A Novel Method of Modulation Classification for Digital Signals

Author

Huo, Lei ; Duan, Tiandong ; Fang, Xiangqian

Author_Institution

Inf. Eng. Univ., Zheng Zhou

fYear

0

fDate

0-0 0

Firstpage

2435

Lastpage

2438

Abstract

This paper introduces a novel method of modulation classification for digitally modulated signals in the presence of additive white Gaussian noise (AWGN). This method does not need any prior knowledge of the signals, such as SNR (Signal-to-Noise Ratio), symbol rate and carrier frequency. Four kinds of features are extracted to achieve a tree-based classification approach, and three radial basis function (RBF) neural networks are employed in the classifier. The computer simulation is carried out and the results are presented.

Keywords

AWGN; feature extraction; modulation; radial basis function networks; signal processing; trees (mathematics); AWGN; RBF neural networks; additive white Gaussian noise; carrier frequency; computer simulation; digital signals; feature extraction; modulation classification method; radial basis function; signal-to-noise ratio; tree-based classification approach; AWGN; Additive white noise; Bandwidth; Classification tree analysis; Digital modulation; Feature extraction; Frequency shift keying; Neural networks; OFDM; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9490-9

Type

conf

DOI

10.1109/IJCNN.2006.247070

Filename

1716420