMA and the RRBF blind equalizers in the nonlinear channel case. The small size and high performance of the RWNN equalizer makes it suitable for high speed channel blind equalization.