Investigation of automatic analog modulation classification algorithms

Author

Kabasakal, Mehmet ; Toker, Cenk

Author_Institution

Sistem Muhendisligi Mudurlugu, MIKES A.S., Ankara, Turkey

fYear

2012

fDate

18-20 April 2012

Firstpage

1

Lastpage

4

Abstract

In this paper, feature based modulation classifiers are investigated for AM, DSB, LSB, USB and FM analog modulations methods. Instantaneous phase, magnitude and spectrum of the signals to be classified are used for the calculation of the key features. At the decision step decision tree, minimum distance classifier and support vector machines are used. Then the performance of the developed classifiers is assessed through computer simulations and the decision tree classifier is realized on an USRP software radio platform.

Keywords

decision trees; modulation; pattern classification; signal classification; software radio; support vector machines; AM analog modulations methods; DSB analog modulations methods; FM analog modulations methods; LSB analog modulations methods; USB analog modulations methods; USRP software radio platform; automatic analog modulation classification; computer simulations; decision step decision tree; feature based modulation classifiers; minimum distance classifier; support vector machines; Decision trees; Frequency modulation; Signal to noise ratio; Software radio; Support vector machines; Universal Serial Bus;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2012 20th

Conference_Location

Mugla

Print_ISBN

978-1-4673-0055-1

Electronic_ISBN

978-1-4673-0054-4

Type

conf

DOI

10.1109/SIU.2012.6204538

Filename

6204538