Welcome to the InfoSci Platform
Reference Hub3
Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

Hui Wang, Li Li Guo, Yun Lin
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 22
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781522511977|DOI: 10.4018/IJMCMC.2017070107
Cite Article Cite Article

MLA

Wang, Hui, et al. "Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection." IJMCMC vol.8, no.3 2017: pp.90-111. http://doi.org/10.4018/IJMCMC.2017070107

APA

Wang, H., Guo, L. L., & Lin, Y. (2017). Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 8(3), 90-111. http://doi.org/10.4018/IJMCMC.2017070107

Chicago

Wang, Hui, Li Li Guo, and Yun Lin. "Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 8, no.3: 90-111. http://doi.org/10.4018/IJMCMC.2017070107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.