PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems

Authors:
Carlos A. R. Fernandes;Gérard Favier;João C. M. Mota
Affiliations:
Computer Engineering, Campus Sobral, Federal University of Ceará, Praça Senador Figueira, rua Anahid Andrade, S/N, Brazil;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:
2011

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Abstract

In this paper, a new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.