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Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiple-channel applications. In this paper, we investigate the suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind deconvolution of linear channels. Comparisons between maximum and minimum entropy approaches, as well as the effect of entropy order, equalizer length, sample size, and measurement noise on performance, will be investigated through Monte Carlo simulations. The results indicate that this nonparametric entropy estimation approach outperforms the standard Bell-Sejnowski and normalized kurtosis algorithms in blind deconvolution. In addition, the solutions using Shannon's entropy were not optimal either for super- or sub-Gaussian source densities.