Full Text:   <5052>

Summary:  <240>

Suppl. Mater.: 

CLC number: TN911.3

On-line Access: 2023-05-31

Received: 2022-06-10

Revision Accepted: 2023-05-31

Crosschecked: 2022-08-26

Cited: 0

Clicked: 1687

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

-   Go to

Article info.
Open peer comments

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.

@article{title="Modulation recognition network of multi-scale analysis with deep threshold noise elimination",
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"
}

%0 Journal Article
%T Modulation recognition network of multi-scale analysis with deep threshold noise elimination
%A Xiang LI
%A Yibing LI
%A Chunrui TANG
%A Yingsong LI
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 5
%P 742-758
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200253

TY - JOUR
T1 - Modulation recognition network of multi-scale analysis with deep threshold noise elimination
A1 - Xiang LI
A1 - Yibing LI
A1 - Chunrui TANG
A1 - Yingsong LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 5
SP - 742
EP - 758
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
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

Reference

[1]An ZL, Zhang TQ, Shen M, et al., 2022. Series-constellation feature based blind modulation recognition for beyond 5G MIMO-OFDM systems with channel fading. IEEE Trans Cogn Commun Netw, 8(2):793-811.

[2]Chang SG, Yu B, Vetterli M, 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process, 9(9):1532-1546.

[3]Dahap BI, Hongshu L, 2015. Advanced algorithm for automatic modulation recognition for analogue & digital signals. Proc Int Conf on Computing, Control, Networking, Electronics and Embedded Systems Engineering, p.32-36.

[4]Doan VS, Huynh-The T, Hoang VP, et al., 2022. MoDANet: multi-task deep network for joint automatic modulation classification and direction of arrival estimation. IEEE Commun Lett, 26(2):335-339.

[5]Donoho DL, 1995. De-noising by soft-thresholding. IEEE Trans Inform Theory, 41(3):613-627.

[6]Eltaieb RA, Abouelela HAE, Saif WS, et al., 2020. Modulation format identification of optical signals: an approach based on singular value decomposition of Stokes space projections. Appl Opt, 59(20):5989-6004.

[7]Han H, Ren ZY, Li L, et al., 2021. Automatic modulation classification based on deep feature fusion for high noise level and large dynamic input. Sensors, 21(6):2117.

[8]Harris FJ, 1978. On the use of windows for harmonic analysis with the discrete Fourier transform. Proc IEEE, 66(1):51-83.

[9]Huang G, Liu Z, van der Maaten L, et al., 2017. Densely connected convolutional networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2261-2269.

[10]Huang S, Yao YY, Wei ZQ, et al., 2017. Automatic modulation classification of overlapped sources using multiple cumulants. IEEE Trans Veh Technol, 66(7):6089-6101.

[11]Jia HR, Zhang XY, Bai J, 2013. A continuous differentiable wavelet threshold function for speech enhancement. J Cent South Univ, 20(8):2219-2225.

[12]Kline DM, Berardi VL, 2005. Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neur Comput Appl, 14(4):310-318.

[13]Li B, Chen XF, 2014. Wavelet-based numerical analysis: a review and classification. Fin Elem Anal Des, 81:14-31.

[14]Li L, Dong ZY, Zhu ZG, et al., 2023. Deep-learning hopping capture model for automatic modulation classification of wireless communication signals. IEEE Trans Aerosp Electron Syst, 59(2):772-783.

[15]Li LX, Huang JS, Cheng QQ, et al., 2021. Automatic modulation recognition: a few-shot learning method based on the capsule network. IEEE Wirel Commun Lett, 10(3):474-477.

[16]Li T, Liu W, Jiang XY, et al., 2020. Modulation classification in successive relaying systems with interference. IEEE/CIC Int Conf on Communications in China, p.1022-1026.

[17]Liu YB, Liu Y, Yang C, 2020. Modulation recognition with graph convolutional network. IEEE Wirel Commun Lett, 9(5):624-627.

[18]Meng F, Chen P, Wu LN, et al., 2018. Automatic modulation classification: a deep learning enabled approach. IEEE Trans Veh Technol, 67(11):10760-10772.

[19]O'Shea TJ, West N, 2016. Radio machine learning dataset generation with GNU radio. Proc 6th GNU Radio Conf, p.1-6.

[20]O'Shea TJ, Roy T, Clancy TC, 2018. Over-the-air deep learning based radio signal classification. IEEE J Sel Top Signal Process, 12(1):168-179.

[21]Peng SL, Sun SJ, Yao YD, 2022. A survey of modulation classification using deep learning: sign representation and data preprocessing. IEEE Trans Neur Netw Learn Syst, 33(12):7020-7038.

[22]Phukan GJ, Bora PK, 2018. Blind equalization for classification of digital modulations. Int Conf on Signal Processing and Communications, p.472-476.

[23]Rawat W, Wang ZH, 2017. Deep convolutional neural networks for image classification: a comprehensive review. Neur Comput, 29(9):2352-2449.

[24]Salam AOA, Sheriff RE, Hu YF, et al., 2019. Automatic modulation classification using interacting multiple model Kalman filter for channel estimation. IEEE Trans Veh Technol, 68(9):8928-8939.

[25]Schmidhuber J, 2015. Deep learning in neural networks: an overview. Neur Netw, 61:85-117.

[26]Sendur L, Selesnick IW, 2002. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans Signal Process, 50(11):2744-2756.

[27]Serbes A, Cukur H, Qaraqe K, 2020. Probabilities of false alarm and detection for the first-order cyclostationarity test: application to modulation classification. IEEE Commun Lett, 24(1):57-61.

[28]Szegedy C, Liu W, Jia YQ, et al., 2015. Going deeper with convolutions. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1-9.

[29]Szegedy C, Vanhoucke V, Ioffe S, et al., 2016. Rethinking the inception architecture for computer vision. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2818-2826.

[30]Tayakout H, Dayoub I, Ghanem K, et al., 2018. Automatic modulation classification for D-STBC cooperative relaying networks. IEEE Wirel Commun Lett, 7(5):780-783.

[31]Wang QL, Wu BG, Zhu PF, et al., 2020. ECA-Net: efficient channel attention for deep convolutional neural networks. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.11531-11539.

[32]Wang YS, Ma XJ, Chen ZY, et al., 2019. Symmetric cross entropy for robust learning with noisy labels. Proc IEEE/CVF Int Conf on Computer Vision, p.322-330.

[33]Wei YC, Xiao HX, Shi HH, et al., 2018. Revisiting dilated convolution: a simple approach for weakly- and semi-supervised semantic segmentation. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.7268-7277.

[34]Wei YJ, Fang SL, Wang XY, 2019. Automatic modulation classification of digital communication signals using SVM based on hybrid features, cyclostationary, and information entropy. Entropy, 21(8):745.

[35]Xu JL, Luo CB, Parr G, et al., 2020. A spatiotemporal multi-channel learning framework for automatic modulation recognition. IEEE Wirel Commun Lett, 9(10):1629-1632.

[36]Xu YQ, Xu GX, Ma C, et al., 2022. An advancing temporal convolutional network for 5G latency services via automatic modulation recognition. IEEE Trans Circ Syst II Expr Briefs, 69(6):3002-3006.

[37]Zhang ZF, Wang C, Gan CQ, et al., 2019. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Trans Signal Inform Process Netw, 5(3):469-478.

[38]Zhu MT, Li YJ, Pan ZS, et al., 2020. Automatic modulation recognition of compound signals using a deep multi-label classifier: a case study with radar jamming signals. Signal Process, 169:107393.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE