DocumentCode :
2427879
Title :
Energy-based Bayesian spectrum sensing over κ-μ and κ-μ extreme fading channels
Author :
Gurugopinath, Sanjeev
Author_Institution :
Dept. of Electr. & Electron. Eng., C.M.R. Inst. of Technol., Bangalore, India
fYear :
2015
fDate :
Feb. 27 2015-March 1 2015
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, performance of energy-based spectrum sensing over the κ-μ and κ-μ extreme fading channels under a Bayesian framework is investigated. First, exact expressions for the probability of error (as a convex combination of the probabilities of false-alarm and signal detection) under both κ-μ and κ-μ extreme fading models are derived. Later, analysis and discussions on calculation of the optimal threshold (in the Bayesian sense) are presented. The analytic expressions are validated through numerical techniques and Monte Carlo simulations. The performance of the energy detector is observed to be severely effected by small variations in the fading parameters. The derived results are useful in evaluating the effect of fading on the detection performance for cognitive radio systems operating in various fading environments (which are special cases of the κ-μ distribution), and therefore, this work provides a comprehensive solution set to Bayesian energy detection for spectrum sensing.
Keywords :
Monte Carlo methods; cognitive radio; fading channels; radio spectrum management; signal detection; κ-μ channels; Monte Carlo simulations; cognitive radio systems; convex combination; energy detector; energy-based Bayesian spectrum sensing; extreme fading channels; probability of error; signal detection; Analytical models; Bayes methods; Numerical models; Rayleigh channels; Sensors; Signal to noise ratio; κ-μ extreme fading; κ-μ fading; Spectrum sensing; bayesian approach; energy detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications (NCC), 2015 Twenty First National Conference on
Conference_Location :
Mumbai
Type :
conf
DOI :
10.1109/NCC.2015.7084913
Filename :
7084913
Link To Document :
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