GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size

Shuai Zhang, Yan Zhang, Mingjun Ma, Zunwen He*, Wancheng Zhang

*Corresponding author for this work

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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.

Original languageEnglish
Title of host publicationCommunications and Networking - 16th EAI International Conference, ChinaCom 2021, Proceedings
EditorsHonghao Gao, Jun Wun, Jianwei Yin, Feifei Shen, Yulong Shen, Jun Yu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages80-90
Number of pages11
ISBN (Print)9783030991999
DOIs
Publication statusPublished - 2022
Event16th EAI International Conference on Communications and Networking in China, ChinaCom 2021 - Virtual, Online
Duration: 21 Nov 202122 Nov 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume433 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference16th EAI International Conference on Communications and Networking in China, ChinaCom 2021
CityVirtual, Online
Period21/11/2122/11/21

Keywords

  • Few-shot learning
  • GAN
  • Modulation recognition
  • SNR

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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 H. Gao, J. Wun, J. Yin, F. Shen, Y. Shen, & J. Yu (Eds.), Communications and Networking - 16th EAI International Conference, ChinaCom 2021, Proceedings (pp. 80-90). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 433 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99200-2_7