Deep radio signal clustering with interpretability analysis based on saliency map
With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situat...
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| Published in: | Digital communications and networks Vol. 10; no. 5; pp. 1448 - 1458 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.10.2024
KeAi Communications Co., Ltd |
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| ISSN: | 2352-8648, 2352-8648 |
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| Abstract | With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situation. Meanwhile, the high complexity of deep learning makes it difficult to understand the decision results of the clustering models, making it essential to conduct interpretable analysis. This paper proposed a combined loss function for unsupervised clustering based on autoencoder. The combined loss function includes reconstruction loss and deep clustering loss. Deep clustering loss is added based on reconstruction loss, which makes similar deep features converge more in feature space. In addition, a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map. Extensive experiments have been conducted on a modulated signal dataset, and the results indicate the superior performance of our proposed method over other clustering algorithms. In particular, for the simulated dataset containing six modulation modes, when the SNR is 20 dB, the clustering accuracy of the proposed method is greater than 78%. The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model. |
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| AbstractList | With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situation. Meanwhile, the high complexity of deep learning makes it difficult to understand the decision results of the clustering models, making it essential to conduct interpretable analysis. This paper proposed a combined loss function for unsupervised clustering based on autoencoder. The combined loss function includes reconstruction loss and deep clustering loss. Deep clustering loss is added based on reconstruction loss, which makes similar deep features converge more in feature space. In addition, a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map. Extensive experiments have been conducted on a modulated signal dataset, and the results indicate the superior performance of our proposed method over other clustering algorithms. In particular, for the simulated dataset containing six modulation modes, when the SNR is 20 dB, the clustering accuracy of the proposed method is greater than 78%. The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model. |
| Author | Wang, Yiran Ren, Junjie Jiao, Licheng Yang, Xiaoniu Bai, Jing Zhou, Huaji |
| Author_xml | – sequence: 1 givenname: Huaji surname: Zhou fullname: Zhou, Huaji email: 19172110640@stu.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, 710071, China – sequence: 2 givenname: Jing orcidid: 0000-0001-5412-7793 surname: Bai fullname: Bai, Jing email: baijing@mail.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, 710071, China – sequence: 3 givenname: Yiran surname: Wang fullname: Wang, Yiran email: yrwang_xd@stu.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, 710071, China – sequence: 4 givenname: Junjie surname: Ren fullname: Ren, Junjie email: renjunjie@stu.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, 710071, China – sequence: 5 givenname: Xiaoniu surname: Yang fullname: Yang, Xiaoniu email: yxn2117@126.com organization: Science and Technology on Communication Information Security Control Laboratory, Jiaxing, 314033, China – sequence: 6 givenname: Licheng surname: Jiao fullname: Jiao, Licheng email: lchjiao@xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi'an, 710071, China |
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| Copyright | 2024 Chongqing University of Posts and Telecommunications |
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| Keywords | Clustering features visualization Unsupervised radio signal clustering Autoencoder Deep learning interpretability |
