Blind equalization of nonlinear channels using a tensor decomposition with code/space/time diversities

Authors:
Alain Y. Kibangou;Gérard Favier
Affiliations:
LAAS, CNRS, Université de Toulouse, 7 av. colonel Roche, 31077 Toulouse Cedex, France;Laboratoire I3S, CNRS-UNSA, Les Algorithmes - Bít. Euclide B, 2000 Route des lucioles, B.P. 121 - 06903 Sophia Antipolis Cedex, France
Venue:
Signal Processing
Year:
2009

Citing 19
Cited 2

A bibliography on nonlinear system identification

Signal Processing - Special section on digital signal processing for multimedia communications and services
Multichannel and block based precoding methods for fixed point equalization of nonlinear communication channels

Signal Processing
Blind identification of under-determined mixtures based on the characteristic function

Signal Processing - Signal processing in UWB communications
Tensor-based techniques for the blind separation of DS-CDMA signals

Signal Processing
PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization

Signal Processing
Blind channel identification algorithms based on the Parafac decomposition of cumulant tensors: The single and multiuser cases

Signal Processing
A least-squares approach to blind channel identification

IEEE Transactions on Signal Processing
α-repetition/modulation and blind second-order identification

IEEE Transactions on Signal Processing
Blind PARAFAC receivers for DS-CDMA systems

IEEE Transactions on Signal Processing
Blind equalization of nonlinear channels from second-orderstatistics

IEEE Transactions on Signal Processing
Blind MIMO System Estimation Based on PARAFAC Decomposition of Higher Order Output Tensors

IEEE Transactions on Signal Processing
Linear multichannel blind equalizers of nonlinear FIR Volterrachannels

IEEE Transactions on Signal Processing
Blind channel identification and equalization using periodicmodulation precoders: performance analysis

IEEE Transactions on Signal Processing
Identifiability results for blind beamforming in incoherentmultipath with small delay spread

IEEE Transactions on Signal Processing
Random and pseudorandom inputs for Volterra filter identification

IEEE Transactions on Signal Processing
Transmitter induced cyclostationarity for blind channelequalization

IEEE Transactions on Signal Processing
PARAFAC-Based Blind Estimation Of Possibly Underdetermined Convolutive MIMO Systems

IEEE Transactions on Signal Processing
Constrained Tensor Modeling Approach to Blind Multiple-Antenna CDMA Schemes

IEEE Transactions on Signal Processing
Adaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission

IEEE Journal on Selected Areas in Communications

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

Signal Processing
Blind constrained block-Tucker2 receiver for multiuser SIMO NL-CDMA communication systems

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

In this paper, we consider the blind equalization problem for nonlinear channels represented by means of a Volterra model. We first suggest a precoding scheme inducing a three-dimensional (3-D) structure for the received data due to code, space, and time diversities. The tensor of received data admits a PARAFAC (parallel factors) decomposition with finite alphabet and Vandermonde structure constraints. We derive a uniqueness result taking such constraints into account. When one of the matrix factors, the code matrix, is known or belongs to a known finite set of matrices, we give new uniqueness results and three equalization algorithms are proposed. The performances of these algorithms are illustrated by means of simulation results.