Multi-gene genetic programming based modulation classification using multinomial logistic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Jiang:2016:WPMC,
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author = "Yizhou Jiang and Sai Huang and Yifan Zhang and
Zhiyong Feng",
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booktitle = "2016 19th International Symposium on Wireless Personal
Multimedia Communications (WPMC)",
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title = "Multi-gene genetic programming based modulation
classification using multinomial logistic regression",
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year = "2016",
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pages = "352--357",
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month = "14-16 " # nov,
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address = "Shenzhen, China",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-5377-3",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7954505",
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size = "6 pages",
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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