The use of artificial neural networks for classification of signal sources in cognitive radio systems

Abstract

In the paper, methods of classification of signal sources in cognitive radio systems that are based on artificial neural networks are discussed. A novel method for improving noise immunity of RBF networks is suggested. It is based on introducing an additional self-organizing layer of neurons, which ensures automatic selection of variances of basis functions and a significant reduction of the network dimension. It is shown that the use of auto-associative networks in the problem of the classification of sources of signals makes it possible to minimize the feature space without significant deterioration of its separation properties.