BLIND ADAPTIVE E AEIZATION USING
Vijitha Weerackody, S.A. Kassam · 1990
Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. The Godard Algorithm and the Generalized Sato Algorithm are two widely referenced algorithms for blind equalization of a QAM data system. These algorithms exhibit very slow convergence rates when compared to algorithms employed in conventional data-aided equalization schemes. In order to speed up the convergence process, these algorithms may be switched over to a decision-directed equalization scheme once the error level is reasonably low. In this paper we present a scheme which is capable of operating in two modes: blind equalization mode and a mode similar to the decision-directed equalization mode. In this proposed scheme, the dominant mode of operation changes from the blind equalization mode at higher error levels to the mode similar to the decision-directed equalization mode at lower error levels. Manual switch-over is not necessary since transitions between the two modes take place smoothly and automatically. The proposed scheme results in faster convergence rates and is applicable to a general class of stochastic-gradient type blind equalization algorithms.