Abstract
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.
Highlights
The constant modulus (CM) criterion [1, 2, 3] is a broadly studied blind equalization technique
These works pointed out two aspects that deserve to be highlighted [3, 4]: (1) the CM cost function is multimodal; (2) there is an intimate relationship between CM minima and some Wiener optima
In order to evaluate the performance of the opt-aiNet algorithm when applied to search for the optimal Wiener equalizers, three different channels (C1, C2, and C3) were
Summary
The constant modulus (CM) criterion [1, 2, 3] is a broadly studied blind equalization technique. It is possible to make a strong claim: if one can determine the CM global minima, the best possible Wiener receiver can be evaluated This suggestion opens an exciting perspective: the possibility of obtaining the best equalizer (in the mean square error sense) without a desired signal, that is, by using a blind or unsupervised search strategy. To achieve this goal, it is necessary to propose a method capable of locating, over a set of local minima, the best CM minimum in most of the runs performed by the algorithm.
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