Relative-gradient Bussgang-type blind equalization algorithms

Abstract

In blind equalization (BE) a cost function based on the fit between the equalizer outputs and the signaling constellation is generally defined. To minimize such a cost function, standard gradient descent learning is commonly used. We exploit the idea of relative gradient (RG) learning to modify such standard Bussgang-type algorithms. Instead of one output each time, our method uses a sliding block of outputs. Our RG-based block Bussgang algorithms have faster convergence than corresponding Bussgang algorithms based on the standard gradient.