A real valued neural network based autoregressive energy detector for cognitive radio application
A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/30199/1/AdeizaPaper.pdf |
| _version_ | 1796878022078365696 |
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| author | Onumanyi, A. J. Onwuka, E. N. Aibinu, A. M. Ugweje, O. C. Salami, Momoh Jimoh Emiyoka |
| author_facet | Onumanyi, A. J. Onwuka, E. N. Aibinu, A. M. Ugweje, O. C. Salami, Momoh Jimoh Emiyoka |
| author_sort | Onumanyi, A. J. |
| collection | IIUM |
| description | A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application.
This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system.
By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function
was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high
detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model
order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP),
multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better
performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided
here support the effectiveness of the proposed RVNN based ED for CR application. |
| first_indexed | 2024-03-05T23:15:39Z |
| format | Article |
| id | oai:generic.eprints.org:30199 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T23:15:39Z |
| publishDate | 2014 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | oai:generic.eprints.org:301992018-06-11T03:42:22Z http://irep.iium.edu.my/30199/ A real valued neural network based autoregressive energy detector for cognitive radio application Onumanyi, A. J. Onwuka, E. N. Aibinu, A. M. Ugweje, O. C. Salami, Momoh Jimoh Emiyoka TK5101 Telecommunication. Including telegraphy, radio, radar, television A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application. Hindawi 2014-10-29 Article PeerReviewed application/pdf en http://irep.iium.edu.my/30199/1/AdeizaPaper.pdf Onumanyi, A. J. and Onwuka, E. N. and Aibinu, A. M. and Ugweje, O. C. and Salami, Momoh Jimoh Emiyoka (2014) A real valued neural network based autoregressive energy detector for cognitive radio application. International Scholarly Research Notices, 2014. pp. 1-11. ISSN 2356-7872 http://www.hindawi.com/ |
| spellingShingle | TK5101 Telecommunication. Including telegraphy, radio, radar, television Onumanyi, A. J. Onwuka, E. N. Aibinu, A. M. Ugweje, O. C. Salami, Momoh Jimoh Emiyoka A real valued neural network based autoregressive energy detector for cognitive radio application |
| title | A real valued neural network based autoregressive energy
detector for cognitive radio application |
| title_full | A real valued neural network based autoregressive energy
detector for cognitive radio application |
| title_fullStr | A real valued neural network based autoregressive energy
detector for cognitive radio application |
| title_full_unstemmed | A real valued neural network based autoregressive energy
detector for cognitive radio application |
| title_short | A real valued neural network based autoregressive energy
detector for cognitive radio application |
| title_sort | real valued neural network based autoregressive energy detector for cognitive radio application |
| topic | TK5101 Telecommunication. Including telegraphy, radio, radar, television |
| url | http://irep.iium.edu.my/30199/1/AdeizaPaper.pdf |
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