Noise reduction for fast fading channel by Recurrent Least Squares Support Vector Machines in embedding phase spaces

Abstract

A new strategy for noise reduction of fast fading channel is presented. Firstly, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Support Vector Machines (RLS-SVM), noise reduction of the fast fading channel is realized. This filtering technique does not make use of the spectral contents of the signal. Based on the stability and the fractal of the chaotic attractor, the RLS-SVM algorithm is a better candidate for the nonlinear time series noise-reduction. The simulation results shows that better noise-reduction performance is acquired when the signal to noise ratio is 12dB.