{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T18:41:34Z","timestamp":1768675294782,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Pers Commun"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11277-020-08013-7","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T09:02:59Z","timestamp":1609664579000},"page":"281-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Deep Neural Network Model for Hybrid Spectrum Sensing in Cognitive Radio"],"prefix":"10.1007","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7768-8953","authenticated-orcid":false,"given":"A.","family":"Nasser","sequence":"first","affiliation":[]},{"given":"M.","family":"Chaitou","sequence":"additional","affiliation":[]},{"given":"A.","family":"Mansour","sequence":"additional","affiliation":[]},{"given":"K. C.","family":"Yao","sequence":"additional","affiliation":[]},{"given":"H.","family":"Charara","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"issue":"4","key":"8013_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/98.788210","volume":"6","author":"J Mitolal","year":"1999","unstructured":"Mitolal, J. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communication, 6(4), 13\u201318.","journal-title":"IEEE Personal Communication"},{"issue":"1","key":"8013_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/SURV.2009.090109","volume":"11","author":"T Yucek","year":"2009","unstructured":"Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communication Surveys & Tutorials, 11(1), 116\u2013130. First Quarter.","journal-title":"IEEE Communication Surveys & Tutorials"},{"issue":"4","key":"8013_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/MWC.2007.4300983","volume":"14","author":"C Clancy","year":"2007","unstructured":"Clancy, C., Hecker, J., Stuntebeck, E., & O\u2019Shea, T. (2007). Applications of machine learning to cognitive radio networks. IEEE Wireless Communications, 14(4), 47\u201352.","journal-title":"IEEE Wireless Communications"},{"issue":"11","key":"8013_CR4","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1109\/JSAC.2013.131120","volume":"31","author":"KM Thilina","year":"2013","unstructured":"Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2209\u20132221.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"8013_CR5","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhu, P., Wang, D., & Fattouche M. (2016). Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks. In 2016 IEEE wireless communications and networking conference (pp. 1\u20136).","DOI":"10.1109\/WCNC.2016.7564840"},{"key":"8013_CR6","doi-asserted-by":"crossref","unstructured":"Vyas, M. R., Patel, D.\u00a0K., & Lopez-Benitez, M. (2017). Artificial neural network based hybrid spectrum sensing scheme for cognitive radio. In 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC) (pp. 1\u20137).","DOI":"10.1109\/PIMRC.2017.8292449"},{"key":"8013_CR7","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhang, Q., & Lin, W. (2010). Artificial neural network based spectrum sensing method for cognitive radio. In 2010 6th international conference on wireless communications networking and mobile computing (WiCOM) (pp. 1\u20134).","DOI":"10.1109\/WICOM.2010.5601105"},{"issue":"19","key":"8013_CR8","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1049\/iet-com.2018.5245","volume":"12","author":"Z Li","year":"2018","unstructured":"Li, Z., Wu, W., Liu, X., & Qi, P. (2018). Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks. IET Communications, 12(19), 2485\u20132492.","journal-title":"IET Communications"},{"issue":"8","key":"8013_CR9","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1109\/LCOMM.2018.2842779","volume":"22","author":"C Guo","year":"2018","unstructured":"Guo, C., Jin, M., Guo, Q., & Li, Y. (2018). Spectrum sensing based on combined eigenvalue and eigenvector through blind learning. IEEE Communications Letters, 22(8), 1636\u20131639.","journal-title":"IEEE Communications Letters"},{"key":"8013_CR10","doi-asserted-by":"crossref","unstructured":"Shah, I., & Koo, H. A. (2018). Reliable machine learning based spectrum sensing in cognitive radio networks. Wireless Communications and Mobile Computing, 2018","DOI":"10.1155\/2018\/5906097"},{"key":"8013_CR11","doi-asserted-by":"crossref","unstructured":"Ahmad, H. B. (2019). Ensemble classifier based spectrum sensing in cognitive radio networks. Wireless Communications and Mobile Computing, 2018","DOI":"10.1155\/2019\/9250562"},{"key":"8013_CR12","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., Aguilar-Gonzalez, R., & Ruiz-Boqu\u00e9, S. (2019). Machine learning techniques applied to multiband spectrum sensing in cognitive radios. Sensors, 19(21).","DOI":"10.3390\/s19214715"},{"issue":"17","key":"8013_CR13","first-page":"25","volume":"45","author":"SF Shirazi","year":"2012","unstructured":"Shirazi, S. F., Shirazi, S. H., Shah, S. M., & Shahid, M. K. (2012). Article: Hybrid spectrum sensing algorithm for cognitive radio network. International Journal of Computer Applications, 45(17), 25\u201330.