Title
Impact of noise estimation on energy detection and eigenvalue based spectrum sensing algorithms
Abstract
In this paper, semi-blind class of spectrum sensing algorithms, Energy Detection (ED) and Roy's Largest Root Test (RLRT), are considered under a typical flat fading channel scenario. The knowledge of the noise variance is imperative for the optimum performance of ED and RLRT. Unfortunately, the variation and unpredictability of noise variance is unavoidable. An idea of auxiliary noise variance estimation is introduced in order to cope with the absence of prior knowledge of the noise variance, thus a hybrid approach of signal detection is set forth for each considered method. The detection performance of the methods are derived and expressed by closed form analytical expressions. The impact of noise estimation accuracy on the the performance of ED and RLRT is compared in terms of Receiver Operating Characteristic (ROC) curves and performance curves (Probability of Detection/Miss-detection as a function of SNR by fixing the false alarm probability). It is concluded that optimum performance of ED and RLRT can be achieved even with the use of estimated noise variance by using a large number of slots for variance estimation. Finally, it is also found out that the impairment due to noise uncertainty is reduced on RLRT w. r. t. ED.
Year
DOI
Venue
2014
10.1109/ICC.2014.6883512
Communications
Keywords
Field
DocType
channel estimation,eigenvalues and eigenfunctions,fading channels,probability,radio spectrum management,signal detection,ED,RLRT,ROC curves,Roy largest root test,auxiliary noise variance estimation,closed form analytical expressions,eigenvalue based spectrum sensing algorithms,energy detection,flat fading channel,noise uncertainty,performance curves,probability of detection-miss-detection,receiver operating characteristic,signal detection
Receiver operating characteristic,False alarm,Expression (mathematics),Detection theory,Fading,Root test,Algorithm,Statistics,Statistical power,Eigenvalues and eigenvectors,Mathematics
Conference
ISSN
Citations 
PageRank 
1550-3607
4
0.40
References 
Authors
7
4
Name
Order
Citations
PageRank
Pawan Dhakal140.74
Daniel Rivello240.40
Federico Penna340.40
Roberto Garello420831.47