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

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

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Abstract

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.

Item Type: Article (other)
Additional Information: 2470/30199
Uncontrolled Keywords: real valued neural network (RVNN), energy detector (ED, cognitive radio application
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Prof Momoh-Jimoh Salami
Date Deposited: 02 Dec 2014 08:58
Last Modified: 11 Jun 2018 11:42
URI: http://irep.iium.edu.my/id/eprint/30199

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