MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are esse...

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Published in:	International journal of intelligent systems Vol. 36; no. 12; pp. 7204 - 7238
Main Authors:	Zheng, Qinghe, Zhao, Penghui, Zhang, Deliang, Wang, Hongjun
Format:	Journal Article
Language:	English
Published:	New York John Wiley & Sons, Inc 01.12.2021
Subjects:	Back propagation deep convolutional autoencoder Intelligent systems Invariance manifold consistency manifold regularization Manifolds positive‐unlabeled problem Radio broadcasting Regularization unauthorized broadcasting identification Upper bounds
ISSN:	0884-8173, 1098-111X
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Abstract	Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy‐stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back‐propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross‐layer manifold invariance. Finally, MR‐DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state‐of‐the‐art performance. To help understand the principle behind MR‐DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.
AbstractList	Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy‐stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back‐propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross‐layer manifold invariance. Finally, MR‐DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state‐of‐the‐art performance. To help understand the principle behind MR‐DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting. Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy‐stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back‐propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross‐layer manifold invariance. Finally, MR‐DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state‐of‐the‐art performance. To help understand the principle behind MR‐DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.
Author	Zhao, Penghui Zhang, Deliang Wang, Hongjun Zheng, Qinghe
Author_xml	– sequence: 1 givenname: Qinghe orcidid: 0000-0003-1466-2542 surname: Zheng fullname: Zheng, Qinghe organization: Shandong University – sequence: 2 givenname: Penghui orcidid: 0000-0002-3353-9059 surname: Zhao fullname: Zhao, Penghui organization: Shandong University – sequence: 3 givenname: Deliang orcidid: 0000-0002-2099-0326 surname: Zhang fullname: Zhang, Deliang organization: Shandong University – sequence: 4 givenname: Hongjun orcidid: 0000-0003-0496-1573 surname: Wang fullname: Wang, Hongjun email: hjw@sdu.edu.cn organization: Shandong University
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Cites_doi	10.1007/s11004-021-09935-z 10.1002/int.21875 10.1109/TSP.2016.2645515 10.1002/int.22059 10.1109/ICCV.2015.177 10.1016/j.conengprac.2018.02.005 10.1109/TCCN.2019.2911524 10.1109/CVPR.2017.539 10.1109/ICIS.2018.8466411 10.1002/dac.3968 10.1109/TCYB.2016.2529299 10.1109/ICEICT.2016.7879730 10.1109/TPAMI.2017.2663423 10.1002/2017RS006409 10.1111/j.1468-0270.2010.02062.x 10.7551/mitpress/7503.003.0024 10.1049/iet-cta.2020.0557 10.1109/TSP.2018.2887406 10.1109/ACCESS.2020.2992044 10.1109/TIM.2020.3008988 10.1109/TSSA.2017.8272943 10.1016/j.patrec.2017.03.008 10.1109/ISESD.2016.7886740 10.1007/s00454-013-9557-2 10.3390/math7121170 10.1002/int.21924 10.1109/IMCCC.2018.00039 10.1029/2019RS006885 10.1109/CVPR.2016.319 10.1109/ACCESS.2019.2930627 10.1109/STSIVA.2019.8730244 10.1109/CompComm.2016.7925030 10.1109/TCCN.2021.3071149 10.1109/TCYB.2016.2536638 10.1139/juvs-2016-0011 10.1109/IWCMC48107.2020.9148485 10.1145/3097983.3098052 10.1016/j.neucom.2018.05.027 10.1109/TPAMI.2018.2860995 10.1007/s11071-021-06208-6 10.1016/j.ymssp.2020.106683 10.1038/s41598-019-45333-6 10.1002/rnc.5131 10.1109/TNNLS.2015.2496947 10.1109/MIM.2015.7066682
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References	2017; 5 2019; 7 2019; 9 2015; 18 2019; 5 2020; 140 2017; 47 2019; 32 2021; 103 2017; 65 2019; 34 2011; 31 2006; 7 2007 2005 2020; 100 2018; 40 2020; 55 2018; 66 2021; 14 2020; 8 2017; 52 2018; 8 2014; 5 2017; 90 2015; 27 2020; 30 2019; 41 2021 2020 2017; 32 2018; 310 2019; 67 2019 2014; 15 2018 2017 2020; 69 2016 2018; 74 2015 2013 2018; 33 2016; 47 2018; 16 2010; 51 e_1_2_11_32_1 e_1_2_11_55_1 e_1_2_11_30_1 e_1_2_11_57_1 e_1_2_11_36_1 e_1_2_11_51_1 e_1_2_11_13_1 e_1_2_11_34_1 e_1_2_11_53_1 e_1_2_11_11_1 e_1_2_11_29_1 e_1_2_11_6_1 e_1_2_11_27_1 e_1_2_11_4_1 e_1_2_11_2_1 Belkin M (e_1_2_11_54_1) 2006; 7 Zhang Y (e_1_2_11_7_1) 2014; 5 e_1_2_11_60_1 e_1_2_11_20_1 e_1_2_11_45_1 e_1_2_11_47_1 e_1_2_11_24_1 e_1_2_11_41_1 e_1_2_11_8_1 e_1_2_11_22_1 e_1_2_11_43_1 e_1_2_11_17_1 Laurens V (e_1_2_11_52_1) 2014; 15 e_1_2_11_15_1 e_1_2_11_38_1 e_1_2_11_19_1 e_1_2_11_50_1 e_1_2_11_10_1 e_1_2_11_31_1 e_1_2_11_56_1 e_1_2_11_58_1 e_1_2_11_14_1 e_1_2_11_35_1 e_1_2_11_12_1 Zhao C (e_1_2_11_25_1) 2018; 8 e_1_2_11_28_1 e_1_2_11_5_1 e_1_2_11_26_1 e_1_2_11_3_1 e_1_2_11_49_1 Li J (e_1_2_11_33_1) 2020; 100 Sulam J (e_1_2_11_48_1) 2018; 66 e_1_2_11_21_1 e_1_2_11_44_1 Callegari C (e_1_2_11_59_1) 2018; 16 e_1_2_11_46_1 e_1_2_11_40_1 e_1_2_11_9_1 e_1_2_11_23_1 e_1_2_11_42_1 e_1_2_11_18_1 e_1_2_11_16_1 e_1_2_11_37_1 e_1_2_11_39_1
References_xml	– volume: 30 start-page: 6683 year: 2020 end-page: 6700 article-title: State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises publication-title: Int J Robust Nonlinear Control – volume: 47 start-page: 1017 issue: 4 year: 2017 end-page: 1027 article-title: Stacked convolutional denoising auto‐encoders for feature representation publication-title: IEEE Trans Cybern. – volume: 65 start-page: 1580 issue: 6 year: 2017 end-page: 1594 article-title: Randomized robust subspace recovery and outlier detection for high dimensional data matrices publication-title: IEEE Trans Signal Process – volume: 69 start-page: 5981 issue: 6 year: 2020 end-page: 5983 article-title: Unauthorized broadcasting identification: a deep LSTM recurrent learning approach publication-title: IEEE Trans Instrum Meas – volume: 7 start-page: 2399 issue: 1 year: 2006 end-page: 2434 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J Mach Learn Res – year: 2005 – volume: 32 issue: 11 year: 2019 article-title: Fast detection and DOA estimation of the unknown wideband signal sources publication-title: Int J Commun Syst – year: 2021 article-title: Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder publication-title: Math Geosci – volume: 140 year: 2020 article-title: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks publication-title: Mech Syst Signal Proc – year: 2021 article-title: Shared spectrum monitoring using deep learning publication-title: IEEE Trans Cogn Commun Netw. – volume: 14 start-page: 3344 issue: 19 year: 2021 end-page: 3350 article-title: Robust point‐to‐point iterative learning control with trial‐varying initial conditions publication-title: IET Contr Theory Appl – year: 2007 – volume: 90 start-page: 8 year: 2017 end-page: 14 article-title: K‐means clustering with outlier removal publication-title: Pattern Recogn Lett. – volume: 7 start-page: 111257 year: 2019 end-page: 111270 article-title: Hierarchical edge computing: a novel multi‐source multi‐dimensional data anomaly detection scheme for industrial internet of things publication-title: IEEE Access – volume: 31 start-page: 115 issue: 1 year: 2011 end-page: 117 article-title: Broadcasting licence fee settlements in the UK and Germany publication-title: Econ Aff. – volume: 32 start-page: 881 issue: 9 year: 2017 end-page: 899 article-title: Exponentially weighted ellipsoidal model for anomaly detection publication-title: Int J Intell Syst – volume: 18 start-page: 33 issue: 2 year: 2015 end-page: 37 article-title: A cloud‐based approach to spectrum monitoring publication-title: IEEE Instrum Meas Mag – year: 2016 – volume: 27 start-page: 1997 issue: 10 year: 2015 end-page: 2008 article-title: Why deep learning works: a manifold disentanglement perspective publication-title: IEEE Trans Neural Netw Learn Syst. – year: 2018 – volume: 16 start-page: 9 issue: 1 year: 2018 end-page: 16 article-title: Improving stability of PCA‐based network anomaly detection by means of kernel‐PCA publication-title: Int J Comput Sci Eng – volume: 55 start-page: 1 issue: 4 year: 2020 end-page: 6 article-title: FM broadcast monitoring using artificial intelligence publication-title: Radio Sci – volume: 5 start-page: 50 issue: 1 year: 2014 end-page: 54 article-title: Research on interconnect technology for radio monitoring stations based on RMTP protocol publication-title: Mod Electron Tech. – volume: 33 start-page: 1858 issue: 9 year: 2018 end-page: 1868 article-title: Outlier detection using linguistically quantified statements publication-title: Int J Intell Syst – volume: 8 start-page: 83765 year: 2020 end-page: 83781 article-title: Long short‐term memory and fuzzy logic for anomaly detection and mitigation in software‐defined network environment publication-title: IEEE Access – volume: 103 start-page: 1733 year: 2021 end-page: 1755 article-title: Input‐to‐state stability of impulsive reaction–diffusion neural networks with infinite distributed delays publication-title: Nonlinear Dyn. – volume: 52 start-page: 1261 issue: 10 year: 2017 end-page: 1267 article-title: State of the art and challenges of radio spectrum monitoring in China publication-title: Radio Sci – volume: 34 start-page: 415 issue: 3 year: 2019 end-page: 438 article-title: Detection of outlier information by the use of linguistic summaries based on classic and interval‐valued fuzzy sets publication-title: Int J Intell Syst. – volume: 7 start-page: 1 issue: 12 year: 2019 end-page: 21 article-title: Enhancement of deep learning in image classification performance using Xception with the Swish activation function for colorectal polyp preliminary screening publication-title: Mathematics – volume: 9 start-page: 9051 issue: 1 year: 2019 article-title: EEG decoding reveals the strength and temporal dynamics of goal‐relevant representations publication-title: Sci Rep – volume: 41 start-page: 2584 issue: 11 year: 2019 end-page: 2598 article-title: Efficient training for positive unlabeled learning publication-title: IEEE Trans Pattern Anal – volume: 15 start-page: 3221 issue: 1 