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-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

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-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

Comparative sensitivity analysis of energy detection techniques for cognitive radio application

With sensitivity being an important factor in spectrum sensing based Cognitive Radio (CR) application; it remains unclear which out of the many existing Energy Detector (ED) techniques provides the best sensitivity performance for CR application. Consequently, this paper reports a study of some known parametric and non-parametric Energy Detector (ED) schemes for Cognitive Radio (CR) application towards providing relevant information. The models studied are the Simple Periodogram (SP), Welch Periodogram (WP), Multi-Taper (MT), Yule-Walker (YW), Burg (BG), and Covariance (CV). Each technique was developed using known mathematical models and appropriate signals were simulated for comparative analysis. However, owing to the limitation of the typical Receiver Operating Characteristic (ROC) curve to infer comparative information, our study proposes a decomposition of the ROCs of each technique into respective detection and false alarm probability curves in comparison with estimated threshold levels to enhance comparative inference. From our findings, it was observed that a detection performance gain of about 50% can be achieved when using parametric techniques over nonparametric methods especially in low SNR conditions. Furthermore, a possible 15dB increase in sensitivity performance can be achieved in narrow than wideband sensing for all techniques. Finally, an increase in sensing time might not necessarily improve detection performance in low SNR conditions provided a low false alarm performance must be maintained

Signal Evaluation of a Novel Dual-energy Multimedia Imaging Sensor

In this study, experimental results on the signal quality of a multimedia imaging detector, operating on gaseous solid state ionization principles, with specific emphasis on single X-ray exposure dual-energy radiography, are presented. The results of this study indicate that the multimedia detector technology exhibits excellent signal characteristics suitable for a large number of imaging applications