Amplitude banded technique and parallel structure for Godard and Sato algorithms of blind channel equalization

The least-mean-squares (LMS) algorithm which updates the filter coefficients by gradient-based method is the most popular adaptive filtering one. In this paper we propose a novel amplitude banded (AB) technique with LMS on Godard (ABGodard) and Sato (ABSato) algorithms for the equalization of communication channels. The nonlinear properties of the AB technique with LMS algorithm are inherited into the ABGodard and ABSato algorithms, resulting in an improvement of equalization performance in favor of the ABSato algorithm. Mean square error (MSE) as well as bit error rate (BER) are investigated on a simple communication channel model. Observations on simulations show that ABGodard and ABSato algorithms provide better performance than the standard Godard and Sato algorithms, respectively. The parallel structure of the Sato and ABSato algorithms provides a further performance improvement. This is also observed from MSE and BER performances.