Research Article
Transfer Learning Method for Convolutional Neural Network in Automatic Modulation Classification
@INPROCEEDINGS{10.1007/978-3-319-73447-7_41, author={Yu Xu and Dezhi Li and Zhenyong Wang and Gongliang Liu and Haibo Lv}, title={Transfer Learning Method for Convolutional Neural Network in Automatic Modulation Classification}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Modulation classification Convolutional neural network Transfer learning method}, doi={10.1007/978-3-319-73447-7_41} }- Yu Xu
Dezhi Li
Zhenyong Wang
Gongliang Liu
Haibo Lv
Year: 2018
Transfer Learning Method for Convolutional Neural Network in Automatic Modulation Classification
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_41
Abstract
Automatic modulation classification (AMC) plays an important role in many fields to identify the modulation type of signals, in which the deep learning methods have shown attractive potential development. In our research, we introduce convolutional neural network (CNN) to recognize the modulation of the input signal. We used real signal data generated by instruments as dataset for training and testing. Based on analysis of the unstable training problem of CNN for weak signals recognition with low SNR, a transfer learning method is proposed. Experiments results show that the proposed transfer learning method can locate better initial values for CNN training and converge to a good result. According to the recognition accuracy performance analysis, The CNN with the proposed transfer learning method has higher average classification accuracy and is more compatible for unstable training problem.
