Amplitude-based neuro-classifier for classification of digital quadrature and staggered modulations

Author

Delgosha, Farshid ; Menhaj, Mohammad B.

Volume

1

fYear

2001

fDate

2001

Firstpage

721

Abstract

Recognition of the modulation type has some commercial and military applications. In this paper, we present a new classifier to recognize QPSK, SQPSK and MSK modulations from each other. This classifier, its main part is a neural network, makes use of two features that are based on the amplitude characteristics of the considered modulations. The employed features are easily computed. In the proposed classifier, no synchronization with arrival time of the received signal is needed. If the SNR of the transmitted signal is kept fixed in transmitter, the proposed classifier will be insensitive to the exact value of channel noise power. These properties make our classifier very robust

Keywords

amplitude modulation; minimum shift keying; neural nets; noise; pattern classification; quadrature phase shift keying; signal classification; telecommunication channels; telecommunication computing; MSK modulations; QPSK modulations; SNR; SQPSK modulations; amplitude characteristics; amplitude-based neuro-classifier; channel noise power insensitivity; digital quadrature classification; modulation type recognition; neural network; staggered modulations; Amplitude modulation; Digital modulation; Frequency shift keying; Military computing; Neural networks; Phase modulation; Pulse modulation; Quadrature amplitude modulation; Quadrature phase shift keying; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939113

Filename

939113