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| References | MacQueen (bib13) 1967; vol. 1 Huaji, Jing, Yiran, Licheng, Zheng, Weiguo, Jie, Xiaoniu (bib37) 2022; 35 Khorov, Kiryanov, Lyakhov, Bianchi (bib39) 2018; 21 Nguyen, Bui, Duong, Bui, Luu (bib10) 2021 G. Chen, Deep Learning with Nonparametric Clustering, arXiv preprint arXiv:1501.03084. Li, Zhao, Zhou, Ding, Chen, Wang, Zhang (bib2) 2017; 24 Zhou, Khosla, Lapedriza, Oliva, Torralba (bib24) 2016 Yu, Li (bib3) 2021; 21 Xu, Wunsch (bib12) 2005; 16 Ribeiro, Singh, Guestrin (bib31) 2016 Qi, Zhou, Zheng, Li (bib38) 2020; 7 Cai, He, Han (bib40) 2010; 23 Yang, Fu, Sidiropoulos, Hong (bib16) 2017 Selvaraju, Cogswell, Das, Vedantam, Parikh, Batra (bib25) 2017 Mrabah, Khan, Ksantini, Lachiri (bib17) 2020; 130 Olah, Satyanarayan, Johnson, Carter, Schubert, Ye, Mordvintsev (bib30) 2018; 3 Yang, Parikh, Batra (bib36) 2016 Lin, Tu, Dou, Chen, Mao (bib6) 2020; 7 Li, Qiao, Zhang (bib35) 2018; 83 Ramaswamy (bib28) 2020 Zhou, Jiao, Zheng, Yang, Shen, Yang (bib5) 2020; 17 Chattopadhay, Sarkar, Howlader, Balasubramanian (bib26) 2018 Ya, Yun, Haoran, Zhang, Yu, Guan, Shiwen (bib7) 2022; 35 Von Luxburg (bib14) 2007; 17 Garson (bib22) 1991; 6 Yao, Newson, Gousseau, Hellier (bib9) 2021 Zhang, Yun, Ya, Mao (bib8) 2020; 41 Kuhn (bib42) 2005; 52 Lundberg, Lee (bib23) 2017 Xu (bib33) 2020; 28 Estévez, Tesmer, Perez, Zurada (bib41) 2009; 20 Zhang, Yang, Ma, Wu (bib19) 2019 Pinaya, Vieira, Garcia-Dias, Mechelli (bib15) 2020 Wang, Wang, Du, Yang, Zhang, Ding, Mardziel, Hu (bib27) 2020 Wang, Lin, Tian, Si (bib4) 2021; 70 Károly, Fullér, Galambos (bib11) 2018; 15 Liu, Shi, Li, Li, Zhu, Liu (bib29) 2016; 23 Shafi, Molisch, Smith, Haustein, Zhu, De Silva, Tufvesson, Benjebbour, Wunder (bib1) 2017; 35 M. R. Zafar, N. M. Khan, Dlime: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems, arXiv preprint arXiv:1906.10263. Zeiler, Fergus (bib20) 2014 Bai, Wang, Xiao, Alazab (bib18) 2022; 18 J. T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, Striving for Simplicity: the All Convolutional Net, arXiv preprint arXiv:1412.6806. Yang (10.1016/j.dcan.2023.01.010_bib16) 2017 Yu (10.1016/j.dcan.2023.01.010_bib3) 2021; 21 Bai (10.1016/j.dcan.2023.01.010_bib18) 2022; 18 Zhou (10.1016/j.dcan.2023.01.010_bib24) 2016 Yang (10.1016/j.dcan.2023.01.010_bib36) 2016 Xu (10.1016/j.dcan.2023.01.010_bib12) 2005; 16 Garson (10.1016/j.dcan.2023.01.010_bib22) 1991; 6 Zeiler (10.1016/j.dcan.2023.01.010_bib20) 2014 Olah (10.1016/j.dcan.2023.01.010_bib30) 2018; 3 Wang (10.1016/j.dcan.2023.01.010_bib27) 2020 Cai (10.1016/j.dcan.2023.01.010_bib40) 2010; 23 Li (10.1016/j.dcan.2023.01.010_bib2) 2017; 24 Károly (10.1016/j.dcan.2023.01.010_bib11) 2018; 15 Shafi (10.1016/j.dcan.2023.01.010_bib1) 2017; 35 10.1016/j.dcan.2023.01.010_bib34 Liu (10.1016/j.dcan.2023.01.010_bib29) 2016; 23 10.1016/j.dcan.2023.01.010_bib32 Ya (10.1016/j.dcan.2023.01.010_bib7) 2022; 35 Khorov (10.1016/j.dcan.2023.01.010_bib39) 2018; 21 Yao (10.1016/j.dcan.2023.01.010_bib9) 2021 Selvaraju (10.1016/j.dcan.2023.01.010_bib25) 2017 Zhang (10.1016/j.dcan.2023.01.010_bib8) 2020; 41 Zhang (10.1016/j.dcan.2023.01.010_bib19) 2019 Lin (10.1016/j.dcan.2023.01.010_bib6) 2020; 7 Xu (10.1016/j.dcan.2023.01.010_bib33) 2020; 28 Qi (10.1016/j.dcan.2023.01.010_bib38) 2020; 7 Ramaswamy (10.1016/j.dcan.2023.01.010_bib28) 2020 Li (10.1016/j.dcan.2023.01.010_bib35) 2018; 83 Pinaya (10.1016/j.dcan.2023.01.010_bib15) 2020 Von Luxburg (10.1016/j.dcan.2023.01.010_bib14) 2007; 17 Zhou (10.1016/j.dcan.2023.01.010_bib5) 2020; 17 Huaji (10.1016/j.dcan.2023.01.010_bib37) 2022; 35 Kuhn (10.1016/j.dcan.2023.01.010_bib42) 2005; 52 MacQueen (10.1016/j.dcan.2023.01.010_bib13) 1967; vol. 1 Chattopadhay (10.1016/j.dcan.2023.01.010_bib26) 2018 Lundberg (10.1016/j.dcan.2023.01.010_bib23) 2017 10.1016/j.dcan.2023.01.010_bib21 Ribeiro (10.1016/j.dcan.2023.01.010_bib31) 2016 Nguyen (10.1016/j.dcan.2023.01.010_bib10) 2021 Mrabah (10.1016/j.dcan.2023.01.010_bib17) 2020; 130 Estévez (10.1016/j.dcan.2023.01.010_bib41) 2009; 20 Wang (10.1016/j.dcan.2023.01.010_bib4) 2021; 70 |