year: 2014 end-page: 3245 article-title: Accelerating t‐SNE using tree‐based algorithms publication-title: J Mach Learn Res – volume: 40 start-page: 163 issue: 1 year: 2018 end-page: 178 article-title: Multi‐dimensional sparse models publication-title: IEEE Trans Pattern Anal – volume: 67 start-page: 993 issue: 4 year: 2019 end-page: 1005 article-title: Sequential outlier detection based on incremental decision trees publication-title: IEEE Trans Signal Process – volume: 310 start-page: 223 year: 2018 end-page: 235 article-title: Ramp loss one‐class support vector machine: a robust and effective approach to anomaly detection problems publication-title: Neurocomputing – volume: 66 start-page: 4090 issue: 15 year: 2018 end-page: 4104 article-title: Multi‐layer convolutional sparse modeling: pursuit and dictionary learning publication-title: IEEE Trans Signal Process – volume: 5 start-page: 1 issue: 1 year: 2017 end-page: 12 article-title: Spectrum monitoring with unmanned aerial vehicle carrying a receiver based on the core technology of cognitive radio—a software‐defined radio design publication-title: J Unmanned Veh Syst – year: 2020 – volume: 100 issue: 4 year: 2020 article-title: Clustering‐based anomaly detection in multivariate time series data publication-title: Appl Soft Comput. – volume: 47 start-page: 830 issue: 4 year: 2016 end-page: 840 article-title: Manifold partition discriminant analysis publication-title: IEEE Trans Cybern. – year: 2017 – year: 2019 – volume: 8 start-page: 1 issue: 12 year: 2018 end-page: 15 article-title: Application of auxiliary classifier Wasserstein generative adversarial networks in wireless signal classification of illegal unmanned aerial vehicles publication-title: Appl Sci—Basel. – volume: 5 start-page: 637 issue: 3 year: 2019 end-page: 647 article-title: Unsupervised wireless spectrum anomaly detection with interpretable features publication-title: IEEE Trans Cogn Commun. – year: 2015 – volume: 51 start-page: 221 issue: 1 year: 2010 end-page: 267 article-title: Manifold reconstruction using tangential Delaunay complexes publication-title: Discrete Comput Geom – volume: 74 start-page: 1 year: 2018 end-page: 12 article-title: An incipient fault detection approach via detrending and denoising publication-title: Control Eng Pract – year: 2013 – ident: e_1_2_11_32_1 doi: 10.1007/s11004-021-09935-z – ident: e_1_2_11_26_1 doi: 10.1002/int.21875 – ident: e_1_2_11_41_1 – volume: 16 start-page: 9 issue: 1 year: 2018 ident: e_1_2_11_59_1 article-title: Improving stability of PCA‐based network anomaly detection by means of kernel‐PCA publication-title: Int J Comput Sci Eng – ident: e_1_2_11_21_1 doi: 10.1109/TSP.2016.2645515 – volume: 5 start-page: 50 issue: 1 year: 2014 ident: e_1_2_11_7_1 article-title: Research on interconnect technology for radio monitoring stations based on RMTP protocol publication-title: Mod Electron Tech. – ident: e_1_2_11_35_1 doi: 10.1002/int.22059 – ident: e_1_2_11_38_1 doi: 10.1109/ICCV.2015.177 – ident: e_1_2_11_31_1 doi: 10.1016/j.conengprac.2018.02.005 – ident: e_1_2_11_45_1 doi: 10.1109/TCCN.2019.2911524 – ident: e_1_2_11_57_1 doi: 10.1109/CVPR.2017.539 – ident: e_1_2_11_15_1 doi: 10.1109/ICIS.2018.8466411 – ident: e_1_2_11_17_1 doi: 10.1002/dac.3968 – ident: e_1_2_11_55_1 doi: 10.1109/TCYB.2016.2529299 – ident: e_1_2_11_13_1 doi: 10.1109/ICEICT.2016.7879730 – ident: e_1_2_11_39_1 doi: 