{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:50:08Z","timestamp":1781797808914,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,17]],"date-time":"2021-01-17T00:00:00Z","timestamp":1610841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection\/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, \u201cDLSenseNet\u201d, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that \u201cDLSenseNet\u201d provides better spectrum detection than other sensing models.<\/jats:p>","DOI":"10.3390\/sym13010147","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T02:36:05Z","timestamp":1611196565000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Deep Learning for Spectrum Sensing in Cognitive Radio"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5067-7621","authenticated-orcid":false,"given":"Surendra","family":"Solanki","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0965-1542","authenticated-orcid":false,"given":"Vasudev","family":"Dehalwar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8200-7403","authenticated-orcid":false,"given":"Jaytrilok","family":"Choudhary","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/ACCESS.2015.2461602","article-title":"A Survey of 5G Network: Architecture and Emerging Technologies","volume":"3","author":"Gupta","year":"2015","journal-title":"IEEE Access"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/JPROC.2012.2187132","article-title":"Spectrum policy for radio spectrum access","volume":"100","author":"Marcus","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/TVT.2011.2112676","article-title":"Enhanced spectrum sensing scheme in cognitive radio systems with MIMO antennae","volume":"60","author":"Lee","year":"2011","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.phycom.2010.12.003","article-title":"Cooperative spectrum sensing in cognitive radio networks: A survey","volume":"4","author":"Akyildiz","year":"2011","journal-title":"Phys. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mishra, S.M., Sahai, A., and Brodersen, R.W. (2006, January 11\u201315). Cooperative sensing among cognitive radios. Proceedings of the IEEE International Conference on Communications, Istanbul, Turkey.","DOI":"10.1109\/ICC.2006.254957"},{"key":"ref_9","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2017). Deep learning. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNNLS.2018.2850703","article-title":"Modulation Classification Based on Signal Constellation Diagrams and Deep Learning","volume":"30","author":"Peng","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, C., Chang, Q., and Li, X. (2019). A deep learning approach for mimo-noma downlink signal detection. Sensors, 19.","DOI":"10.3390\/s19112526"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vyas, M.R., Patel, D.K., and L\u00f3pez-Be\u0144itez, M. (2017, January 8\u201313). Artificial neural network based hybrid spectrum sensing scheme for cognitive radio. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292449"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, D., Sobabe, G.C., Zhang, C., Bai, X., Wang, Z., Liu, S., and Guo, B. (2017, January 14\u201316). Spectrum sensing for cognitive radio based on convolution neural network. Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China.","DOI":"10.1109\/CISP-BMEI.2017.8302117"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1109\/TVT.2019.2891291","article-title":"Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks","volume":"68","author":"Lee","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","unstructured":"Chandhok, S., Joshi, H., Subramanyam, A.V., and Darak, S.J. (2019). Novel Deep Learning Framework for Wideband Spectrum Characterization at Sub-Nyquist Rate. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"138","DOI":"10.23919\/JCC.2020.02.012","article-title":"Spectrum sensing based on deep learning classification for cognitive radios","volume":"17","author":"Zheng","year":"2020","journal-title":"China Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LWC.2019.2940579","article-title":"Robust Deep Sensing through Transfer Learning in Cognitive Radio","volume":"9","author":"Peng","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/LCOMM.2020.3002073","article-title":"Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach","volume":"24","author":"Xie","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7785","DOI":"10.1109\/TCOMM.2019.2940013","article-title":"Sensing OFDM Signal: A Deep Learning Approach","volume":"67","author":"Cheng","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/LWC.2019.2939314","article-title":"Deep Learning for Spectrum Sensing","volume":"8","author":"Gao","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/JSAC.2019.2933892","article-title":"Deep CM-CNN for Spectrum Sensing in Cognitive Radio","volume":"37","author":"Liu","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_22","unstructured":"O\u2019Shea, T.J., and West, N. (2016, January 12\u201316). Radio Machine Learning Dataset Generation with GNU Radio. Proceedings of the GNU Radio Conference, Boulder, CO, USA."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, X., Yang, D., and El Gamal, A. (November, January 29). Deep neural network architectures for modulation classification. Proceedings of the Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, (ACSSC 2017), Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2017.8335483"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-3-319-44188-7_16","article-title":"Convolutional radio modulation recognition networks","volume":"Volume 629","author":"Corgan","year":"2016","journal-title":"Engineering Applications of Neural Networks"},{"key":"ref_27","unstructured":"(2019). P802.22\/D6.0.0, May 2019\u2014IEEE Draft Standard for Information Technology\u2014Local and Metropolitan Area Networks\u2014Specific Requirements\u2014Part 22: Cognitive Radio Wireless Regional Area Networks (WRAN) Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the Bands that Allow Spectrum Sharing where the Communications Devices may Opportunistically Operate in the Spectrum of the Primary Service, IEEE."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dehalwar, V., Kalam, A., Kolhe, M.L., and Zayegh, A. (October, January 28). Compliance of IEEE 802.22 WRAN for field area network in smart grid. Proceedings of the 2016 IEEE International Conference on Power System Technology, (POWERCON 2016), Wollongong, Australia.","DOI":"10.1109\/POWERCON.2016.7754046"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:12:09Z","timestamp":1760159529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,17]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["sym13010147"],"URL":"https:\/\/doi.org\/10.3390\/sym13010147","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,17]]}}}