Research Article
GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size
@INPROCEEDINGS{10.1007/978-3-030-99200-2_7, author={Shuai Zhang and Yan Zhang and Mingjun Ma and Zunwen He and Wancheng Zhang}, title={GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size}, proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings}, proceedings_a={CHINACOM}, year={2022}, month={4}, keywords={Modulation recognition GAN SNR Few-shot learning}, doi={10.1007/978-3-030-99200-2_7} }- Shuai Zhang
Yan Zhang
Mingjun Ma
Zunwen He
Wancheng Zhang
Year: 2022
GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size
CHINACOM
Springer
DOI: 10.1007/978-3-030-99200-2_7
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.
