Automatic classification of QAM signals by neural networks

Author

Taira, Shintaro

Author_Institution

Tech. Res. & Dev. Inst., Japan Defense Agency, Japan

Volume

2

fYear

2001

fDate

2001

Firstpage

1309

Abstract

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⩾10 dB. The influence of the number of symbol points which are used for the calculation of the histogram is also discussed

Keywords

demodulation; neural nets; quadrature amplitude modulation; signal classification; statistical analysis; synchronisation; telecommunication computing; 256-QAM; 64-QAM; QAM signals; automatic classification; histogram distribution; input data; instantaneous amplitude; receiver; symbol points; symbol timing; synchronization; three layer neural networks; Classification algorithms; Demodulation; Digital modulation; Frequency synchronization; Histograms; Neural networks; Pattern recognition; Quadrature amplitude modulation; Signal processing; Timing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on

Conference_Location

Salt Lake City, UT

ISSN

1520-6149

Print_ISBN

0-7803-7041-4

Type

conf

DOI

10.1109/ICASSP.2001.941166

Filename

941166