| References_xml | – volume: 15 start-page: 29 year: 2018 end-page: 53 ident: bib11 article-title: Unsupervised clustering for deep learning: a tutorial survey publication-title: Acta Polytechnica Hungarica. – volume: 35 start-page: 35 year: 2022 end-page: 48 ident: bib7 article-title: Large-scale real-world radio signal recognition with deep learning publication-title: Chin. J. Aeronaut. – start-page: 193 year: 2020 end-page: 208 ident: bib15 article-title: Autoencoders publication-title: Machine Learning – start-page: 24 year: 2020 end-page: 25 ident: bib27 article-title: Score-cam: score-weighted visual explanations for convolutional neural networks publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops – volume: 17 start-page: 395 year: 2007 end-page: 416 ident: bib14 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. – start-page: 1135 year: 2016 end-page: 1144 ident: bib31 article-title: Why should i trust you?” explaining the predictions of any classifier publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 5147 year: 2016 end-page: 5156 ident: bib36 article-title: Joint unsupervised learning of deep representations and image clusters publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 35 start-page: 49 year: 2022 end-page: 57 ident: bib37 article-title: Few-shot electromagnetic signal classification: a data union augmentation method publication-title: Chin. J. Aeronaut. – volume: 23 start-page: 902 year: 2010 end-page: 913 ident: bib40 article-title: Locally consistent concept factorization for document clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 18 start-page: 7910 year: 2022 end-page: 7919 ident: bib18 article-title: Rffae-s: autoencoder based on random fourier feature with separable loss for unsupervised signal modulation clustering publication-title: IEEE Trans. Ind. Inf. – start-page: 618 year: 2017 end-page: 626 ident: bib25 article-title: Grad-cam: visual explanations from deep networks via gradient-based localization publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 6 start-page: 46 year: 1991 end-page: 51 ident: bib22 article-title: Interpreting Neural Network Connection Weights publication-title: AI Expert – volume: 130 start-page: 206 year: 2020 end-page: 228 ident: bib17 article-title: Deep clustering with a dynamic autoencoder: from reconstruction towards centroids construction publication-title: Neural Network. – volume: 21 start-page: 24440 year: 2021 end-page: 24452 ident: bib3 article-title: An accurate wifi indoor positioning algorithm for complex pedestrian environments publication-title: IEEE Sensor. J. – volume: vol. 1 start-page: 281 year: 1967 end-page: 297 ident: bib13 article-title: Some methods for classification and analysis of multivariate observations publication-title: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability – start-page: 6261 year: 2019 end-page: 6270 ident: bib19 article-title: Interpreting cnns via decision trees publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 17 start-page: 157 year: 2020 end-page: 169 ident: bib5 article-title: Generative adversarial network-based electromagnetic signal classification: a semi-supervised learning framework publication-title: Chin. Commun. – volume: 52 start-page: 7 year: 2005 end-page: 21 ident: bib42 article-title: The Hungarian method for the assignment problem publication-title: Nav. Res. Logist. – volume: 7 start-page: 34 year: 2020 end-page: 46 ident: bib6 article-title: Contour stella image and deep learning for signal recognition in the physical layer publication-title: IEEE Trans. Cognitive Commun. Network. – start-page: 818 year: 2014 end-page: 833 ident: bib20 article-title: Visualizing and understanding convolutional networks publication-title: European Conference on Computer Vision – start-page: 4768 year: 2017 end-page: 4777 ident: bib23 article-title: A unified approach to interpreting model predictions publication-title: Proceedings of the 31st International Conference on Neural Information Processing Systems – volume: 7 start-page: 21 year: 2020 end-page: 33 ident: bib38 article-title: Automatic modulation