{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T14:10:43Z","timestamp":1777212643841,"version":"3.51.4"},"reference-count":74,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T00:00:00Z","timestamp":1692489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time\u2013frequency images as inputs to convolutional neural networks (CNNs) or recurrent neural networks (RNNs). However, with the advancement of graph neural networks (GNNs), a new approach has been introduced involving transforming time series data into graph structures. In this study, we propose a CNN-transformer graph neural network (CTGNet) for modulation classification, to uncover complex representations in signal data. First, we apply sliding window processing to the original signals, obtaining signal subsequences and reorganizing them into a signal subsequence matrix. Subsequently, we employ CTGNet, which adaptively maps the preprocessed signal matrices into graph structures, and utilize a graph neural network based on GraphSAGE and DMoNPool for classification. Extensive experiments demonstrated that our method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. This underscores CTGNet\u2019s significant advantage in capturing key features in signal data and providing an effective solution for modulation classification tasks.<\/jats:p>","DOI":"10.3390\/s23167281","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T01:49:34Z","timestamp":1692582574000},"page":"7281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2591-6914","authenticated-orcid":false,"given":"Dong","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5115-825X","authenticated-orcid":false,"given":"Meiyan","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8758-9804","authenticated-orcid":false,"given":"Xiaoxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9745-4177","authenticated-orcid":false,"given":"Yonghui","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, Z., and Nandi, A.K. (2015). Automatic Modulation Classification: Principles, Algorithms and Applications, John Wiley & Sons.","DOI":"10.1002\/9781118906507"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67366","DOI":"10.1109\/ACCESS.2020.2986330","article-title":"Deep learning for modulation recognition: A survey with a demonstration","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TVT.2010.2041805","article-title":"Software-defined radio equipped with rapid modulation recognition","volume":"59","author":"Xu","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","unstructured":"Panagiotou, P., Anastasopoulos, A., and Polydoros, A. (2000, January 22\u201325). Likelihood ratio tests for modulation classification. Proceedings of the MILCOM 2000 Proceedings, 21st Century Military Communications, Architectures and Technologies for Information Superiority (Cat. No. 00CH37155), Los Angeles, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5884","DOI":"10.1109\/TWC.2009.12.080883","article-title":"On the likelihood-based approach to modulation classification","volume":"8","author":"Hameed","year":"2009","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.1109\/TWC.2017.2704124","article-title":"Online hybrid likelihood based modulation classification using multiple sensors","volume":"16","author":"Dulek","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","first-page":"156","article-title":"A blind preprocessor for modulation classification applications in frequency-selective non-Gaussian channels","volume":"63","author":"Amuru","year":"2014","journal-title":"IEEE Trans. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TCCN.2018.2835460","article-title":"Deep learning models for wireless signal classification with distributed low-cost spectrum sensors","volume":"4","author":"Rajendran","year":"2018","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Moser, E., Moran, M.K., Hillen, E., Li, D., and Wu, Z. (2015, January 15\u201319). Automatic modulation classification via instantaneous features. Proceedings of the 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA.","DOI":"10.1109\/NAECON.2015.7443070"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1049\/iet-com.2014.0773","article-title":"Cumulants-based modulation classification technique in multipath fading channels","volume":"9","author":"Chang","year":"2015","journal-title":"IET Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hassan, K., Dayoub, I., Hamouda, W., and Berbineau, M. (2009, January 20\u201322). Automatic modulation recognition using wavelet transform and neural network. Proceedings of the 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST), Lille, France.","DOI":"10.1109\/ITST.2009.5399351"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TWC.2016.2623716","article-title":"Low complexity automatic modulation classification based on order-statistics","volume":"16","author":"Han","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/26.837045","article-title":"Hierarchical digital modulation classification using cumulants","volume":"48","author":"Swami","year":"2000","journal-title":"IEEE Trans. