Abstract
For transmission of digital data over a linear channel with additive white noise, it can be shown that the optimal symbol-decision equalizer is nonlinear. The Kernel Adaline algorithm, a nonlinear generalization of Widrow's and Hoff's (1960) Adaline, is capable of learning arbitrary nonlinear decision boundaries while retaining the desirable convergence properties of the linear Adaline. This work investigates the use of the Kernel Adaline as an equalizer for such transmission channels. We show that the performance of the Kernel Adaline approaches that of the optimal symbol-decision equalizer given by Bayes theory and further, still produces useful results when the additive noise is nonwhite. A description and preliminary results of an adaptive version of the Kernel Adaline are also presented.
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