In this paper, automatic classification of QAM signals including 64-state QAM and 256state QAM is discussed Three layer neural networks whose input data is the histogram distribution of instantaneous amplitude at symbol points is used for the classification. The evaluations of classdication performance are canid out for both cases in which the synchronization of symbol timing is assured at the receiver and not assured Good classification results are obtained by the computer simulations at SNR2lodB. The influence of the number of symbol points which are used for the calculation of histogram is also dmussed
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.