DocumentCode
1890038
Title
Further results on energy detection of random signals in Gaussian noise
Author
Olabiyi, O. ; Annamalai, A.
Author_Institution
Dept. of Electr. & Comput. Eng., Prairie View A&M Univ., Prairie View, TX, USA
fYear
2013
fDate
2-6 Dec. 2013
Firstpage
14
Lastpage
19
Abstract
This article shows that the non-coherent detector for random signals which maximizes the generalized likelihood function is the same as the detector that maximizes the probability of correct detection at any specified false alarm probability by deriving the exact statistics for |Y|p (where Y is a Gaussian random variable and p is a positive real number) for the following two cases: (i) sample size L = 1 but for arbitrary real p > 0; (ii) arbitrary L but for p = 1, 2 or 4. This observation is in stark contrast to all earlier studies on the “improved” energy detector (that replaces the squaring operation of the signal amplitude in the classical energy detector with an arbitrary positive power p operation). Our analytical results are in excellent agreement with those obtained via Monte-Carlo simulations, and also highlight the inaccuracies with the Gamma density approximation for |Y| p employed by Chen [2]. Although different choices of p and L will yield distinct receiver operating characteristics (ROC) curves, the optimum p remains 2 (i.e., no gain over the classical energy detector) regardless of the values of the signal-to-noise ratio and/or the sample size L.
Keywords
Gaussian noise; Monte Carlo methods; maximum likelihood detection; probability; radio spectrum management; sensitivity analysis; Gaussian noise; Gaussian random variable; Monte-Carlo simulations; ROC curves; arbitrary positive power; classical energy detector; cooperative spectrum sensing; detection probability; energy detection; false alarm probability; gamma density approximation; generalized likelihood function; improved energy detector; noncoherent random signal detector; random signal detection; receiver operating characteristics curves; signal amplitude; Approximation methods; Detectors; Gaussian noise; Optimization; Probability; Signal to noise ratio; Energy detector; detection probability; false alarm probability; spectrum sensing;
fLanguage
English
Publisher
ieee
Conference_Titel
Connected Vehicles and Expo (ICCVE), 2013 International Conference on
Conference_Location
Las Vegas, NV
Type
conf
DOI
10.1109/ICCVE.2013.6799762
Filename
6799762
Link To Document