Support vector machine assisted genetic programming for MQAM classification

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@InProceedings{ZhechenZhu:2011:ISSCS,

• author = "Zhechen Zhu and Muhammad Waqar Aslam and Asoke Kumar Nandi",
• title = "Support vector machine assisted genetic programming for MQAM classification",
• booktitle = "10th International Symposium on Signals, Circuits and Systems (ISSCS 2011)",
• year = "2011",
• month = "30 " # jun # "-1 " # jul,
• address = "lasi, Romania",
• size = "6 pages",
• abstract = "Automatic modulation classification is used to identify automatically the modulation type of an incoming signal with limited or no prior knowledge to it. Various classifier systems have been developed to solve this problem. However, for certain types of modulations such as 16 QAM and 64 QAM, the classification performance under noisy condition still needs to be improved. In this paper, we propose a new AMC scheme by combining genetic programing (GP) with support vector machine (SVM) for the classification of 16 QAM and 64 QAM signals. The benchmark result shows that SVM assisted GP can produce better accuracy than some other existing methods.",
• keywords = "genetic algorithms, genetic programming, AMC scheme, GP, MQAM classification, QAM signals, SVM, automatic modulation classification, classification performance, classifier systems, modulation type, noisy condition, support vector machine assisted genetic programming, quadrature amplitude modulation, signal classification, support vector machines",
• DOI = "doi:10.1109/ISSCS.2011.5978654",
• notes = "Also known as \cite{5978654}",

}

Genetic Programming entries for Zhechen Zhu Muhammad Waqar Aslam Asoke K Nandi

Citations