{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T06:02:46Z","timestamp":1771740166777,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T00:00:00Z","timestamp":1572393600000},"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 this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.<\/jats:p>","DOI":"10.3390\/s19214715","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"4715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios"],"prefix":"10.3390","volume":"19","author":[{"given":"Yanqueleth","family":"Molina-Tenorio","sequence":"first","affiliation":[{"name":"Master of Sciences and Information Technologies, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2661-2449","authenticated-orcid":false,"given":"Alfonso","family":"Prieto-Guerrero","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3551-1500","authenticated-orcid":false,"given":"Rafael","family":"Aguilar-Gonzalez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico"}]},{"given":"Silvia","family":"Ruiz-Boqu\u00e9","sequence":"additional","affiliation":[{"name":"Department of Signal and Theory Communications, Universitat Polit\u00e8cnica de Catalunya, 08860 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,30]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1016\/j.comnet.2006.05.001","article-title":"NeXt generation\/dynamic spectrum access\/cognitive radio wireless networks: A survey","volume":"50","author":"Akyildiz","year":"2006","journal-title":"Comput. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/SURV.2012.111412.00160","article-title":"Spectrum Decision in Cognitive Radio Networks: A Survey","volume":"15","author":"Masonta","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Arjoune, Y., and Kaabouch, N. (2019). A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions. Sensors, 19.","DOI":"10.3390\/s19010126"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JPROC.2014.2303977","article-title":"Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks","volume":"102","author":"Hattab","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"El-Khamy, S.E., El-Mahallawy, M.S., and Youssef, E.N.S. (2013, January 28\u201331). Improved wideband spectrum sensing techniques using wavelet-based edge detection for cognitive radio. Proceedings of the IEEE 2013 International Conference on Computing, Networking and Communications (ICNC), San Diego, CA, USA.","DOI":"10.1109\/ICCNC.2013.6504120"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.aeue.2017.11.024","article-title":"Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs","volume":"84","author":"Kumar","year":"2018","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Diao, X., Dong, Q., Yang, Z., and Li, Y. (2017). Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension. Algorithms, 10.","DOI":"10.3390\/a10030096"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., and Aguilar-Gonzalez, R. (2019). A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension. Sensors, 19.","DOI":"10.3390\/s19061322"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/COMST.2016.2631080","article-title":"Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications","volume":"19","author":"Ali","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","unstructured":"Zhou, X., Sun, M., Li, G.Y., and Juang, B.-H. (2017). Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/SURV.2012.100412.00017","article-title":"A Survey on Machine-Learning Techniques in Cognitive Radios","volume":"15","author":"Bkassiny","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1109\/JSAC.2013.131120","article-title":"Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks","volume":"31","author":"Thilina","year":"2013","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Oksanen, J., Lunden, J., and Koivunen, V. (September, January 29). Reinforcement learning method for energy efficient cooperative multiband spectrum sensing. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, Kittila, Finland.","DOI":"10.1109\/MLSP.2010.5589224"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Popoola, J.J., and Van Olst, R. (2011, January 13\u201315). Application of neural network for sensing primary radio signals in a cognitive radio environment. Proceedings of the IEEE Africon\u201911, Livingstone, Zambia.","DOI":"10.1109\/AFRCON.2011.6072009"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shamsi, N., Mousavinia, A., and Amirpour, H. (2013, January 7\u20139). A channel state prediction for multi-secondary users in a cognitive radio based on neural network. Proceedings of the IEEE 2013 International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Turkey.","DOI":"10.1109\/ICECCO.2013.6718263"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/TWC.2011.010411.100838","article-title":"Repeated Auctions with Bayesian Nonparametric Learning for Spectrum Access in Cognitive Radio Networks","volume":"10","author":"Han","year":"2011","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_19","first-page":"1","article-title":"Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks","volume":"2018","author":"Shah","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6853","DOI":"10.1109\/TVT.2015.2487047","article-title":"Analysis of Spectrum Occupancy Using Machine Learning Algorithms","volume":"65","author":"Azmat","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8525","DOI":"10.1109\/TVT.2018.2850799","article-title":"Optimal Resource Allocation Using Support Vector Machine for Wireless Power Transfer in Cognitive Radio Networks","volume":"67","author":"Shrestha","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sharma, V., and Bohara, V. (2014, January 24\u201327). Exploiting machine learning algorithms for cognitive radio. Proceedings of the IEEE 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, India.","DOI":"10.1109\/ICACCI.2014.6968571"},{"key":"ref_23","unstructured":"Han, J., and Kamber, M. (2012). Data Mining: Concepts and Techniques, Elsevier. [3rd ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Meila, M. (2006, January 25\u201329). The uniqueness of a good optimum for K-means. Proceedings of the 23rd International Conference on Machine Learning\u2014ICML \u201906, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143923"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.comcom.2018.02.001","article-title":"An energy efficient fair node selection for cooperative in-band and out-of-band spectrum sensing","volume":"119","author":"Shrestha","year":"2018","journal-title":"Comput. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:30:29Z","timestamp":1760189429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,30]]},"references-count":27,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19214715"],"URL":"https:\/\/doi.org\/10.3390\/s19214715","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,30]]}}}