Blind identification of multiuser nonlinear channels using tensor decomposition and precoding

Authors:
Carlos Alexandre Fernandes;Gérard Favier;João Cesar M. Mota
Affiliations:
I3S Laboratory, University of Nice-Sophia Antipolis/CNRS, Les Algorithmes/Euclide B-2000, route des Lucioles, BP 121, 06903 Sophia-Antipolis Cedex, France and Departamento de Engenharia de Teleinf ...;I3S Laboratory, University of Nice-Sophia Antipolis/CNRS, Les Algorithmes/Euclide B-2000, route des Lucioles, BP 121, 06903 Sophia-Antipolis Cedex, France;Departamento de Engenharia de Teleinformática, Federal University of Ceará, Campus do Pici, 60.755-640, 6007 Fortaleza, Brazil
Venue:
Signal Processing
Year:
2009

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Abstract

This paper presents two blind identification methods for nonlinear memoryless channels in multiuser communication systems. These methods are based on the parallel factor (PARAFAC) decomposition of a tensor composed of channel output covariances. Such a decomposition is possible owing to a new precoding scheme developed for phase-shift keying (PSK) signals modeled as Markov chains. Some conditions on the transition probability matrices (TPM) of the Markov chains are established to introduce temporal correlation and satisfy statistical correlation constraints inducing the PARAFAC decomposition of the considered tensor. The proposed blind channel estimation algorithms are evaluated by means of computer simulations.