Blind Adaptation of Decision Feedback Equalizers: Gross Convergence Properties*

An analysis of the stochastic dynamics of the blind adaptation of decision feedback equalizers is presented. The analysis accounts for the presence of decision errors which, under feedback, are propagated. A number of blind algorithms are presented and a theory is developed to explain gross convergence properties observed through simulations. The possibility of and mechanism behind undesirable local minima are highlighted and a detailed case study is given. The potential capture by local minima shows the importance of good initialization. These results superficially resemble those obtained for blind adaptation applied to linear equalizers.