Automatic classification of QAM signals by neural networks
S. Taira · 2002
In this paper, automatic classification of QAM signals including 64-state QAM and 256-state QAM is discussed. Three layer neural networks whose input data are the histogram distribution of instantaneous amplitude at symbol points are used for the classification. The evaluation of the classification performance is carried 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 SNR/spl ges/10 dB. The influence of the number of symbol points which are used for the calculation of the histogram is also discussed.