Adaptive Blind Equalization Using Second- and Higher Order Statistics

Fang B. Ueng, Yu-Ted Su

Research output: Contribution to journalArticle

5 Scopus citations


This paper presents two classes of adaptive blind algorithms based on second- and higher order statistics. The first class contains fast recursive algorithms whose cost functions involve second and third- or fourth-order cumulants. These algorithms are stochastic gradient-based but have structures similar to the fast transversal filters (FTF) algorithms. The second class is composed of two stages: the first stage uses a gradient adaptive lattice (GAL) while the second stage employs a higher order-cumulant (HOC) based least mean squares (LMS) filter. The computational loads for these algorithms are all linearly proportional to the number of taps used. Furthermore, the second class, as various numerical examples indicate, yields very fast convergence rates and low steady state mean square errors (MSE) and intersymbol interference (ISI). MSE convergence analyses for the proposed algorithms are also provided and compared with simulation results.

Original languageEnglish
Pages (from-to)132-140
Number of pages9
JournalIEEE Journal on Selected Areas in Communications
Issue number1
StatePublished - 1 Jan 1995

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