NDLI: Classification Using a Novel Combined Classifier for Digital Modulations in Digital Television Communication

Content Provider	IEEE Xplore Digital Library
Author	Zhong Gao Guanming Lu Daquan Gu
Copyright Year	2008
Abstract	With the rapid development of the communication technology, the communication environment becomes more and more complicated these years. Many signal modulation types are used simultaneously in digital TV communication systems. Therefore, a need arises for modulation classification that can automatically detect the incoming modulation type. In this paper, we propose a new approach for modulation classification, which uses a novel combined classifier based on multi-class support vector machine (SVM) and fuzzy integral to make the classification more suitable and accurate for signals in a wide range of signal to noise rate (SNR). Further, three efficient features with high robustness and less computation are extracted from intercepted signals to classify eleven digital modulation types. The experimental results show that the proposed scheme has the advantages of high accuracy and reliability.
Starting Page	913
Ending Page	916
File Size	252011
Page Count	4
File Format	PDF
ISBN	9780769535432
DOI	10.1109/WKDD.2009.219
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Digital modulation Support vector machine classification Feature extraction Educational institutions Data engineering Communications technology Data mining Digital TV Signal to noise ratio
Content Type	Text
Resource Type	Article

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