classification based on deep residual networks with multimodal information publication-title: IEEE Trans. Cognitive Commun. Network. – start-page: 10847 year: 2021 end-page: 10856 ident: bib10 article-title: Clusformer: a transformer based clustering approach to unsupervised large-scale face and visual landmark recognition publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 83 start-page: 161 year: 2018 end-page: 173 ident: bib35 article-title: Discriminatively boosted image clustering with fully convolutional auto-encoders publication-title: Pattern Recogn. – volume: 24 start-page: 175 year: 2017 end-page: 183 ident: bib2 article-title: Intelligent 5g: when cellular networks meet artificial intelligence publication-title: IEEE Wireless Commun. – start-page: 3861 year: 2017 end-page: 3870 ident: bib16 article-title: Towards k-means-friendly spaces: simultaneous deep learning and clustering publication-title: International Conference on Machine Learning – reference: M. R. Zafar, N. M. Khan, Dlime: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems, arXiv preprint arXiv:1906.10263. – start-page: 13789 year: 2021 end-page: 13798 ident: bib9 article-title: A latent transformer for disentangled face editing in images and videos publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – reference: J. T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, Striving for Simplicity: the All Convolutional Net, arXiv preprint arXiv:1412.6806. – reference: G. Chen, Deep Learning with Nonparametric Clustering, arXiv preprint arXiv:1501.03084. – start-page: 983 year: 2020 end-page: 991 ident: bib28 article-title: Ablation-cam: visual explanations for deep convolutional network via gradient-free localization publication-title: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision – start-page: 2921 year: 2016 end-page: 2929 ident: bib24 article-title: Learning deep features for discriminative localization publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 16 start-page: 645 year: 2005 end-page: 678 ident: bib12 article-title: Survey of clustering algorithms publication-title: IEEE Trans. Neural Network. – volume: 21 start-page: 197 year: 2018 end-page: 216 ident: bib39 article-title: A tutorial on ieee 802.11 ax high efficiency wlans publication-title: IEEEn Commun. Survey Tutorial. – volume: 20 start-page: 189 year: 2009 end-page: 201 ident: bib41 article-title: Normalized mutual information feature selection publication-title: IEEE Trans. Neural Network. – volume: 70 start-page: 790 year: 2021 end-page: 807 ident: bib4 article-title: Transfer learning promotes 6g wireless communications: recent advances and future challenges publication-title: IEEE Trans. Reliab. – start-page: 839 year: 2018 end-page: 847 ident: bib26 article-title: Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks publication-title: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE – volume: 3 start-page: e10 year: 2018 ident: bib30 article-title: The building blocks of interpretability publication-title: Distill – volume: 28 start-page: 1746 year: 2020 end-page: 1767 ident: bib33 article-title: Frequency principle: fourier analysis sheds light on deep neural networks publication-title: Commun. Comput. Phys. – volume: 41 start-page: 12 year: 2020 end-page: 21 ident: bib8 article-title: Electromagnetic signal modulation recognition technology based on lightweight deep neural network publication-title: J. Commun. – volume: 35 start-page: 1201 year: 2017 end-page: 1221 ident: bib1 article-title: 5g: a tutorial overview of standards, trials, challenges, deployment, and practice publication-title: IEEE J. Sel. Area. Commun. – volume: 23 start-page: 91 year: 2016 end-page: 100 ident: bib29 article-title: Towards better analysis of deep convolutional neural networks publication-title: IEEE Trans. Visual. Comput. Graph. – volume: 7 start-page: 34 issue: 1 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib6 article-title: Contour stella image and deep learning for signal recognition in the physical layer publication-title: IEEE Trans. Cognitive