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MCAS.2008.931739","article-title":"Automatic modulation classification for cognitive radios using cyclic feature detection","volume":"9","author":"Ramkumar","year":"2009","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_15","first-page":"29","article-title":"Automatic recognition of analog modulated signals using artificial neural networks","volume":"2","author":"Popoola","year":"2011","journal-title":"Comput. Technol. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., and Macherey, K. (2016). Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gao, C., Wang, X., He, X., and Li, Y. (2022, January 21\u201325). Graph neural networks for recommender system. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Tempe, AZ, USA.","DOI":"10.1145\/3488560.3501396"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qian, X., Lin, S., Cheng, G., Yao, X., Ren, H., and Wang, W. (2020). Object detection in remote sensing images based on improved bounding box regression and multi-level features fusion. Remote Sens., 12.","DOI":"10.3390\/rs12010143"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/JSTARS.2022.3229834","article-title":"Hyperspectral anomaly detection via sparse representation and collaborative representation","volume":"16","author":"Lin","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, S., Zhang, M., Cheng, X., Wang, L., Xu, M., and Wang, H. (2022). Hyperspectral anomaly detection via dual dictionaries construction guided by two-stage complementary decision. Remote Sens., 14.","DOI":"10.3390\/rs14081784"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JSTARS.2022.3214508","article-title":"Dual Collaborative Constraints Regularized Low-Rank and Sparse Representation via Robust Dictionaries Construction for Hyperspectral Anomaly Detection","volume":"16","author":"Lin","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.r., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_26","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016, January 2\u20135). Convolutional radio modulation recognition networks. Proceedings of the Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK. Proceedings 17."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","article-title":"Over-the-air deep learning based radio signal classification","volume":"12","author":"Roy","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hong, D., Zhang, Z., and Xu, X. (2017, January 13\u201316). Automatic modulation classification using recurrent neural networks. Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/CompComm.2017.8322633"},{"key":"ref_30","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","first-page":"1","article-title":"Graph clustering with graph neural networks","volume":"24","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_33","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_35","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_36","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_37","first-page":"4805","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"Ying","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1109\/LCOMM.2021.3102656","article-title":"An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1109\/LWC.2020.2999453","article-title":"A spatiotemporal multi-channel learning framework for automatic modulation recognition","volume":"9","author":"Xu","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/LWC.2018.2855749","article-title":"A learnable distortion correction module for modulation recognition","volume":"8","author":"Yashashwi","year":"2018","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, X., Yang, D., and El Gamal, A. (November, January 29). Deep neural network architectures for modulation classification. Proceedings of the 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2017.8335483"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","first-page":"012052","article-title":"Multi-modulation recognition using convolution gated recurrent unit networks","volume":"Volume 1284","author":"Jiyuan","year":"2019","journal-title":"Journal of Physics: Conference Series"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TVT.2019.2951594","article-title":"Deep neural network for robust modulation classification under uncertain noise conditions","volume":"69","author":"Hu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1109\/TVT.2020.3030018","article-title":"Automatic modulation classification using CNN-LSTM based dual-stream structure","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1109\/JIOT.2021.3091523","article-title":"Multitask-learning-based deep neural network for automatic modulation classification","volume":"9","author":"Chang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9467","DOI":"10.1109\/JIOT.2022.3141032","article-title":"GS-QRNN: A high-efficiency automatic modulation classifier for cognitive radio IoT","volume":"9","author":"Ghasemzadeh","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/TWC.2021.3095855","article-title":"Real-time radio technology and modulation classification via an LSTM auto-encoder","volume":"21","author":"Ke","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_49","first-page":"5977","article-title":"An Autoencoder-based I\/Q Channel Interaction Enhancement Method for Automatic Modulation Recognition","volume":"21","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5977","DOI":"10.1109\/TWC.2022.3144608","article-title":"Modulation recognition of underwater