Blind Separation Of Convolutive Mixtures Over Galois Fields
Denis G. Fantinato, Daniel G. Silva, Everton Z. Nadalin, Aline Neves, Jugurta Montalvćo, Romis Attux, Joćo M. T. Romano

Abstract:
The efforts of Yeredor, Gutch, Gruber and Theis have established a theory of blind source separation (BSS) over finite fields that can be applied to linear and instantaneous mixing models. In this work, the problem is treated for the case of convolutive mixtures, for which the process of BSS must be understood in terms of space-time processing. A method based on minimum entropy and deflation is proposed, and structural conditions for perfect signal recovery are defined, establishing interesting points of contact with canonical MIMO equalization. Simulation results give support to the applicability of the proposed algorithm and also reveal the important role of efficient entropy estimation when the complexity of the mixing system is increased.