An Improved Recursive Least Square Algorithm For Adapting Fuzzy Channel Equalizer

Abstract

Adaptive filters have been thoroughly investigated in digital communication. They are especially exploited as equalizers, to compensate for channel distortions, although equalizers based on linear filters perform poorly in nonlinear distortion. In this paper, a nonlinear equalizer based on a fuzzy filter is proposed and a new algorithm for the adaptation parameters is presented. The followed approach is based on a regularization of the Recursive Least Square (RLS) algorithm and an incorporation of fuzzy rules in the adaptation process. The proposed approach, named Improved Fuzzy Recursive Least Square (IFRLS), enhances significantly the fuzzy equalizer performance through the acquisition of more convergence properties and lower steady-state Mean Square Error (MSE). The efficiency of the IFRLS algorithm is confirmed through extensive simulations in a nonlinear environment, besides the conventional RLS, in terms of convergence abilities, through MSE, and the equalized signal behavior. The IFRLS algorithm recovers the transmitted signal efficiently and leads to lower steady-state MSE. An improvement in convergence abilities is noticed, besides the RLS.