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Identification of Fifth-order Wiener and Hammerstein Channels based on the Estimation of an Associated Volterra Kernel

by Zouhour Ben Ahmed, G´erard Favier, Nabil Derbel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 7
Year of Publication: 2015
Authors: Zouhour Ben Ahmed, G´erard Favier, Nabil Derbel
10.5120/ijca2015905405

Zouhour Ben Ahmed, G´erard Favier, Nabil Derbel . Identification of Fifth-order Wiener and Hammerstein Channels based on the Estimation of an Associated Volterra Kernel. International Journal of Computer Applications. 123, 7 ( August 2015), 1-5. DOI=10.5120/ijca2015905405

@article{ 10.5120/ijca2015905405,
author = { Zouhour Ben Ahmed, G´erard Favier, Nabil Derbel },
title = { Identification of Fifth-order Wiener and Hammerstein Channels based on the Estimation of an Associated Volterra Kernel },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 7 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number7/21968-2015905405/ },
doi = { 10.5120/ijca2015905405 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:59.962944+05:30
%A Zouhour Ben Ahmed
%A G´erard Favier
%A Nabil Derbel
%T Identification of Fifth-order Wiener and Hammerstein Channels based on the Estimation of an Associated Volterra Kernel
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 7
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we consider the problem of identification of fifth-order Wiener and Hammerstein nonlinear communication channels using the estimation of an associated Volterra kernel. We exploit the special form of the fifth-order associated Volterra kernel for deriving two algorithms that allow to estimate the parameters of the linear part of these channels. In the case of a Wiener channel, the associated Volterra kernel is a tensor satisfying a rank-one PARAFAC decomposition whose the parameters can be estimated by means of an alternating least squares (ALS) algorithm. In the case of a Hammerstein channel, its associated Volterra kernel is a diagonal tensor, which leads to a closed-form solution for estimating the parameters of the linear block. The coefficients of the nonlinear block modeled as a fifth degree polynomial are then estimated by means of the standard non recursive least squares (LS) algorithm. The performance of the proposed identification methods is illustrated by means of Monte Carlo simulation results.

References
  1. N.J. Bershad, P. Celka, and S. Mclaughlin. Analysis of stochastic gradient identification of Wiener-Hammerstein systems for nonlinearities with Hermite polynomial expansions. IEEE Trans. on Signal Processing, 49(5) :1060–1072, May 2001.
  2. M. Boizard, J.H. Goulart, R. Boyer, G. Favier, and P. Comon. Statistical efficiency of structured CPD estimation applied to Wiener-Hammerstein modeling. Proc. European Signal Process. Conf. (EUSIPCO), Nice, France, Aug 2015.
  3. T. Bouilloc and G. Favier. Nonlinear channel modeling and identification using bandpass Volterra-PARAFAC models. Signal Processing, Elsevier, 92(6) :1492–1498, June 2012.
  4. X. Chen, H.T Fang, and X Wang. Subspace identification for Wiener systems with general nonlinearity. 30th Chinese Control Conference (CCC), Yantai, China, pages 1696–1701, 2011.
  5. G. Favier. Nonlinear system modeling and identification using tensor approaches. 10th International conference on Sciences and Techniques of Automatic control and computer engineering (STA’2009), Hammamet, Tunisie, Dec 2009.
  6. G. Favier, A. Kibangou, and Bouilloc T. Nonlinear system modeling and identification using Volterra-PARAFAC models. Int. J. of Adaptive Control and Sig. Proc., 26 :30–53, 2012.
  7. G. Favier and A. Y. Kibangou. Tensor-based methods for system identification. 9th International conference on Sciences and Techniques of Automatic control and computer engineering (STA’2008), Sousse, Tunisie, 3(1) :840–869, Dec 2008.
  8. X.N. Fernando and A.B. Sesay. Fiber wireless channel estimation using correlation properties of PN sequences. Canadian Journal of Electrical and Computer Enginneering, 26(2), April 2001.
  9. R. Haber and L. Keviczky. Nonlinear system identification - Input-output modeling approach, vol. 1 of Mathematical modelling: theory and applications. Kluwer Academy Publishers, 1999.
  10. R. A. Harshman. Foundations of the PARAFAC procedure : Model and conditions for an “explanatory” multi-mode factor analysis. UCLA Working Papers in Phonetics, 16 :1–84, Dec. 1970.
  11. A. Y. Kibangou and G. Favier. Matrix and tensor decompositions for identification of block-structured nonlinear channels in digital transmission systems. Signal Processing Advances in Wireless Communications (SPAWC), Recife, Brazil, pages 281–285, July 2008.
  12. A. Y. Kibangou and G. Favier. Identification of fifth-order Volterra systems using i.i.d. inputs. IET Signal Processing, 4(1) :30–44, Feb 2010.
  13. M.J. Korenberg and I.W. Hunter. The identification of nonlinear biological systems : LNL cascade models. Biological Cybernetics, pages 125–134, 1986.
  14. F. Le, I. Markovsky, C. Freeman, and E. Rogers. Recursive identification of Hammerstein structure. In, 18th IFAC World Congress, Milan, Italy, August 28 - September 2 2011.
  15. V. Z. Marmarelis. Nonlinear dynamic modeling of physiological systems. IEEE Press, John Wiley & Sons, 2004.
  16. M. Pouliquen, F. Giri, O. Gehan, E. Pigeon, M. Frikel, and B Targui. Subspace identification for Hammerstein systems with nonparametric input backlash and switch nonlinearities. Conference on Decision and Control, Firenze, Italy, pages 4302–4307, 2013.
  17. R. Raich. Nonlinear System Identification and Analysis with Applications to Power Amplifier Modeling and Power Amplifier Predistortion. PhD thesis, School of Electrical and Computer Engineering Georgia Institute of Technology, 2004.
  18. C.H. Tseng and E.J. Powers. Identification of Nonlinear Channels in Digital Transmission Systems. Proc. IEEE Signal Processing Workshop on Higher-order Statistics, South Lake Tahoe, CA,, pages 42–45, June 1993.
  19. J. Voros. Recursive identification of Wiener system with two-segment polynomial nonlinearities. J. Electr. Engin,59(1) :40–44, 2008.
  20. G.T. Zhou and R. Raich. Spectral analysis of polynomial nonlinearity with applications to RF power amplifiers. EURASIP Journal on Applied Signal Processing, 12 :1831–1840, 2004.
  21. L. Zhou, X. Li, and F. Pan. Least-squares-based iterative identification algorithm for Wiener nonlinear systems. Journal of Applied Mathematics, 2013 :1–6, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Wiener and Hammerstein models Volterra kernels PARAFAC decomposition Channel estimation ALS algorithm

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