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

共 50 条

[21] Automatic Recognition of Rice Leaf Diseases Using Transfer Learning
Simhadri, Chinna Gopi
Kondaveeti, Hari Kishan
AGRONOMY-BASEL, 2023, 13 (04):
[22] Automatic Speaker Recognition using Transfer Learning Approach of Deep Learning Models
Ganvir, Sonal
Lal, Nidhi
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021), 2021, : 595 - 601
[23] Automatic Modulation Recognition with Deep Learning Algorithms
Camlibel, Aysenur
Karakaya, Bahattin
Tanc, Yesim Hekim
32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024,
[24] Few-Shot Learning for Fine-Grained Signal Modulation Recognition Based on Foreground Segmentation
Zhang, Zilin
Li, Yan
Zhai, Qihang
Li, Yunjie
Gao, Meiguo
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (03) : 2281 - 2292
[25] Bayesian Prototype Learning for Few-Shot Radar Signal Intra-Pulse Modulation Recognition
Gao, Jingpeng
Chen, Geng
Shen, Chen
IEEE COMMUNICATIONS LETTERS, 2024, 28 (10) : 2362 - 2366
[26] Wavelet Cyclic Feature Based Automatic Modulation Recognition Using Nonuniform Compressive Samples
Zhou, Lei
Man, Hong
2013 IEEE 78TH VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL), 2013,
[27] AMCRN: Few-Shot Learning for Automatic Modulation Classification
Zhou, Quan
Zhang, Ronghui
Mu, Junsheng
Zhang, Hongming
Zhang, Fangpei
Jing, Xiaojun
IEEE COMMUNICATIONS LETTERS, 2022, 26 (03) : 542 - 546
[28] Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram
Han, Hui
Yi, Zhijian
Zhu, Zhigang
Li, Lin
Gong, Shuaige
Li, Bin
Wang, Mingjie
ELECTRONICS, 2023, 12 (03)
[29] Transfer learning assisted convolutional neural networks for modulation format recognition in few-mode fibers
Zhu, Xiaorong
Liu, Bo
Zhu, Xu
Ren, Jianxin
Ullah, Rahat
Mao, Yaya
Wu, Xiangyu
Li, Mingye
Chen, Shuaidong
Bai, Yu
OPTICS EXPRESS, 2021, 29 (22) : 36953 - 36963
[30] Contrastive Language-Signal Prediction for Automatic Modulation Recognition
Qu, Yunpeng
Lu, Zhilin
Hui, Bingyu
Wang, Jintao
Wang, Jian
IEEE WIRELESS COMMUNICATIONS LETTERS, 2024, 13 (11) : 3242 - 3246

← 1 2 3 4 5 →