Commun. Network. doi: 10.1109/TCCN.2020.3024610 – volume: 17 start-page: 157 issue: 10 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib5 article-title: Generative adversarial network-based electromagnetic signal classification: a semi-supervised learning framework publication-title: Chin. Commun. doi: 10.23919/JCC.2020.10.011 – volume: 41 start-page: 12 issue: 11 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib8 article-title: Electromagnetic signal modulation recognition technology based on lightweight deep neural network publication-title: J. Commun. – volume: 15 start-page: 29 issue: 8 year: 2018 ident: 10.1016/j.dcan.2023.01.010_bib11 article-title: Unsupervised clustering for deep learning: a tutorial survey publication-title: Acta Polytechnica Hungarica. doi: 10.12700/APH.15.8.2018.8.2 – volume: 83 start-page: 161 year: 2018 ident: 10.1016/j.dcan.2023.01.010_bib35 article-title: Discriminatively boosted image clustering with fully convolutional auto-encoders publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2018.05.019 – volume: 52 start-page: 7 issue: 1 year: 2005 ident: 10.1016/j.dcan.2023.01.010_bib42 article-title: The Hungarian method for the assignment problem publication-title: Nav. Res. Logist. doi: 10.1002/nav.20053 – volume: 35 start-page: 35 issue: 9 year: 2022 ident: 10.1016/j.dcan.2023.01.010_bib7 article-title: Large-scale real-world radio signal recognition with deep learning publication-title: Chin. J. Aeronaut. doi: 10.1016/j.cja.2021.08.016 – ident: 10.1016/j.dcan.2023.01.010_bib21 – start-page: 193 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib15 article-title: Autoencoders – volume: 35 start-page: 49 issue: 9 year: 2022 ident: 10.1016/j.dcan.2023.01.010_bib37 article-title: Few-shot electromagnetic signal classification: a data union augmentation method publication-title: Chin. J. Aeronaut. doi: 10.1016/j.cja.2021.07.014 – start-page: 839 year: 2018 ident: 10.1016/j.dcan.2023.01.010_bib26 article-title: Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks – volume: 17 start-page: 395 issue: 4 year: 2007 ident: 10.1016/j.dcan.2023.01.010_bib14 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. doi: 10.1007/s11222-007-9033-z – start-page: 818 year: 2014 ident: 10.1016/j.dcan.2023.01.010_bib20 article-title: Visualizing and understanding convolutional networks – start-page: 2921 year: 2016 ident: 10.1016/j.dcan.2023.01.010_bib24 article-title: Learning deep features for discriminative localization – volume: 21 start-page: 197 issue: 1 year: 2018 ident: 10.1016/j.dcan.2023.01.010_bib39 article-title: A tutorial on ieee 802.11 ax high efficiency wlans publication-title: IEEEn Commun. Survey Tutorial. doi: 10.1109/COMST.2018.2871099 – start-page: 3861 year: 2017 ident: 10.1016/j.dcan.2023.01.010_bib16 article-title: Towards k-means-friendly spaces: simultaneous deep learning and clustering – start-page: 10847 year: 2021 ident: 10.1016/j.dcan.2023.01.010_bib10 article-title: Clusformer: a transformer based clustering approach to unsupervised large-scale face and visual landmark recognition – start-page: 1135 year: 2016 ident: 10.1016/j.dcan.2023.01.010_bib31 article-title: Why should i trust you?” explaining the predictions of any classifier – volume: 3 start-page: e10 issue: 3 year: 2018 ident: 10.1016/j.dcan.2023.01.010_bib30 article-title: The building blocks of interpretability publication-title: Distill doi: 10.23915/distill.00010 – start-page: 4768 year: 2017 ident: 10.1016/j.dcan.2023.01.010_bib23 article-title: A unified approach to interpreting model predictions – volume: 7 start-page: 21 issue: 1 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib38 article-title: Automatic modulation classification based on deep residual networks with multimodal information publication-title: IEEE Trans. Cognitive Commun. Network. doi: 10.1109/TCCN.2020.3023145 – start-page: 6261 year: 2019 ident: 10.1016/j.dcan.2023.01.010_bib19 article-title: Interpreting cnns via decision trees – volume: 6 start-page: 46 issue: 4 year: 1991 ident: 10.1016/j.dcan.2023.01.010_bib22 article-title: Interpreting