NDLI: Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM

Content Provider	IEEE Xplore Digital Library
Author	Cheol-Sun Park Jun-Ho Choi Sun-Phil Nah Won Jang Dae Young Kim
Copyright Year	2008
Description	Author affiliation: EW Lab., Agency for Defense Dev., Taejon (Cheol-Sun Park; Jun-Ho Choi; Sun-Phil Nah; Won Jang)
Abstract	This paper presents modulation classification method capable of classifying incident digital signals without a priori information using WT key features and SVM. These key features for modulation classification should have good properties of sensitive with modulation types and insensitive with SNR variation. In this paper, the 4 key features using WT coefficients, which have the property of insensitive to the changing of noise, are selected. The numerical simulations using these features are performed. We investigate the performance of the SVM-DDAG classifier for classifying 8 digitally modulated signals using only 4 WT key features (i.e., 4 level scale), and compare with that of decision tree classifier to adapt the modulation classification module in software radio. Results indicated an overall success rate of 95% at the SNR of 10dB in SVM-DDAG classifier on an AWGN channel.
Starting Page	387
Ending Page	390
File Size	1824363
Page Count	4
File Format	PDF
ISBN	9788955191363
ISSN	17389445
DOI	10.1109/ICACT.2008.4493784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-02-17
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	ETRI, NIA
Subject Keyword	Digital modulation Support vector machines Classification tree analysis Support vector machine classification Signal to noise ratio Additive white noise Gaussian noise Numerical simulation Decision trees Software radio Decision Tree (DT) Modulation Classification (MC) Wavelet Transformation (WT) Support Vector Machine (SVM) Decision Directed Acyclic Graph (DDAG)
Content Type	Text
Resource Type	Article

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