Blind Channel Equalization with Amplitude Banded Godard and Sato Algorithms

The least-mean-squares (LMS) algorithm which updates the filter coefficients by a stochastic gradient descent approach 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 non-linear properties of the AB technique with LMS algorithm are inherited into the ABGodard and ABSato algorithms, resulting in an improvement of equalization performance. These properties are validated from a signal separation aspect based on decision boundary. Mean square error (MSE) and bit error rate (BER) are investigated on several communication channel models. Observations on simulations show that the ABGodard and ABSato algorithms provide better performance than the standard Godard and Sato algorithms, respectively, and that the ABSato algorithm is superior to the ABGodard algorithm. As the division number used for the AB technique is increased, the MSE and BER performances of the ABSato algorithm are improved. A parallel structure of the Sato and ABSato algorithms provides a further improvement of the MSE and BER performances.