@inproceedings{f2342df76d0343d98ee8a241759e6857,
title = "Automatic modulation classification using principle composition analysis based features selection",
abstract = "Automatic Modulation Classification (AMC) plays an important role in both military and civilian applications. Feature based AMC is used in this paper. Principle Component Analysis (PCA) is employed to reduce dimensions of the feature vector. Two classifiers mainly k-nearest neighbor (KNN) and Support Vector Machine (SVM) are used to investigate the correct classification rate against different SNRs for test signals. Experiments are conducted using data trained at two different SNRs of 15dB and 3dB respectively. Results show that KNN classifier shows better results when data is trained at high SNRs. However, both the classifiers show almost same performance when data is trained at low SNR.",
keywords = "Automatic Modulation Classification, classifier, Feature based, k-Nearest Neighbor, Principle Component Analysis, Support Vector Machine",
author = "Afan Ali and Fan Yangyu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 SAI Computing Conference 2017 ; Conference date: 18-07-2017 Through 20-07-2017",
year = "2018",
month = jan,
day = "8",
doi = "10.1109/SAI.2017.8252117",
language = "英语",
series = "Proceedings of Computing Conference 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "294--296",
booktitle = "Proceedings of Computing Conference 2017",
}