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

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.
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https://hal.archives-ouvertes.fr/hal-00417609
Contributor : Thomas Bouilloc <>
Submitted on : Wednesday, September 16, 2009 - 1:32:48 PM
Last modification on : Friday, April 12, 2019 - 4:22:17 PM

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  • HAL Id : hal-00417609, version 1

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Alain Y. Kibangou, Gérard Favier. Blind equalization of nonlinear channels using a tensor decomposition with code/space/time diversities. Signal Processing, Elsevier, 2009, 89 (2), pp.133-143. ⟨hal-00417609⟩

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