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Bussgang Blind Equalization Using Nonlinear Estimators with Reduced Computational Complexity  

Oh, Kil-Nam (Dept. of Information and Communications, Gwangju University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.42, no.6, 2005 , pp. 177-186 More about this Journal
Abstract
This paper introduces nonlinear estimators with reduced complexity, and proposes the Bussgang blind equalization algorithm employing the nonlinear estimators. The proposed algorithm utilized the facts that the Bayesian estimator is well approximated to the sigmoid estimator in initial stage of equalization with closed eye and is well approximated to the threshold estimator under open eye condition. The proposed method adopts selectively one of the two nonlinear estimators, i.e., the sigmoid estimator and the threshold estimator, according to channel distortion level at each iteration. As a result, by using the sigmoid estimator with reduced constellation, the proposed scheme, as it is applied to blind equalization of high-order QAM signals, simplifies the computational complexity extremely, and enhances the blind convergence capability and steady-state performance.
Keywords
Deconvolution; Bussgang blind equalization; Bayesian estimator; Zero-memory nonlinear estimator;
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