{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:00:33Z","timestamp":1775066433731,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"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>Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.<\/jats:p>","DOI":"10.3390\/s19102270","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T11:21:22Z","timestamp":1558005682000},"page":"2270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["A Blind Spectrum Sensing Method Based on Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9007-6030","authenticated-orcid":false,"given":"Kai","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Zhitao","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Xueqiong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11277-011-0288-5","article-title":"Internet of things: Applications and challenges in technology and standardization","volume":"58","author":"Bandyopadhyay","year":"2011","journal-title":"Wirel. 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":"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_4","unstructured":"Zhou, X., Sun, M., Li, G.Y., and Juang, B.-H. (2019, May 15). Machine Learning and Cognitive Technology for Intelligent Wireless Networks. Available online: https:\/\/pdfs.semanticscholar.org\/861c\/4753f1e61e3d84b9f44a889368d201286c47.pdf?_ga=2.182030119.1481772666.1557890123-1021550873.1538971903."},{"key":"ref_5","unstructured":"Cabric, D., Mishra, S.M., and Brodersen, R.W. (2004, January 7\u201310). Implementation issues in spectrum sensing for cognitive radios. Proceedings of the Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/TCOMM.2009.06.070402","article-title":"Eigenvalue-based spectrum sensing algorithms for cognitive radio","volume":"57","author":"Zeng","year":"2009","journal-title":"IEEE Trans. Commun."},{"key":"ref_7","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_8","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. Areas Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TCOMM.2010.112310.090306","article-title":"Optimal spectral feature detection for spectrum sensing at very low SNR","volume":"59","author":"Quan","year":"2011","journal-title":"IEEE Trans. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Arjoune, Y., and Kaabouch, N. (2018). Wideband spectrum sensing: A Bayesian Compressive Sensing Approach. Sensors, 18.","DOI":"10.3390\/s18061839"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lu, Q., Yang, S., and Liu, F. (2017). Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks. Sensors, 17.","DOI":"10.3390\/s17040661"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/78.317857","article-title":"Statistical tests for presence of cyclostationarity","volume":"42","author":"Dandawate","year":"1994","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TVT.2008.2005267","article-title":"Spectrum-sensing algorithms for cognitive radio based on statistical covariances","volume":"58","author":"Zeng","year":"2009","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.crhy.2006.07.009","article-title":"Cognitive radio: methods for the detection of free bands","volume":"7","author":"Ghozzi","year":"2006","journal-title":"C.R. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/26.3769","article-title":"Signal interception: A unifying theoretical framework for feature detection","volume":"36","author":"Gardner","year":"1988","journal-title":"IEEE Trans. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/26.126716","article-title":"Signal interception: Performance advantages of cyclic-feature detectors","volume":"40","author":"Gardner","year":"1992","journal-title":"IEEE Trans. Commun."},{"key":"ref_17","unstructured":"Han, N., Shon, S.H., Chung, J.H., and Kim, J.M. (2006, January 20\u201322). Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems. Proceedings of the 2006 8th International Conference Advanced Communication Technology, Phoenix Park, Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, K., Akbar, I.A., Bae, K.K., Um, J., Spooner, C.M., and Reed, J.H. (2007, January 17\u201320). Cyclostationary approaches to signal detection and classification in cognitive radio. Proceedings of the 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland.","DOI":"10.1109\/DYSPAN.2007.35"},{"key":"ref_19","unstructured":"Tandra, R., and Sahai, A. (2005, January 13\u201316). Fundamental limits on detection in low SNR under noise uncertainty, Wireless Networks. Proceedings of the 2005 International Conference on Wireless Networks, Communications and Mobile Computing, Maui, HI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_22","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_23","unstructured":"Tang, Y.J., Zhang, Q.Y., and Lin, W. (2010, January 23\u201325). Artificial neural network based spectrum sensing method for cognitive radio. Proceedings of the 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, China."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tumuluru, V.K., Wang, P., and Niyato, D. (2010, January 23\u201327). A neural network based spectrum prediction scheme for cognitive radio. Proceedings of the 2010 IEEE International Conference on Communications (ICC), Cape Town, South Africa.","DOI":"10.1109\/ICC.2010.5502348"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TSP.2012.2229998","article-title":"Estimation of primary user parameters in cognitive radio systems via hidden Markov model","volume":"61","author":"Choi","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_26","unstructured":"Taj, M.I., and Akil, M. (2011, January 27\u201329). Cognitive radio spectrum evolution prediction using artificial neural networks based multivariate time series modeling. Proceedings of the 17th European Wireless 2011\u2014Sustainable Wireless Technologies, Vienna, Austria."},{"key":"ref_27","unstructured":"Cheng, Q., Shi, Z., Nguyen, D., and Dutkiewicz, E. (2018). Deep Learning Network Based Spectrum Sensing Methods for OFDM Systems. arXiv."},{"key":"ref_28","unstructured":"Scherer, D., M\u00fcller, A., and Behnke, S. (2019, May 15). Evaluation of pooling operations in convolutional architectures for object recognition. Available online: http:\/\/ais.uni-bonn.de\/papers\/icann2010_maxpool.pdf."},{"key":"ref_29","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TCCN.2018.2835460","article-title":"Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors","volume":"4","author":"Rajendran","year":"2018","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_31","unstructured":"Alexander, M., Olah, C., and Tyka, M. (2019, May 15). Inceptionism: Going deeper into neural networks. Available online: https:\/\/ai.googleblog.com\/2015\/06\/inceptionism-going-deeper-into-neural.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:52:31Z","timestamp":1760187151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,16]]},"references-count":31,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102270"],"URL":"https:\/\/doi.org\/10.3390\/s19102270","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,16]]}}}