Kurtosis maximization for blind identification of nonlinear communication channels
نویسندگان
چکیده
This paper presents an original approach for blind deconvolution of a nonlinear communication channel using a criterion based on the secondand fourth-order moments of the input sequence. This approach is a simple extension of kurtosis maximization a method well known in a linear blind identification. We illustrate through a simple example that kurtosis maximization may also be used in a generalized Wiener-Hammerstein nonlinear systems identification process. The only constaint on probability distribution of the unobserved input process is non Gaussianity.
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