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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hazza, A., Shoaib, M., Alshebeili, S.A., Fahad, A.: An overview of feature-based methods for digital modulation classification. In: 2013 1st International Conference on Communications, Signal Processing and their Applications, pp. 1–6. IEEE Press, New York (2013)
Zhu, X., Fujii, T.: A modulation classification method in cognitive radios system using stacked denoising sparse autoencoder. In: 2017 IEEE Radio and Wireless Symposium, pp. 218–220. IEEE Press, New York (2017)
Dai, A., Zhang, H., Sun, H.: Automatic modulation classification using stacked sparse auto-encoders. In: 13th IEEE International Conference on Signal Processing, pp. 248–252. IEEE Press, New York (2017)
Mendis, G.J., Wei, J., Madanayake, A.: Deep learning-based automated modulation classification for cognitive radio. In: 2016 IEEE International Conference on Communication Systems, pp. 1–6. IEEE Press, New York (2016)
O’Shea, T.J., Corgan, J., Clancy, T.C.: Convolutional radio modulation recognition networks. In: Jayne, C., Iliadis, L. (eds.) EANN 2016. CCIS, vol. 629, pp. 213–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44188-7_16
Fu, J., Zhao, C., Li, B., Peng, X.: Deep learning based digital signal modulation recognition. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds.) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. LNEE, vol. 322, pp. 955–964. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-08991-1_100
Tang, Y., Eliasmith, C.: Deep networks for robust visual recognition. In: 27th International Conference on Machine Learning, pp. 1055–1062. ACM, New York (2010)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). IEEE Press, New York
Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11(3), 625–660 (2010). ACM, New York
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 2012 26th Annual Conference on Neural Information Processing Systems, vol. 25, no. 2, pp. 1097–1105. ACM, New York (2012)
Acknowledgement
This work was supported by National Natural Science Foundation of China. (No. 61601147, No. 61571316, No. 61371100) and the Fundamental Research Funds for the Central Universities (Grant No. HIT. MKSTISP. 2016013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, Y., Li, D., Wang, Z., Liu, G., Lv, H. (2018). Transfer Learning Method for Convolutional Neural Network in Automatic Modulation Classification. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-73447-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73446-0
Online ISBN: 978-3-319-73447-7
eBook Packages: Computer ScienceComputer Science (R0)