Multi-gene genetic programming based modulation classification using multinomial logistic regression

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@InProceedings{Jiang:2016:WPMC,
  author =       "Yizhou Jiang and Sai Huang and Yifan Zhang and 
                 Zhiyong Feng",
  booktitle =    "2016 19th International Symposium on Wireless Personal
                 Multimedia Communications (WPMC)",
  title =        "Multi-gene genetic programming based modulation
                 classification using multinomial logistic regression",
  year =         "2016",
  pages =        "352--357",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7954505",
  size =         "6 pages",
  abstract =     "Automatic modulation classification (AMC) acts as a
                 critical role in cognitive radio network, which has
                 many civilian and military applications including
                 signal demodulation and interference identification. In
                 this paper, we explore a novel feature based (FB) AMC
                 method using multi-gene genetic programming (MGGP) and
                 multinomial logistic regression (MLR) jointly with
                 spectral correlation features (SCFs). The proposed
                 scheme includes two phases. In the training phase, MGGP
                 generates various mappings to transform SCFs into new
                 features and MLR selects some highly distinctive new
                 features as MGGP-features and the mappings as feature
                 optimisation functions (FOFs). Meanwhile the
                 corresponding MLR based classifier is output. In the
                 classification phase, SCFs are transformed by the FOFs
                 and the trained classifier identifies signal formats
                 with MGGP-features. Compared to traditional FB methods,
                 simulation results demonstrate that our proposed method
                 yields satisfactory performance improvement and
                 achieves robust classification, especially at lower SNR
                 and fewer number of samples.",
  notes =        "Also known as \cite{7954505}",
}

Genetic Programming entries for Yizhou Jiang Sai Huang Yifan Zhang Zhiyong Feng

Citations