摘要:
A generalized form of a multimodulus algorithm (GMMA) is described for use in blind equalization. A receiver uses a signal point constellation representing 256 levels. This signal space is divided into sample, or data, subsets. Cost function minimization is done with respect to each sample subset.
摘要:
A blind convergence technique is restricted to using a subset of equalizer output samples. Illustratively, a receiver implements a windowed MMA approach. In this windowed MMA approach, a sample window overlays the two-dimensional plane representing the set of equalizer output samples. Only those equalizer output samples appearing within the sample window are used during filter adaptation.
摘要:
A blind equalization technique uses both the "constant modulus algorithm" (CMA) and the "multimodulus algorithm" (MMA) during blind start-up. This approach provides the basis for a "transition algorithm." One example of a transition algorithm is the CMA-MMA transition algorithm in which an adaptive filter simply switches from CMA to MMA. Other examples are variations of the CMA-MMA transition algorithm and are illustrated by the "Constant Rotation CMA-MMA" transition algorithm and the "Dynamic Rotation CMA-MMA" transition algorithm.
摘要:
A blind equalization technique - the multimodulus algorithm (MMA) - adapts coefficients of an equalizer so as to minimize dispersion of the output samples, of the equalizer, around piecewise linear contours of a signal space. The MMA technique is illustrated in the context of both square and non-square signal point constellations.
摘要:
A blind convergence technique is restricted to using a subset of equalizer output samples. Illustratively, a receiver implements a windowed MMA approach. In this windowed MMA approach, a sample window overlays the two-dimensional plane representing the set of equalizer output samples. Only those equalizer output samples appearing within the sample window are used during filter adaptation.
摘要:
A blind equalization technique - the multimodulus algorithm (MMA) - adapts coefficients of an equalizer so as to minimize dispersion of the output samples, of the equalizer, around piecewise linear contours of a signal space. The MMA technique is illustrated in the context of both square and non-square signal point constellations.
摘要:
A decision feedback equalizer (DFE) comprises a feed-forward filter and a feedback filter. Blind training of the DFE is performed using a statistical-based tap updating algorithm for the feed-forward filter, and a symbol-based type of tap updating algorithm for the feedback filter.
摘要:
An adaptive equalizer (e.g., 100, 950) having a digital filter (e.g., 400) including filter tap coefficients; a slicer (e.g., 200); and a filter tap coefficient update block (e.g., 300). The filter (e.g., 400), slicer (e.g., 200) and coefficient update block (e.g., 300) are configured so as to perform at least one burst update of the filter tap coefficients. Further, a method of updating the filter tap coefficients of an adaptive equalizer (e.g., 100, 950) comprises the step of: performing at least one burst update of the filter tap coefficients.