Optimal decision boundaries for M-QAM signal formats using neural classifiers

The application of neural classifiers for providing optimal decision boundaries of a warped and clustered M-QAM constellation affected by nonlinearity is analyzed in this paper. The classifier behavior, for the specific application, has been evaluated both by the carrier to noise ratio (CNR) degradation (DeltaC/N) due to nonlinearity for a target error rate P(c)=10(-3), and more thoroughly by classical figures of merit of the pattern recognition theory such as classification confidence and generalization capability. The influence of the probability distribution of the training examples and the effects of activation functions' sharpness (namely the temperature of the net) have also been investigated. The results, obtained on a simulation basis, indicate optimal matching with respect to upper bounds evaluated with some minor simplifying hypothesis, even if the overall method's effectiveness can be adequate only for mild nonlinearity conditions.