{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T22:00:08Z","timestamp":1783461608958,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U19b2015"],"award-info":[{"award-number":["U19b2015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problems of gradient vanishing and limited feature extraction capability of traditional CNN spectrum sensing methods in deep network structures and to effectively avoid network degradation issues under deep network structures, this paper proposes a collaborative spectrum sensing method based on Residual Dense Network and attention mechanisms. This method involves stacking and normalizing the time-domain information of the signal, constructing a two-dimensional matrix, and mapping it to a grayscale image. The grayscale images are divided into training and testing sets, and the training set is used to train the neural network to extract deep features. Finally, the test set is fed into the well-trained neural network for spectrum sensing. Experimental results show that, under low signal-to-noise ratios, the proposed method demonstrates superior spectral sensing performance compared to traditional collaborative spectrum sensing methods.<\/jats:p>","DOI":"10.3390\/s23187791","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T10:42:49Z","timestamp":1694428969000},"page":"7791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Spectrum Sensing Method Based on Residual Dense Network and Attention"],"prefix":"10.3390","volume":"23","author":[{"given":"Anyi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3984-7156","authenticated-orcid":false,"given":"Mingbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"ref_1","first-page":"16","article-title":"Spectrum occupancy measurement and analysis","volume":"7","author":"Tian","year":"2009","journal-title":"ZTE Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications","volume":"17","author":"Guizani","year":"2015","journal-title":"IEEE Commun. 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