Automatic classification of analog modulation schemes
Document Type
Conference Proceeding
Publication Date
5-11-2012
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. © 2012 IEEE.
Identifier
84860680749 (Scopus)
ISBN
[9781457711541]
Publication Title
Rww 2012 Proceedings IEEE Radio and Wireless Symposium Rws 2012
External Full Text Location
https://doi.org/10.1109/RWS.2012.6175327
First Page
5
Last Page
8
Recommended Citation
Xiao, Haifeng; Shi, Yun Q.; Su, Wei; and Kosinski, John A., "Automatic classification of analog modulation schemes" (2012). Faculty Publications. 18257.
https://digitalcommons.njit.edu/fac_pubs/18257
