Proceedings of the 2013 International Conference on Software Engineering and Computer Science

Modulation mode Recognition based on multi-class classification of support vector machine

Authors
Qian Ren, Guangmin Sun, Yuanyuan Zhang
Corresponding Author
Qian Ren
Available Online September 2013.
DOI
10.2991/icsecs-13.2013.32How to use a DOI?
Keywords
Support vector machine; Modulation recognition; Multi-class classification
Abstract

An analog and digital modulation recognition method based on support vector machine (SVM) is proposed. A multi-class classifier is designed through the reasonable use of SVM multi-class classification method. A comparison for the performances of one-against-all (OAA), one-against-one (OAO) and binary tree (BT) with the different kernels of SVM is made. Experimental results show that the Gaussian radial basis function (GRBF) kernel has better performance than others. It can be seen from the simulation result that the proposed method is correct and efficient. The scheme can achieve 93% recognition accuracy at low levels of SNR. (Abstract)

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2013 International Conference on Software Engineering and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
September 2013
ISBN
10.2991/icsecs-13.2013.32
ISSN
1951-6851
DOI
10.2991/icsecs-13.2013.32How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Qian Ren
AU  - Guangmin Sun
AU  - Yuanyuan Zhang
PY  - 2013/09
DA  - 2013/09
TI  - Modulation mode Recognition based on multi-class classification of support vector machine
BT  - Proceedings of the 2013 International Conference on Software Engineering and Computer Science
PB  - Atlantis Press
SP  - 150
EP  - 154
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsecs-13.2013.32
DO  - 10.2991/icsecs-13.2013.32
ID  - Ren2013/09
ER  -