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 ''plain 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.