Genetic algorithm assisted support vector machine for M-QAM classification
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Authors
Bany Muhammad, Nooh
Ghauri, Sajjad
Sarfraz, Mubashar
Munir, Shahrukh
Issue Date
2020-09-03
Type
Journal Article
Peer-Reviewed
Peer-Reviewed
Language
Keywords
Alternative Title
Abstract
Automatic modulation classification (AMC) has wide spread applications in today’s communication system. AMC has vast applications both in military as well as civilian. In intelligent communication systems such as software defined radios networks and cognitive radio networks, AMC is the most important issue, when there is no prior information about the signal. In this research article, pattern recognition approach has been utilized for classification of M-ARY quadrature amplitude modulated (M-QAM) signals. Higher order cumulants are selected as feature set and Genetic Algorithm assisted Support Vector Machine (SVM) classifier is used for classification of M-QAM signals. The performance of classifier is evaluated on fading channels in the presence of additive white Guassain noise. The classification accuracy is also compared with and without optimized classifier.
Description
Citation
Ghauri, S.A.,Sarfraz, M., Muhammad, N.B., Munir, S. (2020). Genetic algorithm assistedsupport vector machine for M-QAM classification. Mathematical Modelling ofEngineering Problems, Vol. 7, No. 3, pp. 441-449. https://doi.org/10.18280/mmep.070315
Publisher
Mathematical Modelling of Engineering Problems