Improved primary signal sensing at the frequency of 433 MHz using MAF-KF-NPD algorithms with the arduino controller in an experimental scenario
By: Adardour, Haroun Errachid
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Contributor(s): Kameche, Samir.
Publisher: New York Springer 2022Edition: Vol.103(3), June.Description: 859-873p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: The present paper is an experimental study that aims to investigate the performance in detecting in real time the primary signal at the frequency of 433 MHz for Cognitive Radio Networks (or CRNs); it also seeks to validate the implementation of the proposed algorithm for the purpose of detecting the Amplitude of a Primary Signal (or APS) buried under an Additive White Gaussian Noise (or AWGN) channel model. For this reason, a very efficient solution is suggested to use three algorithms, namely the Moving Average Filter (or MAF), Kalman Filter (or KF), and Neyman–Pearson Detector (or NPD) algorithms. The purpose of using MAF and KF is to reduce the random noise and identify the APS, respectively, at the frequency of 433 MHz. Afterward, the APS sensing is checked by the performance of NPD approach.. by the NPD approach. In addition, the controller of the proposed system is programmed by two programmable microcontrollers, i.e. the Arduinos NANO and UNO. In the end, the numerical results obtained are presented to validate our proposal through the evaluation of the sensing performances in terms of the Total Probability of Detection Error (or TPDE).
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1301 |
The present paper is an experimental study that aims to investigate the performance in detecting in real time the primary signal at the frequency of 433 MHz for Cognitive Radio Networks (or CRNs); it also seeks to validate the implementation of the proposed algorithm for the purpose of detecting the Amplitude of a Primary Signal (or APS) buried under an Additive White Gaussian Noise (or AWGN) channel model. For this reason, a very efficient solution is suggested to use three algorithms, namely the Moving Average Filter (or MAF), Kalman Filter (or KF), and Neyman–Pearson Detector (or NPD) algorithms. The purpose of using MAF and KF is to reduce the random noise and identify the APS, respectively, at the frequency of 433 MHz. Afterward, the APS sensing is checked by the performance of NPD approach.. by the NPD approach. In addition, the controller of the proposed system is programmed by two programmable microcontrollers, i.e. the Arduinos NANO and UNO. In the end, the numerical results obtained are presented to validate our proposal through the evaluation of the sensing performances in terms of the Total Probability of Detection Error (or TPDE).
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