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Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks

Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks

Bommidi Sridhar, Srinivasulu Tadisetty
Copyright: © 2019 |Volume: 15 |Issue: 2 |Pages: 14
ISSN: 1548-0631|EISSN: 1548-064X|EISBN13: 9781522564270|DOI: 10.4018/IJBDCN.2019070101
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MLA

Sridhar, Bommidi, and Srinivasulu Tadisetty. "Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks." IJBDCN vol.15, no.2 2019: pp.1-14. http://doi.org/10.4018/IJBDCN.2019070101

APA

Sridhar, B. & Tadisetty, S. (2019). Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks. International Journal of Business Data Communications and Networking (IJBDCN), 15(2), 1-14. http://doi.org/10.4018/IJBDCN.2019070101

Chicago

Sridhar, Bommidi, and Srinivasulu Tadisetty. "Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks," International Journal of Business Data Communications and Networking (IJBDCN) 15, no.2: 1-14. http://doi.org/10.4018/IJBDCN.2019070101

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Abstract

Cognitive radio-based systems rely on spectrum sensing techniques to detect whitespaces to exploit. Cognitive radio (CR) is an attractive approach to face the shortage in the electromagnetic spectrum resources and improve the overall spectrum utilization. However, Energy detectors perform far from optimally by affecting the performance of the underlying system. In this article, two spectrum-sensing techniques are considered for CR networks; one based on energy detection and the other based on multi-taper spectral estimation (MSE). This article proposes a new method to optimize the overall performance in cooperative spectrum sensing in cognitive radio (CR) networks. An efficient recursive least square (ERLS)-based approach is proposed in order to optimize the overall performance to monitor the primary user active or inactive stage with use of secondary user while receiving data. An energy detector (ED) and multi-taper (MTM) spectrum sensing techniques are examined as local spectrum sensing techniques. Finally, a genetic algorithm is compared with the proposed system to show the system effectiveness.

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