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 |
| Published: |
Hindawi
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/30199/ http://irep.iium.edu.my/30199/ http://irep.iium.edu.my/30199/1/AdeizaPaper.pdf |
| Summary: | 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. |
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