Modulation classification using ARBF networks

Author

Tao, He ; Xiaorong, Jing

Author_Institution

Dept. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

3

fYear

2004

fDate

31 Aug.-4 Sept. 2004

Firstpage

1809

Abstract

A simple and robust method based on statistical pattern recognition theory to approach modulation type classification is proposed. The features being used for classifying digital signaling formats are the fourth-order and sixth-order cumulants of the received signal. An adaptive radial-basis function networks (ARBF) which tandem combine a single-layer RBF and a single-layer linear-basis function (LBF) networks is constructed as the classifier. Examples of classifying four modulation types - 4 ASK, 2 ASK/2 PSK, 4 PSK and 16 QAM - are given. T´he result of computer simulation has proved that this method can process well in a wide range of SNR and has a preferable generalization.

Keywords

adaptive signal processing; feature extraction; higher order statistics; phase shift keying; quadrature amplitude modulation; radial basis function networks; signal classification; ARBF network; adaptive radial-basis function network; digital signal classification; feature extraction; fourth-order cumulant; modulation classification; sixth-order cumulant; statistical pattern recognition theory; Adaptive systems; Artificial neural networks; Constellation diagram; Fault tolerance; Feature extraction; Frequency estimation; Jitter; Noise robustness; Pattern recognition; Timing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on

Print_ISBN

0-7803-8406-7

Type

conf

DOI

10.1109/ICOSP.2004.1442079

Filename

1442079