NDLI: A new scheme of automatic modulation classification using wavelet and WSVM

Content Provider	IEEE Xplore Digital Library
Author	Wu Dan Gu Xuemai Guo Qing
Copyright Year	2005
Description	Author affiliation: Commun. Res. Center, Harbin Inst. of Technol. (Wu Dan; Gu Xuemai; Guo Qing)
Abstract	This paper deals with automatic modulation classification of communication signals. A new scheme of automatic modulation classification using wavelet analysis and wavelet support vector machine (WSVM) is proposed. Further, a new way of training for wavelet features is carried out to adapt to signals which are non-stable and varied in a wide range of signal-to-noise rates (SNR). Through such training, a single classifier can classify modulation types with high accuracy without knowing signals' SNR if only the SNR is in a certain range. Computer simulation shows that the classifier can separate ten modulation types, i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, pi/4QPSK, OQPSK and success rates are over 96.5% when SNR is not lower than 3 dB. Accuracy and efficiency of the proposed scheme are obviously improved
Starting Page	5
Ending Page	5
File Size	231935
Page Count	1
File Format	PDF
ISBN	9810545738
DOI	10.1109/MTAS.2005.243757
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Mobility Organising Committee
Subject Keyword	modulation classification Computer simulation Artificial neural networks WASVM Wavelet analysis Pattern recognition kernel function Support vector machines Support vector machine classification Signal processing wavelet Feature extraction Kernel Signal to noise ratio
Content Type	Text
Resource Type	Article

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