Blind identification of multiuser nonlinear channels using tensor decomposition and precoding

DOI10.1016/j.sigpro.2009.05.012zbMath1197.94049OpenAlexW2090147074MaRDI QIDQ1957708

Gérard Favier, João Cesar M. Mota, Carlos Estêvão R. Fernandes

Publication date: 27 September 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.05.012

zbMATH Keywords

Markov chainVolterra modelPARAFAC decompositionblind nonlinear channel identificationmultiuser channel

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (2)

PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems ⋮ Nonlinear system modeling and identification using Volterra‐PARAFAC models

Cites Work

• PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization
• Some further results on blind identification of MIMO FIR channels via second-order statistics
• Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
• Channel identification for high speed digital communications
• Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization
• Jacobi Angles for Simultaneous Diagonalization
• Blind spatial signature estimation via time-varying user power loading and parallel factor analysis

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