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/).
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 -