Blind search for optimal Wiener equalizers using an artificial immune network model

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.2003874074

Chaotic phenomena in adaptive blind equalisers

The authors investigate the occurrence of chaotic phenomena in blind equalisers adapted by the constant modulus algorithm (CMA). It is shown that periodic and chaotic behaviour may take place during the update of the coefficients of the equaliser, for certain values of the adaptation step size, in both deterministic and stochastic versions of the algorithm. To study the stochastic CMA, an original theoretical framework is proposed, founded on a Markov chain-based modelling of the algorithm. The results reveal important features of the most useful technique for non-supervised equalisation. As far as is known, such convergence issues have not been properly explored in previous work.150636036