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.
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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.
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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
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