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CLC number: TN911.3

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2022-08-26

Cited: 0

Clicked: 2339

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiang LI

https://orcid.org/0000-0003-1745-0676

Yibing LI

https://orcid.org/0000-0003-4510-982X

Chunrui TANG

https://orcid.org/0009-0005-6995-283X

Yingsong LI

https://orcid.org/0000-0002-2450-6028

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.5 P.742-758

http://doi.org/10.1631/FITEE.2200253


Modulation recognition network of multi-scale analysis with deep threshold noise elimination


Author(s):  Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI

Affiliation(s):  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):   chunruitang@126.com

Key Words:  Signal noise elimination, Deep adaptive threshold learning network, Multi-scale feature fusion, Modulation recognition


Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI. Modulation recognition network of multi-scale analysis with deep threshold noise elimination[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(5): 742-758.

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author="Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="5",
pages="742-758",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200253"
}

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%A Xiang LI
%A Yibing LI
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%A Yingsong LI
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A1 - Xiang LI
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A1 - Yingsong LI
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200253


Abstract: 
To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion. The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

具有深度阈值噪声消除的多尺度分析调制识别网络

李响1,2,李一兵1,2,汤春瑞3,4,李迎松1,2
1哈尔滨工程大学信息与通信工程学院,中国哈尔滨市,150001
2哈尔滨工程大学先进船舶通信与信息技术工业和信息化部重点实验室,中国哈尔滨市,150001
3中煤科工集团重庆研究院有限公司,中国重庆市,400037
4瓦斯灾害监控与应急技术国家重点实验室,中国重庆市,400039
摘要:为了提高多变环境下调制信号识别的准确性、减少先验知识不足等因素对识别结果的影响,研究人员逐渐采用深度学习技术来替代传统的调制信号处理技术。为了解决低信噪比下调制信号识别精度低的问题,我们设计了一种具有深度阈值噪声消除的多尺度分析调制识别网络,在标签平滑的对称交叉熵函数作用下识别实际采集的调制信号。该网络由一个具有深度自适应阈值学习的消噪编码器和一个具有多尺度特征融合的解码器组成。将两个模块进行跳跃连接,共同作用以提高整体网络的鲁棒性。实验结果表明,该方法在低信噪比下比以前的方法具有更好的识别效果。该网络展示了对噪声阈值的灵活自学习能力以及所设计的特征融合模块对各种调制类型的多尺度特征获取的有效性。

关键词:信号消噪;深度自适应阈值学习网络;多尺度特征融合;调制识别

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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