A real valued neural network based autoregressive energy detector for cognitive radio application

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dc.contributor.authorOnumanyi, A. J.
dc.contributor.authorOnwuka, E. N.
dc.contributor.authorAibinu, A. M.
dc.contributor.authorUgweje, O. C.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-08-14T15:07:15Z
dc.date.available2019-08-14T15:07:15Z
dc.date.issued2014
dc.description.abstractA 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.en_US
dc.identifier.citationOnumanyi, A. J., Onwuka, E. N., Aibinu, A. M., Ugweje, O. C., & Salami, M. J. E. (2014). A real valued neural network based autoregressive energy detector for cognitive radio application. International scholarly research notices, 2014.en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2014/579125
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/492
dc.language.isoenen_US
dc.publisherHindawien_US
dc.subjectNeural networken_US
dc.subjectAutoregressive energy detectoren_US
dc.subjectCognitive radio applicationen_US
dc.titleA real valued neural network based autoregressive energy detector for cognitive radio applicationen_US
dc.typeArticleen_US
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