{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:37:36Z","timestamp":1784248656206,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"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>With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.<\/jats:p>","DOI":"10.3390\/s18113913","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"3913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Mingxuan","family":"Li","sequence":"first","affiliation":[{"name":"National Digital Switching System Engineering and Technology R&amp;D Center, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ou","family":"Li","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technology R&amp;D Center, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyi","family":"Liu","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technology R&amp;D Center, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7693-0077","authenticated-orcid":false,"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technology R&amp;D Center, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/JSAC.2004.839380","article-title":"Cognitive radio: Brain-empowered wireless communications","volume":"23","author":"Haykin","year":"2005","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/98.788210","article-title":"Cognitive radio: Making software radios more personal","volume":"6","author":"Mitola","year":"1999","journal-title":"IEEE Pers. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MSP.2012.2183771","article-title":"Spectrum Sensing for Cognitive Radio: State-of-the-Art and Recent Advances","volume":"29","author":"Axell","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/S1004-4132(08)60174-7","article-title":"Classification using wavelet packet decomposition and support vector machine for digital modulations","volume":"19","author":"Fucai","year":"2008","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/LCOMM.2011.112311.112006","article-title":"Cyclostationarity-Based Robust Algorithms for QAM Signal Identification","volume":"16","author":"Dobre","year":"2012","journal-title":"IEEE Commun. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1109\/TWC.2008.070015","article-title":"Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels","volume":"7","author":"Wu","year":"2008","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shimaoka, S., Stenetorp, P., Inui, K., and Riedel, S. (arXiv, 2016). Neural Architectures for Fine-grained Entity Type Classification, arXiv.","DOI":"10.18653\/v1\/W16-1313"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, D., and Deng, L. (2014). Automatic Speech Recognition: A Deep Learning Approach, Springer.","DOI":"10.1007\/978-1-4471-5779-3"},{"key":"ref_9","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_10","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","first-page":"431","article-title":"Automatic Modulation Recognition of Communication Signals","volume":"46","author":"Azzouz","year":"1996","journal-title":"IEEE Trans. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016, January 2\u20135). Convolutional Radio Modulation Recognition Networks. Proceedings of the International Conference on Engineering Applications of Neural Networks, Aberdeen, UK.","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An Introduction to Deep Learning for the Physical Layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. Cognit. Commum. Netw."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1049\/el.2016.0876","article-title":"Joint modulation format\/bit-rate classification and signal-to-noise ratio estimation in multipath fading channels using deep machine learning","volume":"52","author":"Khan","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/LCOMM.2018.2809732","article-title":"Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks","volume":"22","author":"Li","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15713","DOI":"10.1109\/ACCESS.2018.2815741","article-title":"Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks","volume":"6","author":"Tang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, D., Ding, W., Zhang, B., Xie, C., Li, H., Liu, C., and Han, J. (2018). Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles. Sensors, 18.","DOI":"10.3390\/s18030924"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long Short-Term Memory, Springer.","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hauser, S.C., Headley, W.C., and Michaels, A.J. (2017, January 23\u201325). Signal detection effects on deep neural networks utilizing raw IQ for modulation classification. Proceedings of the MILCOM 2017\u20132017 IEEE Military Communications Conference (MILCOM), Baltimore, MD, USA.","DOI":"10.1109\/MILCOM.2017.8170853"},{"key":"ref_21","unstructured":"Mirza, M., and Osindero, S. (2014, January 8\u201313). Conditional Generative Adversarial Nets. Proceedings of the Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_22","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, PR, USA."},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_24","unstructured":"Larsen, A.B.L., Larochelle, H., and Winther, O. (2016, January 19\u201324). Autoencoding beyond pixels using a learned similarity metric. Proceedings of the International Conference on International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_25","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (arXiv, 2016). Spatial Transformer Networks, arXiv."},{"key":"ref_26","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (arXiv, 2016). Improved Techniques for Training GANs, arXiv."},{"key":"ref_27","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (arXiv, 2017). Wasserstein GAN, arXiv."},{"key":"ref_28","unstructured":"Odena, A., Olah, C., and Shlens, J. (arXiv, 2016). Conditional Image Synthesis With Auxiliary Classifier GANs, arXiv."},{"key":"ref_29","unstructured":"O\u2019Shea, T.J., and West, N. (2016, January 6). Radio Machine Learning Dataset Generation with GNU Radio. Proceedings of the 6th GNU Radio Conference, Boulder, CO, USA."},{"key":"ref_30","unstructured":"Chollet, F. (2015, November 24). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_31","unstructured":"Abadi, M.A.A. (2015, June 04). Tensorflow: Large-Scale Machine Learning on Heterogeneous Systems, Software. Available online: http:\/\/tensorflow.org\/."},{"key":"ref_32","unstructured":"Goodfellow, I. (arXiv, 2016). NIPS 2016 Tutorial: Generative Adversarial Networks, arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bergstra, J.S., Yamins, D., and Cox, D.D. (2015, May 23). Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. Available online: http:\/\/hyperopt.github.io\/.","DOI":"10.1088\/1749-4699\/8\/1\/014008"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pelikan, M. (2005). Bayesian Optimization Algorithm. Hierarchical Bayesian Optimization Algorithm, Springer.","DOI":"10.1007\/b10910"},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014, January 14\u201316). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_36","unstructured":"Zhu, X. (2003, January 21\u201324). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International conference on Machine learning (ICML-03), Washington, DC, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2009, January 10\u201312). When Semi-supervised Learning Meets Ensemble Learning. Proceedings of the International Workshop on Multiple Classifier Systems, Reykjavik, Iceland.","DOI":"10.1007\/978-3-642-02326-2_53"},{"key":"ref_38","unstructured":"Valle, R., Cai, W., and Doshi, A. (arXiv, 2018). TequilaGAN: How to easily identify GAN samples, arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3913\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:30Z","timestamp":1760196570000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3913"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,13]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113913"],"URL":"https:\/\/doi.org\/10.3390\/s18113913","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,13]]}}}