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Open AccessArticle

Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance

1
Nanfang College, Sun Yat-Sen Univeristy, Guangzhou 510900, China
2
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
3
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada
4
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2019, 8(2), 28; https://doi.org/10.3390/jsan8020028
Received: 27 February 2019 / Revised: 6 May 2019 / Accepted: 7 May 2019 / Published: 13 May 2019
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Abstract

In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments. View Full-Text
Keywords: energy detection; noise variance; spectrum sensing energy detection; noise variance; spectrum sensing
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Hu, X.-L.; Ho, P.-H.; Peng, L. Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance. J. Sens. Actuator Netw. 2019, 8, 28.

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