{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:31:07Z","timestamp":1760232667309,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["61901477","19JC-QNJC00800","2021KJ062","3122022068"],"award-info":[{"award-number":["61901477","19JC-QNJC00800","2021KJ062","3122022068"]}]},{"name":"Natural Science Foundation of Tianjin City under Grant","award":["61901477","19JC-QNJC00800","2021KJ062","3122022068"],"award-info":[{"award-number":["61901477","19JC-QNJC00800","2021KJ062","3122022068"]}]},{"name":"Scientific Research Program of Tianjin Education Commission under Grant","award":["61901477","19JC-QNJC00800","2021KJ062","3122022068"],"award-info":[{"award-number":["61901477","19JC-QNJC00800","2021KJ062","3122022068"]}]},{"name":"Fundamental Research Funds for the Central Universities under Grant","award":["61901477","19JC-QNJC00800","2021KJ062","3122022068"],"award-info":[{"award-number":["61901477","19JC-QNJC00800","2021KJ062","3122022068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose a spectrum sensing algorithm based on the Jones vector covariance matrix (JCM) and AlexNet model, i.e., the JCM-AlexNet algorithm, by taking advantage of the different state characteristics of the signal and noise in the polarization domain. We use the AlexNet model, which is good at extracting matrix features, as the classification model and use the Jones vector, which characterizes the polarization state, to calculate its covariance matrix and convert it into an image and then use it as the input to the AlexNet model. Then, we calculate the likelihood ratio test statistic (AlexNet-LRT) based on the output of the model to achieve the classification of the signal and noise. The simulation analysis shows that the JCM-AlexNet algorithm performs better than the conventional polarization detection (PSD) algorithm and the other three (LeNet5, long short-term memory (LSTM), multilayer perceptron (MLP)) excellent deep-learning-based spectrum sensing algorithms for different signal-to-noise ratios and different false alarm probabilities.<\/jats:p>","DOI":"10.3390\/s22228946","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:39:59Z","timestamp":1669005599000},"page":"8946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Polarization Domain Spectrum Sensing Algorithm Based on AlexNet"],"prefix":"10.3390","volume":"22","author":[{"given":"Shiyu","family":"Ren","sequence":"first","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8579-0931","authenticated-orcid":false,"given":"Wantong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxia","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1016\/j.jnca.2012.06.006","article-title":"Cognitive radio network security: A survey","volume":"35","author":"Sazia","year":"2012","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sharma, S.K., Chatzinotas, S., and Ottersten, B. (2012, January 18\u201320). Exploiting polarization for spectrum sensing in cognitive SatComs. Proceedings of the 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Stockholm, Sweden.","DOI":"10.4108\/icst.crowncom.2012.248473"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s11277-020-07537-2","article-title":"Polarized Antenna Aided Spectrum Sensing Based on Stochastic Resonance","volume":"114","author":"Lu","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_4","first-page":"1","article-title":"Study of spectrum sensing exploiting polarization: From optimal LRT to practical detectors","volume":"114","author":"Cali","year":"2016","journal-title":"Digit. Signal Process."},{"key":"ref_5","unstructured":"Deng, Y. (2012). Research of Spectrum Sensing Algirthm in Cognitive Radio Based on Polarization Information. [Master\u2019s Thesis, Beijing University of Posts and Telecommunications]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e4352","DOI":"10.1002\/ett.4352","article-title":"Machine learning for cooperative spectrum sensing and sharing: A survey","volume":"33","author":"Janu","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_7","first-page":"36","article-title":"Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods","volume":"3","author":"Khamaysa","year":"2020","journal-title":"J. Telecommun. Inf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1049\/iet-com.2019.0941","article-title":"Machine Learning-based Models for Spectrum Sensing in Cooperative Radio Networks","volume":"14","author":"Tavares","year":"2020","journal-title":"IET Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: A probabilistic model for information storage and organization in the brain","volume":"6","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"97437","DOI":"10.1109\/ACCESS.2020.2995633","article-title":"Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics","volume":"8","author":"Soni","year":"2020","journal-title":"IEEE Access."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10514","DOI":"10.1109\/TVT.2021.3109236","article-title":"Spectrum Sensing and Signal Identification With Deep Learning Based on Spectral Correlation Function","volume":"70","author":"Tekbiyik","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1587\/transcom.2017EBP3442","article-title":"A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph","volume":"E101.B","author":"Mengbo","year":"2018","journal-title":"IEICE Trans. Commun."},{"key":"ref_16","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":"Jiandong","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_17","first-page":"1157","article-title":"An Introduction of Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res. Spec. Issue Var. Feature Sel."},{"key":"ref_18","unstructured":"Fangfang, L., Chunyan, F., and Caili, G. (2009, January 24\u201326). Polarization Spectrum Sensing Scheme for Cognitive Radios. Proceedings of the 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, China."},{"key":"ref_19","unstructured":"Huanyao, D., and Xuesong, W. (2018). Connotation and Representation of Spatial Polarization Characteristic, Springer."},{"key":"ref_20","unstructured":"Di, B., and Baldassare (2018). Classical Theory of Electromagnetism, WORLD SCIENTIFIC, Boston College. [3rd ed.]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8946\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:28Z","timestamp":1760145688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,18]]},"references-count":20,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228946"],"URL":"https:\/\/doi.org\/10.3390\/s22228946","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,11,18]]}}}