\u00a0","journal-title":"International Journal of Computer Applications"},{"key":"8013_CR14","doi-asserted-by":"crossref","unstructured":"Moghimi, F., Schober, R., & Mallik, R.\u00a0K. (2010). Hybrid coherent\/energy detection for cognitive radio networks. In 2010 IEEE global telecommunications conference GLOBECOM 2010 (pp. 1\u20136).","DOI":"10.1109\/GLOCOM.2010.5683956"},{"issue":"7","key":"8013_CR15","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1049\/iet-spr.2015.0006","volume":"10","author":"M Cardenas-Juarez","year":"2016","unstructured":"Cardenas-Juarez, M., Ghogho, M., Pineda-Rico, U., & Stevens-Navarro, E. (2016). Improved semi-blind spectrum sensing for cognitive radio with locally optimum detection. IET Signal Processing, 10(7), 524\u2013531.","journal-title":"IET Signal Processing"},{"issue":"1","key":"8013_CR16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TCOMM.2006.887483","volume":"55","author":"F Digham","year":"2007","unstructured":"Digham, F., Alouini, M.-S., & Simon, K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21\u201324.","journal-title":"IEEE Transactions on Communications"},{"issue":"2","key":"8013_CR17","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TVT.2009.2035628","volume":"59","author":"M Naraghi-Poor","year":"2010","unstructured":"Naraghi-Poor, M., & Ikuma, T. (2010). Autocorrelation-based spectrum sensing for cognitive radio. IEEE Transactions on Vehicular Technology, 59(2), 718\u2013733.","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"8013_CR18","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1109\/TCOMM.2009.06.070402","volume":"57","author":"Y Zeng","year":"2009","unstructured":"Zeng, Y., & Liang, Y.-C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784\u20131793.","journal-title":"IEEE Transactions on Communications"},{"issue":"1","key":"8013_CR19","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13634-017-0475-y","volume":"2017","author":"A Nasser","year":"2017","unstructured":"Nasser, A., Mansour, A., Yao, K. C., Abdallah, H., & Charara, H. (2017). Spectrum sensing based on cumulative power spectral density. EURASIP Journal on Advances in Signal Processing, 2017(1), 38.","journal-title":"EURASIP Journal on Advances in Signal Processing"},{"issue":"3","key":"8013_CR20","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1049\/el.2014.3579","volume":"51","author":"D Teguig","year":"2015","unstructured":"Teguig, D., Le Nir, V., & Scheers, B. (2015). Spectrum sensing method based on the likelihood ratio goodness of fit test. IEEE Electronic Letters, 51(3), 253\u2013255.","journal-title":"IEEE Electronic Letters"},{"key":"8013_CR21","unstructured":"Guo, C., & Berkhahn, F. (2016). Entity embeddings of categorical variables. arXiv, http:\/\/arxiv.org\/abs\/1604.06737."},{"key":"8013_CR22","unstructured":"Zhu, D., Yao, H., Jiang, B., & Yu, P. (2018). Negative log likelihood ratio loss for deep neural network classification. arXiv, http:\/\/arxiv.org\/abs\/1804.10690."},{"key":"8013_CR23","unstructured":"Labach, A., Salehinejad, H. & Valaee, S. (2019). Survey of dropout methods for deep neural networks. arXiv, http:\/\/arxiv.org\/abs\/1904.13310."},{"key":"8013_CR24","unstructured":"Ioffe, S. & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv, http:\/\/arxiv.org\/abs\/1502.03167."},{"key":"8013_CR25","unstructured":"Arora, R., Basu, A., Mianjy, P. & Mukherjee, A. (2016). Understanding deep neural networks with rectified linear units. arXiv, http:\/\/arxiv.org\/abs\/1611.01491."},{"key":"8013_CR26","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv, http:\/\/arxiv.org\/abs\/1609.04747."},{"key":"8013_CR27","doi-asserted-by":"crossref","unstructured":"Markidis, S., Der Chien, S. W., Laure, E., Peng, I. B., & Vetter, J. S. (2018). Nvidia tensor core programmability, performance & precision. In 2018 IEEE international parallel and distributed processing symposium workshops (IPDPSW) (pp. 522\u2014531).","DOI":"10.1109\/IPDPSW.2018.00091"},{"key":"8013_CR28","unstructured":"Smith, L.\u00a0N. (2015). Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV) (pp. 464\u2013472)."},{"issue":"12","key":"8013_CR29","doi-asserted-by":"publisher","first-page":"3410","DOI":"10.1109\/TCOMM.2010.11.090209","volume":"58","author":"G Zhang","year":"2010","unstructured":"Zhang, G., Wang, X., Liang, Y.-C., & Liu, J. (2010). Fast and robust spectrum sensing via Kolmogorov\u2013Smirnov test. IEEE Transactions on Communications, 58(12), 3410\u20133416.","journal-title":"IEEE Transactions on Communications"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-020-08013-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-020-08013-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-020-08013-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T16:12:45Z","timestamp":1618848765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-020-08013-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,3]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["8013"],"URL":"https:\/\/doi.org\/10.1007\/s11277-020-08013-7","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,3]]},"assertion":[{"value":"27 November 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}