Table of Contents
International Scholarly Research Notices
Volume 2014, Article ID 579125, 11 pages
Research Article

A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application

1Department of Telecommunication, Federal University of Technology, Minna, Niger State, Nigeria
2Digital Bridge Institute, Abuja, Nigeria
3Department of Mechatronic Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia

Received 31 March 2014; Revised 9 June 2014; Accepted 1 July 2014; Published 29 October 2014

Academic Editor: George Kyriacou

Copyright © 2014 A. J. Onumanyi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.