10.1109/TPAMI.2017.2663423 – ident: e_1_2_11_2_1 doi: 10.1002/2017RS006409 – ident: e_1_2_11_4_1 doi: 10.1111/j.1468-0270.2010.02062.x – ident: e_1_2_11_53_1 doi: 10.7551/mitpress/7503.003.0024 – ident: e_1_2_11_30_1 doi: 10.1049/iet-cta.2020.0557 – ident: e_1_2_11_49_1 doi: 10.1109/TSP.2018.2887406 – ident: e_1_2_11_50_1 doi: 10.1109/ACCESS.2020.2992044 – ident: e_1_2_11_24_1 doi: 10.1109/TIM.2020.3008988 – ident: e_1_2_11_22_1 doi: 10.1109/TSSA.2017.8272943 – volume: 66 start-page: 4090 issue: 15 year: 2018 ident: e_1_2_11_48_1 article-title: Multi‐layer convolutional sparse modeling: pursuit and dictionary learning publication-title: IEEE Trans Signal Process – ident: e_1_2_11_36_1 doi: 10.1016/j.patrec.2017.03.008 – ident: e_1_2_11_9_1 doi: 10.1109/ISESD.2016.7886740 – ident: e_1_2_11_43_1 doi: 10.1007/s00454-013-9557-2 – ident: e_1_2_11_51_1 doi: 10.3390/math7121170 – ident: e_1_2_11_27_1 doi: 10.1002/int.21924 – ident: e_1_2_11_58_1 – ident: e_1_2_11_23_1 doi: 10.1109/IMCCC.2018.00039 – ident: e_1_2_11_5_1 doi: 10.1029/2019RS006885 – ident: e_1_2_11_44_1 – ident: e_1_2_11_60_1 doi: 10.1109/CVPR.2016.319 – ident: e_1_2_11_37_1 doi: 10.1109/ACCESS.2019.2930627 – volume: 7 start-page: 2399 issue: 1 year: 2006 ident: e_1_2_11_54_1 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J Mach Learn Res – volume: 15 start-page: 3221 issue: 1 year: 2014 ident: e_1_2_11_52_1 article-title: Accelerating t‐SNE using tree‐based algorithms publication-title: J Mach Learn Res – ident: e_1_2_11_14_1 doi: 10.1109/STSIVA.2019.8730244 – ident: e_1_2_11_19_1 doi: 10.1109/CompComm.2016.7925030 – ident: e_1_2_11_12_1 – ident: e_1_2_11_3_1 doi: 10.1109/TCCN.2021.3071149 – ident: e_1_2_11_42_1 doi: 10.1109/TCYB.2016.2536638 – ident: e_1_2_11_8_1 – ident: e_1_2_11_6_1 doi: 10.1139/juvs-2016-0011 – ident: e_1_2_11_16_1 doi: 10.1109/IWCMC48107.2020.9148485 – ident: e_1_2_11_40_1 doi: 10.1145/3097983.3098052 – ident: e_1_2_11_46_1 doi: 10.1016/j.neucom.2018.05.027 – ident: e_1_2_11_47_1 doi: 10.1109/TPAMI.2018.2860995 – ident: e_1_2_11_20_1 – volume: 100 start-page: 106919 issue: 4 year: 2020 ident: e_1_2_11_33_1 article-title: Clustering‐based anomaly detection in multivariate time series data publication-title: Appl Soft Comput. – volume: 8 start-page: 1 issue: 12 year: 2018 ident: e_1_2_11_25_1 article-title: Application of auxiliary classifier Wasserstein generative adversarial networks in wireless signal classification of illegal unmanned aerial vehicles publication-title: Appl Sci—Basel. – ident: e_1_2_11_29_1 doi: 10.1007/s11071-021-06208-6 – ident: e_1_2_11_34_1 – ident: e_1_2_11_11_1 doi: 10.1016/j.ymssp.2020.106683 – ident: e_1_2_11_10_1 doi: 10.1038/s41598-019-45333-6 – ident: e_1_2_11_28_1 doi: 10.1002/rnc.5131 – ident: e_1_2_11_56_1 doi: 10.1109/TNNLS.2015.2496947 – ident: e_1_2_11_18_1 doi: 10.1109/MIM.2015.7066682
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Snippet	Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal...
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SubjectTerms	Back propagation deep convolutional autoencoder Intelligent systems Invariance manifold consistency manifold regularization Manifolds positive‐unlabeled problem Radio broadcasting Regularization unauthorized broadcasting identification Upper bounds
Title	MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification
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