Title :
Automatic classification of analog modulation schemes
Author :
Xiao, Haifeng ; Shi, Yun Q. ; Su, Wei ; Kosinski, John A.
Author_Institution :
Dept. of ECE, New Jersey Inst. of Technol., Newark, NJ, USA
Abstract :
This paper discusses automatic modulation classification (AMC) of analog schemes. Histograms of instantaneous frequency are used as classification features and Support Vector Machines (SVMs) are then applied to classify the unknown modulation schemes. This novel machine-learning based method can insure robustness in a wide range of SNR. Extensive simulation has demonstrated the validity of the proposed AMC algorithm. It is a practical algorithm in blind AMC environments.
Keywords :
amplitude modulation; frequency modulation; learning (artificial intelligence); pattern classification; support vector machines; telecommunication computing; AMC algorithm; SNR wide range; analog modulation schemes; automatic modulation classification; blind AMC environments; machine learning-based method; support vector machines; Classification algorithms; Frequency estimation; Frequency modulation; Histograms; Signal to noise ratio; Support vector machines; Automatic modulation classification; Support Vector Machine; analog modulation; histogram;
Conference_Titel :
Radio and Wireless Symposium (RWS), 2012 IEEE
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-1-4577-1153-4
DOI :
10.1109/RWS.2012.6175327
