DocumentCode :
2467971
Title :
An Optimal Choice Method for Recognition Characteristics of Digital Modulation Signal
Author :
Zhao, Lei ; Li, Bingbing ; Liu, Mingqian
Author_Institution :
State Key Lab. of Integrated Service Networks, Xidian Univ., Xi´´an, China
fYear :
2010
fDate :
17-19 Dec. 2010
Firstpage :
1358
Lastpage :
1361
Abstract :
In the recognition of digital modulation signals, extracting the characteristics that more easily distinguish the source signals, and have better performance of the signal, the source signals can be identified by using these characteristics. This paper optimized the original 20 features using orthogonal experiment method, and then identified the signals by the neural network, finally compared its results with that of PCA(principal component analysis) and KPCA(kernel principal component analysis) methods. Experimental results show that orthogonal experiment method in Gaussian and multipath environment can optimize the selection of the features to achieve higher recognition rate than the original features. Orthogonal experiment method has a better ability than PCA and KPCA methods in the optimal choice of these 20 characteristics.
Keywords :
Gaussian processes; principal component analysis; signal processing; Gaussian; KPCA; digital modulation signal recognition; kernel principal component analysis; neural network; optimal choice method; orthogonal experiment method; source signals; Character recognition; Feature extraction; Kernel; OFDM; Optimization; Principal component analysis; Signal to noise ratio; characteristic parameters; modulation recognition; optimal choice; orthogonal experiment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
Type :
conf
DOI :
10.1109/ICCIS.2010.330
Filename :
5709536
Link To Document :
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