{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T23:48:05Z","timestamp":1768607285489,"version":"3.49.0"},"reference-count":29,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Communications"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Cognitive Spectrum Sensing (CSS) stands as a foundational component in 5G and emerging 6G wireless communication systems, enabling intelligent identification and dynamic utilisation of underutilised spectrum bands. However, the implementation of CSS in dense and heterogeneous 5G\/6G environments presents significant challenges, including high spectral dynamics, multi\u2010protocol interference, and the requirement for real\u2010time decision\u2010making across diverse frequency bands. Existing methods such as deep belief networks, CNN\u2010PSO hybrids, and DQN\u2010based models suffer from limited adaptability, insufficient spatial\u2010temporal learning, and poor generalisation in real\u2010world RF environments. The proposed model includes a dual\u2010stream deep learning architecture which has a 1D convolutional neural network (CNN)\u2010based spectral encoder and a graph convolutional network (GCN)\u2010based spatial encoder for extracting the frequency\u2010domain and node\u2010topology features. Experimental analysis of proposed model is performed using the Real\u2010World Wireless Communication Dataset containing Wi\u2010Fi, LTE, and 5G RF signals. The dataset is pre\u2010processed using Fast Fourier Transformation (FFT) transformation and labelled through a signal\u2010power\u2010based thresholding mechanism. Results indicate the Spectral\u2010Spatial Dual Encoder with Bio\u2010Inspired Swarm Adaptation (SSDE\u2010BSA) achieves an accuracy of 96.1%, an F1\u2010score of 96.1%, and a spectral efficiency of 91. These results confirm the model's superiority in adapting to real\u2010world spectrum dynamics, offering a robust and scalable solution for cognitive spectrum sensing in next\u2010generation wireless networks.<\/jats:p>","DOI":"10.1049\/cmu2.70133","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:40:26Z","timestamp":1768567226000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Spectral\u2010Spatial Dual Encoder With Bio\u2010Inspired Swarm Adaptation for Cognitive Spectrum Sensing Using Real\u2010World RF Signal Data"],"prefix":"10.1049","volume":"20","author":[{"given":"P.","family":"Ramakrishnan","sequence":"first","affiliation":[{"name":"Electronics and Communication Engineering M. Kumarasamy College of Engineering  Karur Tamil Nadu India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1132-4794","authenticated-orcid":false,"given":"C.","family":"Kumar","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Karpagam College of Engineering, Coimbatore  Tami Nadu India"}]},{"given":"R. Saravana","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering Vellore Institute of Technology Chennai Campus  Chennai Tamil Nadu India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1291-2624","authenticated-orcid":false,"given":"Sourav","family":"Barua","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Green University of Bangladesh  Narayanganj, Dhaka Bangladesh"}]}],"member":"265","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11235\u2010023\u201001079\u20101"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11235\u2010025\u201001261\u20107"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phycom.2022.101673"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.52783\/jes.1433"},{"issue":"110","key":"e_1_2_11_6_1","first-page":"1","article-title":"Exploring and Analyzing the Role of Hybrid Spectrum Sensing Methods in 6G\u2010based Smart Health Care Applications","volume":"13","author":"Kumar A.","year":"2024","journal-title":"F1000Research"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21072408"},{"issue":"2","key":"e_1_2_11_8_1","first-page":"1","article-title":"Spectrum Sensing in Cognitive Radio Networks and Metacognition for Dynamic Spectrum Sharing Between Radar and Communication System: A Review","volume":"52","author":"Agrawal S. 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