Multi-objective Genetic Programming based Automatic Modulation Classification

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@InProceedings{Dai:2019:WCNC,

• author = "Rui Dai and Yicheng Gao and Sai Huang and Fan Ning and Zhiyong Feng",
• booktitle = "2019 IEEE Wireless Communications and Networking Conference (WCNC)",
• title = "Multi-objective Genetic Programming based Automatic Modulation Classification",
• year = "2019",
• abstract = "Automatic modulation classification (AMC) plays a crucial role in the cognitive radio networks, to which feature-based (FB) methods are the dominating solutions. However, the original features in FB methods are redundant, leading to the ambiguity of classification. To tackle this problem, this paper proposes a novel multi-objective modulation classification (MOMC) method. To reduce the redundant features, the original multi-features are recombined into a single feature by multiobjective genetic programming (MOGP) algorithm. Two quantitative objectives, the classification error rate and the variance for robustness, are then presented to jointly optimize the algorithm as two fitness functions. Furthermore, the single feature generated by MOGP is classified by logistic regression (LR) with low computational complexity. Simulation results verify the enhanced robustness and classification accuracy performance yielded by our proposed MOMC method compared to the existing classification methods.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/WCNC.2019.8885738",
• ISSN = "1558-2612",
• month = apr,
• notes = "Also known as \cite{8885738}",

}

Genetic Programming entries for Rui Dai Yicheng Gao Sai Huang Fan Ning Zhiyong Feng

Citations