Abstract
Modulation recognition plays an important role in non-cooperative communications. In practice, only a small number of samples can be collected for training purposes. The limited training data degrade the accuracy of the modulation recognition networks. In this paper, we propose a novel network to realize the modulation recognition on basis of the few-shot learning. Generative adversarial networks (GANs) and a signal-to-noise ratio (SNR) augment module are introduced to expand the training dataset. In addition, a preprocessing module and residual shrinkage networks are used to improve the capability of characterizing signal features and the anti-noise performance. The proposed network is evaluated using the RML2016.10a dataset. It is illustrated that the proposed network outperforms the baseline method and the method without data augment with a small number of training samples.
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
Dobre, O., Abdi, A., Bar-Ness, Y., Su, W.: Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun 1(2), 137 (2007)
Hameed, F., Dobre, O.A., Popescu, D.C.: On the likelihood-based approach to modulation classification. IEEE Trans. Wirel. Commun. 8(12), 5884–5892 (2009)
Chen, W., Xie, Z., Ma, L., Liu, J., Liang, X.: A faster maximum-likelihood modulation classification in flat fading non-Gaussian channels. IEEE Commun. Lett. 23(3), 454–457 (2019)
Nandi, A.K., Azzouz, E.E.: Algorithms for automatic modulation recognition of communication signals. IEEE Trans. Commun. 46(4), 431–436 (1998)
Ma, J., Qiu, T.: Automatic modulation classification using cyclic correntropy spectrum in impulsive noise. IEEE Wirel. Commun. Lett. 8(2), 440–443 (2019)
Wu, Z., Zhou, S., Yin, Z., Ma, B., Yang, Z.: Robust automatic modulation classification under varying noise conditions. IEEE Access 5, 19733–19741 (2017)
Li, R., Li, L., Yang, S., Li, S.: Robust automated VHF modulation recognition based on deep convolutional neural networks. IEEE Commun. Lett. 22(5), 946–949 (2018)
Peng, S., et al.: Modulation classification based on signal constellation diagrams and deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 718–727 (2019)
Hong, D., Zhang, Z., Xu, X.: Automatic modulation classification using recurrent neural networks. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 695–700 (2017)
Li, L., Huang, J., Cheng, Q., Meng, H., Han, Z.: Automatic modulation recognition: a few-shot learning method based on the capsule network. IEEE Wirel. Commun. Lett. 10(3), 474–477 (2021)
Zhang, D., Ding, W., Liu, C., Wang, H., Zhang, B.: Modulated autocorrelation convolution networks for automatic modulation classification based on small sample set. IEEE Access 8, 27097–27105 (2020)
Zhang, H., Huang, M., Yang, J., Sun, W.: A data preprocessing method for automatic modulation classification based on CNN. IEEE Commun. Lett. 25(4), 1206–1210 (2021)
Gong, J., Xu, X., Lei, Y.: Unsupervised specific emitter identification method using radio-frequency fingerprint embedded InfoGAN. IEEE Trans. Inf. Forensics Secur. 15, 2898–2913 (2020)
O’Shea, T.J., Corgan, J., Clancy, T.C.: Convolutional radio modulation recognition networks. In: Jayne, C., Iliadis, L. (eds.) EANN 2016. Communications in Computer and Information Science, vol. 629, pp. 213–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44188-7_16
De Boer, P.T., Kroese, D.P., Mannor, S., et al.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Zhang, Z., Li, H., Chen, L.: Deep residual shrinkage networks with self-adaptive slope thresholding for fault diagnosis. In: 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO) (2021). In press
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 27–30 June 2016, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Acknowledgements
This work was supported by the National Key R&D Program of China under Grant (2019YFE0196400), the National Natural Science Foundation of China under Grant (61871035), and the National Defense Science and Technology Innovation Zone.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, S., Zhang, Y., Ma, M., He, Z., Zhang, W. (2022). GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size. In: Gao, H., Wun, J., Yin, J., Shen, F., Shen, Y., Yu, J. (eds) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-99200-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-99200-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99199-9
Online ISBN: 978-3-030-99200-2
eBook Packages: Computer ScienceComputer Science (R0)