Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept
Cognitive tactical wireless networks (TWNs) require spectrum awareness to avoid interference and jamming in the communication channel and assure quality-of-service in data transmission. Conventional supervised machine learning (ML) algorithm’s capability to provide spectrum awareness is c...
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2025-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/11068948/ |
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| author | Eli Garlick Nourhan Hesham MD. Zoheb Hassan Imtiaz Ahmed Anas Chaaban MD. Jahangir Hossain |
| author_facet | Eli Garlick Nourhan Hesham MD. Zoheb Hassan Imtiaz Ahmed Anas Chaaban MD. Jahangir Hossain |
| author_sort | Eli Garlick |
| collection | DOAJ |
| description | Cognitive tactical wireless networks (TWNs) require spectrum awareness to avoid interference and jamming in the communication channel and assure quality-of-service in data transmission. Conventional supervised machine learning (ML) algorithm’s capability to provide spectrum awareness is confronted by the requirement of labeled interference signals. Due to the vast nature of interference signals in the frequency bands used by cognitive TWNs, it is non-trivial to acquire manually labeled data sets of all interference signals. Detecting the presence of an unknown and remote interference source in a frequency band from the transmitter end is also challenging, especially when the received interference power remains at or below the noise floor. To address these issues, this paper proposes an automated interference detection framework, entitled <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> (Machine Learning Aided Resilient Spectrum Surveillance). <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is a fully unsupervised method, which first extracts the low-dimensional representative features from spectrograms by suppressing noise and background information and employing convolutional neural network (CNN) with novel loss function, and subsequently, distinguishes signals with and without interference by applying an isolation forest model on the extracted features. The uniqueness of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is its ability to detect hidden and unknown interference signals in multiple frequency bands without using any prior labels, thanks to its superior feature extraction capability. The capability of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is further extended to infer the level of interference by designing a multi-level interference classification framework. Using extensive simulations in GNURadio, the superiority of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> in detecting interference over existing ML methods is demonstrated. The effectiveness <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is also validated by extensive over-the-air (OTA) experiments using software-defined radios. |
| format | Article |
| id | doaj-art-515f86bd644345c8be2a05a21c9a8084 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-515f86bd644345c8be2a05a21c9a80842025-08-20T02:40:30ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01381483410.1109/TMLCN.2025.358584911068948Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-ConceptEli Garlick0Nourhan Hesham1https://orcid.org/0000-0003-2603-3460MD. Zoheb Hassan2https://orcid.org/0000-0003-0037-5382Imtiaz Ahmed3https://orcid.org/0000-0001-6465-532XAnas Chaaban4MD. Jahangir Hossain5https://orcid.org/0000-0002-3377-7831School of Engineering, The University of British Columbia, Vancouver, BC, CanadaSchool of Engineering, The University of British Columbia, Vancouver, BC, CanadaElectrical and Computer Engineering Department, Université Laval, Québec, QC, CanadaElectrical Engineering and Computer Science Department, Howard University, Washington, DC, USASchool of Engineering, The University of British Columbia, Vancouver, BC, CanadaSchool of Engineering, The University of British Columbia, Vancouver, BC, CanadaCognitive tactical wireless networks (TWNs) require spectrum awareness to avoid interference and jamming in the communication channel and assure quality-of-service in data transmission. Conventional supervised machine learning (ML) algorithm’s capability to provide spectrum awareness is confronted by the requirement of labeled interference signals. Due to the vast nature of interference signals in the frequency bands used by cognitive TWNs, it is non-trivial to acquire manually labeled data sets of all interference signals. Detecting the presence of an unknown and remote interference source in a frequency band from the transmitter end is also challenging, especially when the received interference power remains at or below the noise floor. To address these issues, this paper proposes an automated interference detection framework, entitled <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> (Machine Learning Aided Resilient Spectrum Surveillance). <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is a fully unsupervised method, which first extracts the low-dimensional representative features from spectrograms by suppressing noise and background information and employing convolutional neural network (CNN) with novel loss function, and subsequently, distinguishes signals with and without interference by applying an isolation forest model on the extracted features. The uniqueness of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is its ability to detect hidden and unknown interference signals in multiple frequency bands without using any prior labels, thanks to its superior feature extraction capability. The capability of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is further extended to infer the level of interference by designing a multi-level interference classification framework. Using extensive simulations in GNURadio, the superiority of <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> in detecting interference over existing ML methods is demonstrated. The effectiveness <inline-formula> <tex-math notation="LaTeX">$\textsf {MARSS}$ </tex-math></inline-formula> is also validated by extensive over-the-air (OTA) experiments using software-defined radios.https://ieeexplore.ieee.org/document/11068948/Convolutional neural networkinterference detection and classificationfeature learningGNURadio |
| spellingShingle | Eli Garlick Nourhan Hesham MD. Zoheb Hassan Imtiaz Ahmed Anas Chaaban MD. Jahangir Hossain Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept IEEE Transactions on Machine Learning in Communications and Networking Convolutional neural network interference detection and classification feature learning GNURadio |
| title | Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept |
| title_full | Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept |
| title_fullStr | Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept |
| title_full_unstemmed | Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept |
| title_short | Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept |
| title_sort | machine learning aided resilient spectrum surveillance for cognitive tactical wireless networks design and proof of concept |
| topic | Convolutional neural network interference detection and classification feature learning GNURadio |
| url | https://ieeexplore.ieee.org/document/11068948/ |
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