Self-learning deconvolution using a cascade of magnitude and phase equalizers
Carlos A. F. da Rocha, João Marcos Travassos Romano, Odile Macchi · 2002
In this work, we propose a non-linear structure for self-learning equalization, which can be easily updated using the direct-decision error criterion. Such a structure consists of three different systems: an IIR predictor that provides the magnitude equalization, an automatic gain control and a non-linear phase equalizer. The paper presents a theoretical analysis for the proposed structure and some simulation results with severe channels.