NDLI: New results on a two-stage novel modulation classification technique for cognitive radio applications

Content Provider	IEEE Xplore Digital Library
Author	Azarmanesh, O. Bilen, S.G.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, The Pennsylvania State University, University Park, 16802 USA (Azarmanesh, O.; Bilen, S.G.)
Abstract	We report here on new results for a novel modulation classification technique designed for a real-time cognitive radio (CR) system. The goal is a technique that is robust and efficient with a processing time overhead low enough to allow a CR to maintain its real-time operating objectives. We investigated classification of linear single-carrier modulations as well as multi-carrier modulations. The method uses the waveform's I-Q diagrams, and by employing clustering algorithms on them, determines the type of modulation being transmitted. For classifying single-/multi-carrier modulations, we further study existing methods to find and modify the appropriate Gaussianity test to distinguish between single-carrier signals and multi-carrier signals. This new modulation classification method is capable of determining the type of modulation scheme among various PAM, QAM, and PSK modulations. Its performance in classifying these modulations is comparable to the best available published classifiers. It is also capable of extracting features of OFDM modulations and can be further expanded to include any new modulation scheme with unique I-Q diagram.
Starting Page	266
Ending Page	271
File Size	516227
Page Count	6
File Format	PDF
ISBN	9781467300797
ISSN	21557578
e-ISBN	9781467300810
DOI	10.1109/MILCOM.2011.6127673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Modulation OFDM Clustering algorithms Signal to noise ratio Time frequency analysis Feature extraction Error analysis
Content Type	Text
Resource Type	Article

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