Open Access

Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

  • Abdelouahib Zaouche1Email author,
  • Iyad Dayoub2,
  • Jean Michel Rouvaen2 and
  • Charles Tatkeu1
EURASIP Journal on Advances in Signal Processing20082008:765462

Received: 5 November 2007

Accepted: 26 August 2008

Published: 2 December 2008


We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR) strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA) are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

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Authors’ Affiliations

IEMN DOAE, University of Valenciennes and Hainaut-Cambresis, Le Mont Houy


© Abdelouahib Zaouche et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.