Neural Network Connection Weights publication-title: AI Expert – volume: vol. 1 start-page: 281 year: 1967 ident: 10.1016/j.dcan.2023.01.010_bib13 article-title: Some methods for classification and analysis of multivariate observations – start-page: 5147 year: 2016 ident: 10.1016/j.dcan.2023.01.010_bib36 article-title: Joint unsupervised learning of deep representations and image clusters – volume: 16 start-page: 645 issue: 3 year: 2005 ident: 10.1016/j.dcan.2023.01.010_bib12 article-title: Survey of clustering algorithms publication-title: IEEE Trans. Neural Network. doi: 10.1109/TNN.2005.845141 – start-page: 24 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib27 article-title: Score-cam: score-weighted visual explanations for convolutional neural networks – volume: 35 start-page: 1201 issue: 6 year: 2017 ident: 10.1016/j.dcan.2023.01.010_bib1 article-title: 5g: a tutorial overview of standards, trials, challenges, deployment, and practice publication-title: IEEE J. Sel. Area. Commun. doi: 10.1109/JSAC.2017.2692307 – volume: 18 start-page: 7910 issue: 11 year: 2022 ident: 10.1016/j.dcan.2023.01.010_bib18 article-title: Rffae-s: autoencoder based on random fourier feature with separable loss for unsupervised signal modulation clustering publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2022.3171349 – volume: 21 start-page: 24440 issue: 21 year: 2021 ident: 10.1016/j.dcan.2023.01.010_bib3 article-title: An accurate wifi indoor positioning algorithm for complex pedestrian environments publication-title: IEEE Sensor. J. doi: 10.1109/JSEN.2021.3113376 – start-page: 983 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib28 article-title: Ablation-cam: visual explanations for deep convolutional network via gradient-free localization – volume: 23 start-page: 91 issue: 1 year: 2016 ident: 10.1016/j.dcan.2023.01.010_bib29 article-title: Towards better analysis of deep convolutional neural networks publication-title: IEEE Trans. Visual. Comput. Graph. doi: 10.1109/TVCG.2016.2598831 – volume: 130 start-page: 206 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib17 article-title: Deep clustering with a dynamic autoencoder: from reconstruction towards centroids construction publication-title: Neural Network. doi: 10.1016/j.neunet.2020.07.005 – ident: 10.1016/j.dcan.2023.01.010_bib32 – start-page: 618 year: 2017 ident: 10.1016/j.dcan.2023.01.010_bib25 article-title: Grad-cam: visual explanations from deep networks via gradient-based localization – ident: 10.1016/j.dcan.2023.01.010_bib34 – volume: 20 start-page: 189 issue: 2 year: 2009 ident: 10.1016/j.dcan.2023.01.010_bib41 article-title: Normalized mutual information feature selection publication-title: IEEE Trans. Neural Network. doi: 10.1109/TNN.2008.2005601 – start-page: 13789 year: 2021 ident: 10.1016/j.dcan.2023.01.010_bib9 article-title: A latent transformer for disentangled face editing in images and videos – volume: 70 start-page: 790 issue: 2 year: 2021 ident: 10.1016/j.dcan.2023.01.010_bib4 article-title: Transfer learning promotes 6g wireless communications: recent advances and future challenges publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2021.3062045 – volume: 23 start-page: 902 issue: 6 year: 2010 ident: 10.1016/j.dcan.2023.01.010_bib40 article-title: Locally consistent concept factorization for document clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2010.165 – volume: 24 start-page: 175 issue: 5 year: 2017 ident: 10.1016/j.dcan.2023.01.010_bib2 article-title: Intelligent 5g: when cellular networks meet artificial intelligence publication-title: IEEE Wireless Commun. doi: 10.1109/MWC.2017.1600304WC – volume: 28 start-page: 1746 issue: 5 year: 2020 ident: 10.1016/j.dcan.2023.01.010_bib33 article-title: Frequency principle: fourier analysis sheds light on deep neural networks publication-title: Commun. Comput. Phys. doi: 10.4208/cicp.OA-2020-0085 |
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| Title | Deep radio signal clustering with interpretability analysis based on saliency map |
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