{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:04:39Z","timestamp":1778537079721,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901477"],"award-info":[{"award-number":["61901477"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["19JCQNJC00800"],"award-info":[{"award-number":["19JCQNJC00800"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Program of Tianjin Education Commission","award":["2021KJ062"],"award-info":[{"award-number":["2021KJ062"]}]},{"name":"Scientific Research Program of Tianjin Education Commission","award":["2020KJ011"],"award-info":[{"award-number":["2020KJ011"]}]},{"name":"Start-up Research Foundation of Civil Aviation University of China","award":["2017QD06X"],"award-info":[{"award-number":["2017QD06X"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.<\/jats:p>","DOI":"10.3390\/s22062286","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T03:36:23Z","timestamp":1647401783000},"page":"2286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["CM-LSTM Based Spectrum Sensing"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8579-0931","authenticated-orcid":false,"given":"Wantong","family":"Chen","sequence":"first","affiliation":[{"name":"Civil Aviation Flight Wide Area Surveillance and Safety Control Technology Key Laboratory, 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\/0000-0002-3423-4134","authenticated-orcid":false,"given":"Shiyu","family":"Ren","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight Wide Area Surveillance and Safety Control Technology Key Laboratory, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"ref_1","first-page":"e4352","article-title":"Machine learning for cooperative spectrum sensing and sharing: A survey","volume":"8","author":"Janu","year":"2021","journal-title":"Trans. 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