@inproceedings{a77b7d221bbf421689053b3012a00f95,
title = "GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size",
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.",
keywords = "Few-shot learning, GAN, Modulation recognition, SNR",
author = "Shuai Zhang and Yan Zhang and Mingjun Ma and Zunwen He and Wancheng Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 16th EAI International Conference on Communications and Networking in China, ChinaCom 2021 ; Conference date: 21-11-2021 Through 22-11-2021",
year = "2022",
doi = "10.1007/978-3-030-99200-2_7",
language = "English",
isbn = "9783030991999",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "80--90",
editor = "Honghao Gao and Jun Wun and Jianwei Yin and Feifei Shen and Yulong Shen and Jun Yu",
booktitle = "Communications and Networking - 16th EAI International Conference, ChinaCom 2021, Proceedings",
address = "Germany",
}