NDLI: Energy Detection Based Spectrum Sensing Over <formula formulatype=inline><tex Notation=TeX>$kappa{-}mu$</tex></formula> and <formula formulatype=inline><tex Notation=TeX>$kappa{-}mu$</tex></formula> Extreme Fading Channels

Content Provider	IEEE Xplore Digital Library
Author	Sofotasios, P.C. Rebeiz, E. Li Zhang Tsiftsis, T.A. Cabric, D. Freear, S.
Copyright Year	1967
Abstract	Energy detection (ED) is a simple and popular method of spectrum sensing in cognitive radio systems. It is also widely known that the performance of sensing techniques is largely affected when users experience fading effects. This paper investigates the performance of an energy detector over generalized κ-μ and κ- μ extreme fading channels, which have been shown to provide remarkably accurate fading characterization. Novel analytic expressions are firstly derived for the corresponding average probability of detection for the case of single-user detection. These results are subsequently extended to the case of square-law selection (SLS) diversity and for collaborative detection scenarios. As expected, the performance of the detector is highly dependent upon the severity of fading since even small variations of the fading conditions affect significantly the value of the average probability of detection. Furthermore, the performance of the detector improves substantially as the number of branches or collaborating users increase in both severe and moderate fading conditions, whereas it is shown that the κ- μ extreme model is capable of accounting for fading variations even at low signal-to-noise values. The offered results are particularly useful in assessing the effect of fading in ED-based cognitive radio communication systems; therefore, they can be used in quantifying the associated tradeoffs between sensing performance and energy efficiency in cognitive radio networks.
Sponsorship	IEEE Vehicular Technology Society
Starting Page	1031
Ending Page	1040
Page Count	10
File Size	1257001
File Format	PDF
ISSN	00189545
Volume Number	62
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rayleigh channels Detectors Signal to noise ratio Cognitive radio Collaboration unknown signal detection Collaborative spectrum sensing diversity energy detector fading channels $\kappa{-}\mu$ fading spectrum sensing
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Automotive Engineering Computer Networks and Communications Electrical and Electronic Engineering Aerospace Engineering

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