Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources

Authors:
IváN DuráN-DíAz;Sergio Cruces;MaríA Auxiliadora Sarmiento-Vega;Pablo Aguilera-Bonet
Affiliations:
Escuela Técnica Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Camino de los descubrimientos s/n, 41092 Sevilla, Spain;Escuela Técnica Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Camino de los descubrimientos s/n, 41092 Sevilla, Spain;Escuela Técnica Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Camino de los descubrimientos s/n, 41092 Sevilla, Spain;Escuela Técnica Superior de Ingenieros, Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Camino de los descubrimientos s/n, 41092 Sevilla, Spain
Venue:
Neurocomputing
Year:
2011

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Abstract

This article presents a new algorithm for the blind extraction of communications sources (complex-valued sources) through the maximization of negentropy approximations based on nonlinearities. A criterion based on the square modulus of a nonlinearity of the output is used. We decouple the arguments of the criterion so that the algorithm maximizes it cyclically with respect to each argument by means of the Cauchy-Schwarz inequality. A proof of the ascent of the objective function after each iteration is also provided. Numerical simulations corroborate the good performance of the proposed algorithm in comparison with the existing methods.