@inproceedings{f5bbbfb135b2413e8fa10410d0403082,
title = "Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features",
abstract = "Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.",
keywords = "CNN, LSTM, automatic modulation classification, electronic reconnaissance, expert feature",
author = "Tianyao Yao and Yuan Chai and Shuai Wang and Xiaqing Miao and Xiangyuan Bu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 ; Conference date: 12-06-2020 Through 14-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ITNEC48623.2020.9085077",
language = "English",
series = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1225--1230",
editor = "Bing Xu and Kefen Mou",
booktitle = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
address = "United States",
}
