Adaptive Equalizer Using Self-Generating Radial Basis Function Network

Abstract

In digital communication systems, a linear transversal equalizer was applied to signal equalization. But because of the nonlinearity of the equalization problem, it was desirable to incorporate some nonlinearity in the adaptive equalizer structure. We considered the application of the radial basis function (RBF) network to the adaptive equalizer and compared the performance of the equalizer using an RBF network between the maximum absolute error (MAE) selection method and the orthogonal least squares (OLS) method as a learning procedure. By comparing the MAE method with the OLS method, we show that the MAE method can achieve a more efficient performance in terms of bit error rate with fewer basis functions than the OLS method.