Transfer Learning for Automatic Modulation Recognition Using a Few Modulated Signal Samples

被引：14

作者：

Lin, Wensheng ^{[1
]}

Hou, Dongbin ^{[1
]}

Huang, Junsheng ^{[1
]}

Li, Lixin ^{[1
]}

Han, Zhu ^{[2
,3
]}

机构：

[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710129, Peoples R China

[2] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA

[3] Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2023年 / 72卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Transfer learning; few-shot learning; automatic modulation recognition; convolutional neural network; deep learning; CLASSIFICATION;

D O I：

10.1109/TVT.2023.3267270

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter proposes a transfer learning model for automatic modulation recognition (AMR) with only a few modulated signal samples. The transfer model is trained with the audio signal UrbanSound8K as the source domain, and then fine-tuned with a few modulated signal samples as the target domain. For improving the classification performance, the signal-to-noise ratio (SNR) is utilized as a feature to facilitate the classification of signals. Simulation results indicate that the transfer model has a significant superiority in terms of classification accuracy.

引用

页码：12391 / 12395

页数：5

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