{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:06:40Z","timestamp":1771614400585,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National IT Industry Promotion Agency","award":["D0747-23-1001"],"award-info":[{"award-number":["D0747-23-1001"]}]},{"name":"National IT Industry Promotion Agency","award":["2022-0-00454"],"award-info":[{"award-number":["2022-0-00454"]}]},{"name":"Institute of Information and Communications Technology Panning and Evaluation (IITP)","award":["D0747-23-1001"],"award-info":[{"award-number":["D0747-23-1001"]}]},{"name":"Institute of Information and Communications Technology Panning and Evaluation (IITP)","award":["2022-0-00454"],"award-info":[{"award-number":["2022-0-00454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device\u2019s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.<\/jats:p>","DOI":"10.3390\/s23249806","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T12:00:37Z","timestamp":1702468837000},"page":"9806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2436-3784","authenticated-orcid":false,"given":"Hanjin","family":"Kim","sequence":"first","affiliation":[{"name":"Future Convergence Engineering Major, Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9772-021X","authenticated-orcid":false,"given":"Young-Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3426-3792","authenticated-orcid":false,"given":"Won-Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"Future Convergence Engineering Major, Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan 31253, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/MCOMSTD.001.2000054","article-title":"Spectrum sharing for 5G\/6G URLLC: Research frontiers and standards","volume":"5","author":"Yang","year":"2021","journal-title":"IEEE Commun. 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