{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:09:50Z","timestamp":1783184990412,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T00:00:00Z","timestamp":1564444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11604048"],"award-info":[{"award-number":["11604048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11704069"],"award-info":[{"award-number":["11704069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11574048"],"award-info":[{"award-number":["11574048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11674057"],"award-info":[{"award-number":["11674057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242019K30021"],"award-info":[{"award-number":["2242019K30021"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242016K30013"],"award-info":[{"award-number":["2242016K30013"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.<\/jats:p>","DOI":"10.3390\/e21080745","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T11:15:56Z","timestamp":1564485356000},"page":"745","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-0774","authenticated-orcid":false,"given":"Yangjie","family":"Wei","sequence":"first","affiliation":[{"name":"Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiliang","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1049\/iet-com:20050176","article-title":"Survey of automatic modulation classification techniques: Classical approaches and new trends","volume":"1","author":"Dobre","year":"2007","journal-title":"IET Commun."},{"key":"ref_2","unstructured":"Kim, K., and Polydoros, A. (1998, January 23\u201326). Digital modulation classification: The BPSK and QPSK case. Proceedings of the MILCOM 88, 21st Century Military Communications\u2014What\u2019s Possible? Conference Record. Military Communications Conference, San Diego, CA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/26.664294","article-title":"Algorithms for automatic modulation recognition of communication signals","volume":"334","author":"Nandi","year":"1998","journal-title":"IEEE Trans. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, J., Chae, H., Yoon, D., and Choi, J.W. (2016, January 19\u201321). Deep neural network-based auomatic modulation classification technique. Proceedings of the 2016 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, South Korea.","DOI":"10.1109\/ICTC.2016.7763537"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.dsp.2017.09.005","article-title":"Automatic modulation classification of digital modulation signals with stacked autoencoders","volume":"71","author":"Ali","year":"2017","journal-title":"Digit. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.apacoust.2018.03.033","article-title":"Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis","volume":"138","author":"Jiang","year":"2018","journal-title":"Appl. Acoust."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1049\/ip-rsn:20000492","article-title":"Modulation identification of digital signals by the wavelet transform","volume":"147","author":"Ho","year":"2000","journal-title":"IEE Proc. Radar Sonar Navig."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/26.58753","article-title":"On the detection and classification of quadrature digital modulations in broad-band noise","volume":"38","author":"Polydoros","year":"1990","journal-title":"IEEE Trans. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1016\/j.sigpro.2010.03.002","article-title":"Separation of digital communication mixtures with the CMA: Case of unknown rates","volume":"90","author":"Jallon","year":"2010","journal-title":"Digit. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1109\/26.141456","article-title":"Signal classification using statistical moments","volume":"40","author":"Soliman","year":"1992","journal-title":"IEEE Trans. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1109\/LCOMM.2018.2840147","article-title":"On the classification of binary space shift keying modulation","volume":"22","author":"Oner","year":"2018","journal-title":"IEEE Trans. Commun. Lett."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/0165-1684(89)90093-5","article-title":"Identification of the modulation type of a signal","volume":"16","author":"Chan","year":"1989","journal-title":"Signal Process."},{"key":"ref_14","first-page":"2742","article-title":"Automatic modulation classification using combination of genetic programming and KNN","volume":"11","author":"Aslam","year":"2012","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1109\/LCOMM.2018.2806489","article-title":"Automatic modulation classification using moments and likelihood maximization","volume":"22","author":"Aboutaleb","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., and Ying, Y. (2014, January 15\u201317). Radar Signal Recognition Algorithm Based on Entropy Theory. Proceedings of the 2014 2nd International Conference on Systems and Informatics (ICSAI 2014), Shanghai, China.","DOI":"10.1109\/ICSAI.2014.7009379"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s11036-018-1000-8","article-title":"A New Method of Cognitive Signal Recognition Based on Hybrid Information Entropy and D-S Evidence Theory","volume":"23","author":"Wang","year":"2018","journal-title":"Mob. Netw. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Lu, M., Liu, G., and Pan, Z. (2017). A Novel Distance Metric: Generalized Relative Entropy. Entropy, 19.","DOI":"10.3390\/e19060269"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1109\/TASSP.1986.1164951","article-title":"Measurement of Spectral Correlation","volume":"34","author":"Gardner","year":"1986","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TCOM.1987.1096820","article-title":"Spectral Correlation of Modulated Signals: Part I-Analog Modulation","volume":"35","author":"Gardner","year":"1987","journal-title":"IEEE Trans. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TCOM.1987.1096816","article-title":"Spectral Correlation of Modulated Signals: Part 11-Digital Modulation","volume":"35","author":"Gardner","year":"1987","journal-title":"IEEE Trans. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TIFS.2011.2159000","article-title":"Modulation Recognition in Continuous Phase Modulation Using Approximate Entropy","volume":"6","author":"Pawar","year":"2011","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_23","first-page":"1","article-title":"A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals","volume":"2015","author":"Li","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_24","first-page":"40","article-title":"A Study of Wavelet Entropy Theory and its Application in Electric Power System Fault Detection","volume":"5","author":"He","year":"2005","journal-title":"Proc. CSEE"},{"key":"ref_25","first-page":"15","article-title":"Application of approximate entropy to cross-country fault detection in distribution networks","volume":"7","author":"Jiang","year":"2015","journal-title":"Power Syst. Prot. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.5815\/ijigsp.2011.05.05","article-title":"Emotion recognition method using entropy analysis of EEG signals","volume":"5","author":"Hosseini","year":"2011","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, X., Yu, J., Yang, X., and Yang, H. (2019). The data-driven optimization method and its application in feature extraction of ship-radiated noise with sample entropy. Energies, 12.","DOI":"10.3390\/en12030359"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, L., Li, X., and Yang, X. (2019). A novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise, duffing chaotic oscillator, and weighted-permutation entropy. Entropy, 21.","DOI":"10.3390\/e21050507"},{"key":"ref_29","first-page":"297","article-title":"Renyi extrapolation of Shannon entropy","volume":"10","author":"Zyczkowski","year":"2013","journal-title":"Physics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"10","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shang, X., and Veldhuis, R.N.J. (2008, January 1\u20133). Grip-Pattern Recognition in Smart Gun Based on Likelihood-Ratio Classifier and Support Vector Machine. Proceedings of the ICISP\u201908 Proceedings of the 3rd International Conference on Image and Signal Processing, Cherbourg-Octeville, France.","DOI":"10.1007\/978-3-540-69905-7_33"},{"key":"ref_32","first-page":"144","article-title":"Support vector networks","volume":"20","author":"Burges","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wei, J., Huang, Z., Su, S., and Zuo, Z. (2016). Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition. Entropy, 18.","DOI":"10.3390\/e18010030"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/8\/745\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:05Z","timestamp":1760188265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/8\/745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,30]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["e21080745"],"URL":"https:\/\/doi.org\/10.3390\/e21080745","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,30]]}}}