Automatic identification of digital modulation types
Signal Processing
Digital modulation classification using constellation shape
Signal Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Automatic signal type recognition plays an important role in various applications. In this paper we have proposed a pattern recognition method for identification of digital signal types. In this technique a suitable combination of the higher order moments (up to eighth) and the higher order cumulants (up to eight) and spectral features are proposed a the effective features. As the classifier we have proposed a multi-class support vectors machine (SVM) based classifier that is constructed via one-against-all method. We have examined the different kernels of SVMs and compare the performances of them for automatic digital signal type identification. Experimental results show that the Gaussian radial basis function (GRBF) kernel has better performance than other kernels. Then we have used a particle swarm optimizer for selection the parameters of the classifier. Simulation results show that the proposed identifier has very high accuracy for identification of digital signal types even at low levels of SNR.