This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.
This article is part of the series Genetic and Evolutionary Computation for Signal Processing and Image Analysis.
Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model
1 DSPCOM, DECOM, FEEC, State University of Campinas, C.P. 6101, Campinas, SP Cep 13083-970, Brazil
2 DCA, FEEC, State University of Campinas, C.P. 6101, Campinas, SP Cep 13083-970, Brazil
EURASIP Journal on Advances in Signal Processing 2003, 2003:460216 doi:10.1155/S1110865703303014
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2003/8/460216
|Received:||28 June 2002|
|Revisions received:||1 December 2002|
|Published:||21 July 2003|
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