{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:33:50Z","timestamp":1782124430839,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2016QY11W2003"],"award-info":[{"award-number":["2016QY11W2003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2018JJ3607"],"award-info":[{"award-number":["2018JJ3607"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51575517"],"award-info":[{"award-number":["51575517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Technology Foundation Project","award":["181GF22006"],"award-info":[{"award-number":["181GF22006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree\u2013support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.<\/jats:p>","DOI":"10.3390\/s20051438","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T09:26:41Z","timestamp":1583486801000},"page":"1438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Modulation Classification Using Compressed Sensing and Decision Tree\u2013Support Vector Machine in Cognitive Radio System"],"prefix":"10.3390","volume":"20","author":[{"given":"Xiaoyong","family":"Sun","sequence":"first","affiliation":[{"name":"College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojing","family":"Su","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zuo","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, X., and Fujii, T. (2017, January 15\u201318). A modulation classification method in cognitive radios system using stacked denoising sparse autoencoder. Proceedings of the 2017 IEEE Radio and Wireless Symposium (RWS), Phoenix, AZ, USA.","DOI":"10.1109\/RWS.2017.7885992"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, S., Shen, B., Wang, X., and Yoo, S. (2019). A strong machine learning classifier and decision stumps based hybrid adaBoost classification algorithm for cognitive radios. Sensors, 19.","DOI":"10.3390\/s19235077"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/JSAC.2008.080103","article-title":"Cyclostationary signatures in practical cognitive radio applications","volume":"26","author":"Sutton","year":"2008","journal-title":"IEEE J. Sel. Area. Comm."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, W. (2009). Cyclostationary Approach to Signal Detection and Classification in Cognitive Radio Systems. Cognitive Radio Systems, Beijing University of Posts and Telecommunications.","DOI":"10.5772\/159"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TWC.2007.05775","article-title":"Cooperative spectrum sensing in cognitive radio Part I: Two users networks","volume":"6","author":"Ganesan","year":"2007","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/JPROC.2009.2015707","article-title":"Signal Processing in Cognitive Radio","volume":"97","author":"Ma","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MSP.2007.361604","article-title":"A Survey of Dynamic Spectrum Access","volume":"24","author":"Zhao","year":"2007","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_8","first-page":"4","article-title":"SNR walls for signal detection","volume":"2","author":"Tandra","year":"2008","journal-title":"IEEE J-STSP"},{"key":"ref_9","unstructured":"Li, C., Xiao, J., and Xu, Q. (2011, January 14\u201316). A novel modulation classification for PSK and QAM signals in wireless communication. Proceedings of the IET International Conference on Communication Technology and Application (ICCTA), Beijing, China."},{"key":"ref_10","unstructured":"Liu, J., and Luo, Q. (2012, January 9\u201311). A novel modulation classification algorithm based on daubechies5 wavelet and fractional fourier transform in cognitive radio. Proceedings of the IEEE 14th International Conference on Communication Technology, Chengdu, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S1005-8885(10)60077-5","article-title":"Automatic modulation classification based on the combination of clustering and neural network","volume":"18","author":"Liu","year":"2011","journal-title":"J. China Univ. Posts Telecommun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, D., Wang, Z., Liu, G., and Lv, H. (2017, January 5\u20136). A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals. Proceedings of the International Conference on Machine Learning and Intelligent Communications (MLICOM), Weihai, China.","DOI":"10.1007\/978-3-319-73564-1_37"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, L., and Xu, J. (2006, January 22\u201324). A novel modulation classification method based on high order cumulants. Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China.","DOI":"10.1109\/WiCOM.2006.157"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, H., and Cai, Q. (2008, January 12\u201314). Performance analysis and optimization of novel high-order statistic features in modulation classification. Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalin, China.","DOI":"10.1109\/WiCom.2008.333"},{"key":"ref_15","first-page":"132","article-title":"The modulation recognition based on decision-making mechanism and neural network integrated classifier","volume":"19","author":"Yuan","year":"2013","journal-title":"High Technol. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, N., Liu, B., Guo, S., and Luo, R. (2010, January 13\u201314). Investigation on signal modulation recognition in the low SNR. Proceedings of the International Conference on Measuring Technology and Mechatronics Automation, Changsha, China.","DOI":"10.1109\/ICMTMA.2010.444"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yoo, Y., and Baek, J. (2018). A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network. Appl. Sci., 8.","DOI":"10.3390\/app8071102"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, S., Sun, Z., Liu, S., Chen, X., and Wang, W. (2014, January 21\u201323). Modulation classification of linear digital signals based on compressive sensing using high-order moments. Proceedings of the European Modeling Symposium, Pisa, Italy.","DOI":"10.1109\/EMS.2014.25"},{"key":"ref_19","unstructured":"Zhang, X. (2015). Modern Signal Processing, Tsinghua University. [3rd ed.]."},{"key":"ref_20","first-page":"14","article-title":"High order modulation format identification based on compressed sensing in optical fiber communication system","volume":"14","author":"Hui","year":"2016","journal-title":"Chin. Opt. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","article-title":"Compressive Sensing","volume":"24","author":"Baraniuk","year":"2007","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1109\/TIT.2005.858979","article-title":"Decoding by linear programming","volume":"51","author":"Candes","year":"2005","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1002\/cpa.20124","article-title":"Stable signal recovery from incomplete and inaccurate measurement","volume":"59","author":"Candes","year":"2006","journal-title":"Commun. Pur. Appl. Math."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.sigpro.2005.05.029","article-title":"Extensions of compressed sensing","volume":"86","author":"Tsaig","year":"2006","journal-title":"Signal Process."},{"key":"ref_26","first-page":"58","article-title":"Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing","volume":"6","author":"Tian","year":"2012","journal-title":"IEEE J-STSP"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kirolos, S., Laska, J., Wakin, M., Duarte, M., Baron, D., Ragheb, T., Massoud, Y., and Baraniuk, R. (2006, January 29\u201330). Analog-to-information conversion via random demodulation. Proceedings of the IEEE Dallas\/CAS Workshop on Design, Application, Integration and Software, Richardson, TX, USA.","DOI":"10.1109\/DCAS.2006.321036"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"25377","DOI":"10.1109\/ACCESS.2018.2825603","article-title":"Spatio-temporal spectrum sensing in cognitive radio networks using beamformer-aided SVM algorithms","volume":"6","author":"Awe","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"408","article-title":"Establishing a classification system for high fall-risk among inpatients using support vector machines","volume":"35","author":"Yokota","year":"2017","journal-title":"CIN Comput. Inform. Nurs."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, W. (2014, January 16\u201323). Automatic modulation classification based on statistical features and support vector machine. Proceedings of the URSI General Assembly and Scientific Symposium (URSI GASS), Beijing, China.","DOI":"10.1109\/URSIGASS.2014.6929232"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:41Z","timestamp":1760173481000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,6]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051438"],"URL":"https:\/\/doi.org\/10.3390\/s20051438","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,6]]}}}