acoustic signals using deep hybrid neural networks","volume":"21","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, S., Liu, L., and Zhao, Z. (2023). DTFTCNet: Radar Modulation Recognition with Deep Time-Frequency Transformation. IEEE Trans. Cogn. Commun. Netw., early access.","DOI":"10.1109\/TCCN.2023.3280949"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2519710","DOI":"10.1109\/TIM.2023.3290301","article-title":"Automatic Modulation Classification Using ResNeXt-GRU with Deep Feature Fusion","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"13387","DOI":"10.1109\/TVT.2022.3196103","article-title":"Spatial-Temporal Hybrid Feature Extraction Network for Few-shot Automatic Modulation Classification","volume":"71","author":"Che","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4600","DOI":"10.1109\/TWC.2022.3227518","article-title":"High-order convolutional attention networks for automatic modulation classification in communication","volume":"22","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2522310","DOI":"10.1109\/TIM.2023.3298657","article-title":"Complex-valued Depth-wise Separable Convolutional Neural Network for Automatic Modulation Classification","volume":"72","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1109\/LCOMM.2022.3145647","article-title":"Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation","volume":"26","author":"Zheng","year":"2022","journal-title":"IEEE Commun. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/LWC.2022.3162422","article-title":"RanNet: Learning residual-attention structure in CNNs for automatic modulation classification","volume":"11","author":"Pham","year":"2022","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"8713","DOI":"10.1109\/TWC.2022.3168884","article-title":"A hierarchical classification head based convolutional gated deep neural network for automatic modulation classification","volume":"21","author":"Chang","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_59","unstructured":"Wang, Z., and Oates, T. (2015, January 25\u201330). Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. Proceedings of the Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNNLS.2018.2850703","article-title":"Modulation classification based on signal constellation diagrams and deep learning","volume":"30","author":"Peng","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_61","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4074","DOI":"10.1109\/TVT.2019.2900460","article-title":"Data-driven deep learning for automatic modulation recognition in cognitive radios","volume":"68","author":"Wang","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"9921","DOI":"10.1109\/TWC.2022.3181026","article-title":"Modulation Recognition Using Signal Enhancement and Multistage Attention Mechanism","volume":"21","author":"Lin","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/LWC.2022.3140828","article-title":"Learning of time-frequency attention mechanism for automatic modulation recognition","volume":"11","author":"Lin","year":"2022","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/TCCN.2021.3120997","article-title":"SigNet: A novel deep learning framework for radio signal classification","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, B., Kim, J., Yoon, D., and Choi, J.W. (2017, January 18\u201320). Deep neural network-based blind modulation classification for fading channels. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC.2017.8191038"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1109\/JIOT.2020.3016125","article-title":"Identification of active attacks in Internet of Things: Joint model-and data-driven automatic modulation classification approach","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/LWC.2019.2963828","article-title":"Modulation recognition with graph convolutional network","volume":"9","author":"Liu","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1109\/TNSE.2022.3146836","article-title":"AvgNet: Adaptive visibility graph neural network and its application in modulation classification","volume":"9","author":"Xuan","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4972","DOI":"10.1073\/pnas.0709247105","article-title":"From time series to complex networks: The visibility graph","volume":"105","author":"Lacasa","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"046103","DOI":"10.1103\/PhysRevE.80.046103","article-title":"Horizontal visibility graphs: Exact results for random time series","volume":"80","author":"Luque","year":"2009","journal-title":"Phys. Rev. E"},{"key":"ref_73","unstructured":"Zhou, T.T., Jin, N.D., Gao, Z.K., and Luo, Y.B. (2012). Limited Penetrable Visibility Graph for Establishing Complex Network from Time Series, Acta Physica Sinica."},{"key":"ref_74","unstructured":"O\u2019shea, T.J., and West, N. (2016, January 12\u201316). Radio machine learning dataset generation with gnu radio. Proceedings of the GNU Radio Conference, Boulder, CO, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:37:52Z","timestamp":1760128672000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,20]]},"references-count":74,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167281"],"URL":"https:\/\/doi.org\/10.3390\/s23167281","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,20]]}}}