TY - JOUR
T1 - Genetic algorithm assisted support vector machine for M-QAM classification
AU - Ghauri, Sajjad A.
AU - Sarfraz, Mubashar
AU - Muhammad, Nooh Bany
AU - Munir, Shahrukh
N1 - 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
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Automatic modulation classification (AMC)
KW - Genetic algorithm (GA)
KW - Higher order cumulants (HOC)
KW - M-ARY quadrature amplitude modulated (M-QAM) signal
KW - Support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85091846918&partnerID=8YFLogxK
U2 - 10.18280/mmep.070315
DO - 10.18280/mmep.070315
M3 - Article
SN - 2369-0739
VL - 7
SP - 441
EP - 449
JO - Mathematical Modelling of Engineering Problems
JF - Mathematical Modelling of Engineering Problems
IS - 3
ER -