A soft decision-directed LMS algorithm for blind equalization

An adaptation algorithm for equalizers operating on very distorted channels is presented. The algorithm is based on the idea of adjusting the equalizer tap gains to maximize the likelihood that the equalizer outputs would be generated by a mixture of two Gaussians with known means. The decision-dire...

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Veröffentlicht in:	IEEE transactions on communications 1993-02, Vol.41 (2), p.275-279
Hauptverfasser:	Nowlan, S.J., Hinton, G.E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Approximation algorithms Blind equalizers Decision feedback equalizers Delay systems Detection, estimation, filtering, equalization, prediction Digital communication Exact sciences and technology Gaussian processes Information, signal and communications theory Least squares approximation Nonlinear distortion Signal and communications theory Signal, noise Telecommunications and information theory Transversal filters
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Beschreibung
Zusammenfassung:	An adaptation algorithm for equalizers operating on very distorted channels is presented. The algorithm is based on the idea of adjusting the equalizer tap gains to maximize the likelihood that the equalizer outputs would be generated by a mixture of two Gaussians with known means. The decision-directed least-mean-square algorithm is shown to be an approximation to maximizing the likelihood that the equalizer outputs come from such an independently and identically distributed source. The algorithm is developed in the context of a binary pulse-amplitude-modulation channel, and simulations demonstrate that the algorithm converges in channels for which the decision-directed LMS algorithms does not converge.< >
ISSN:	0090-6778 1558-0857
DOI:	10.1109/26.216497