Clustering blind convergence process in an adaptive decision feedback equalizer

Abstract

A blind convergence process for an adaptive decision feedback equalizer that includes an quadrature amplitude modulation (QAM) slicer having a index n, a forward filter defined by a plurality of forward tap coefficients and a feedback filter defined by a plurality of feedback tap coefficients. The blind convergence process includes initializing the forward tap coefficients of the forward filter and the feedback tap coefficients of the feedback filters with predetermined values. The slicer operates in two modes: a clustering mode and a decision directed mode. In the clustering mode the following steps are performed: (1) update the forward tap coefficients of the forward filter using an error estimate (e.g. Sato error), and (2) update the feedback tap coefficients of the feedback filter using an error estimate (e.g. Sato error). In the decision directed mode the following steps are performed: (1) update the forward tap coefficients of the forward filter for a prescribed QAM index n using actual error, and (2) update the feedback tap coefficients of the feedback filter for a prescribe QAM index n using actual error. This process enables blind convergence of an equalizer for a wide range of QAM indexes (e.g. 4-256).

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