Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio
The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-07-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.9746/jcmsi.12.134 |
| _version_ | 1827794819376742400 |
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| author | Kenta Asakura Haruhisa Ichikawa Yuusuke Kawakita |
| author_facet | Kenta Asakura Haruhisa Ichikawa Yuusuke Kawakita |
| author_sort | Kenta Asakura |
| collection | DOAJ |
| description | The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods. |
| first_indexed | 2024-03-11T18:39:18Z |
| format | Article |
| id | doaj.art-ec08e4cfd16b4e1b842a30108a6cad1a |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:18Z |
| publishDate | 2019-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-ec08e4cfd16b4e1b842a30108a6cad1a2023-10-12T13:43:55ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702019-07-0112413414110.9746/jcmsi.12.13412103263Normalization of Radio Feature for Online Learning for Identification in Cognitive RadioKenta Asakura0Haruhisa Ichikawa1Yuusuke Kawakita2Graduate School of Informatics and Engineering, The University of Electro-CommunicationsGraduate School of Informatics and Engineering, The University of Electro-CommunicationsFaculty of Information Technology, Kanagawa Institute of TechnologyThe identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.http://dx.doi.org/10.9746/jcmsi.12.134cognitive radioradio access technologyspectrum sharingonline learningsupervised learning |
| spellingShingle | Kenta Asakura Haruhisa Ichikawa Yuusuke Kawakita Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio SICE Journal of Control, Measurement, and System Integration cognitive radio radio access technology spectrum sharing online learning supervised learning |
| title | Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio |
| title_full | Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio |
| title_fullStr | Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio |
| title_full_unstemmed | Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio |
| title_short | Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio |
| title_sort | normalization of radio feature for online learning for identification in cognitive radio |
| topic | cognitive radio radio access technology spectrum sharing online learning supervised learning |
| url | http://dx.doi.org/10.9746/jcmsi.12.134 |
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