Proceedings PaperStop-and-go sign algorithms for blind equalization
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Stop-and-go adaptation rules that are used to improve the blind convergence characteristics of the conventional and sign decision-directed algorithms are proposed and examined. They are based on the so-called Sato and Godard type errors which are commonly used in blind deconvolution applications. The convergence rates achieved by different algorithms with QAM type constellations are compared. Also, optimal values for the parameters that are used in the Sato and Godard errors and their effect to the convergence of the stop-and-go schemes are investigated by means of analysis and computer simulations.