Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model

This work proposes a framework to determine the optimalWiener equalizer by using an artificial immune networkmodel 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 ofmultimodal search, being capable of determining the optimalWiener equalizer in most runs for all tested channels.