Spectrum Sensing Using Energy Detectors with Performance Computation Capabilities

Luca Rugini; Paolo Banelli; G. Leus

doi:10.1109/eusipco.2016.7760520

Spectrum Sensing Using Energy Detectors with Performance Computation Capabilities

Luca Rugini, Paolo Banelli, G. Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

38 Downloads (Pure)

Abstract

We focus on the performance of the energy detector for cognitive radio applications. Our aim is to incorporate, into the energy detector, low-complexity algorithms that compute the performance of the detector itself. The main parameters of interest are the probability of detection and the required number of samples. Since the exact performance analysis involves complicated functions of two variables, such as the regularized lower incomplete Gamma function, we introduce new low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. The numerical comparison of the proposed approximations with the exact analysis highlights the good accuracy of the low-complexity computation approach.

Original language	English
Title of host publication	2016 24th European Signal Processing Conference, EUSIPCO 2016
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1608-1612
Number of pages	5
ISBN (Electronic)	978-0-9928-6265-7
ISBN (Print)	978-1-5090-1891-8
DOIs	https://doi.org/10.1109/eusipco.2016.7760520
Publication status	Published - 1 Dec 2016
Event	EUSIPCO 2016: 24th European Signal Processing Conference - Budapest, Hungary Duration: 29 Aug 2016 → 2 Sept 2016 Conference number: 24 http://www.eusipco2016.org/

Conference

Conference	EUSIPCO 2016
Abbreviated title	EUSIPCO
Country/Territory	Hungary
City	Budapest
Period	29/08/16 → 2/09/16
Internet address	http://www.eusipco2016.org/

Keywords

Random variables
Gaussian approximation
Signal to noise ratio
Detectors
Performance evaluation
Europe

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10.1109/eusipco.2016.7760520

leus16eusipco2Accepted author manuscript, 263 KB

Cite this

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title = "Spectrum Sensing Using Energy Detectors with Performance Computation Capabilities",

abstract = "We focus on the performance of the energy detector for cognitive radio applications. Our aim is to incorporate, into the energy detector, low-complexity algorithms that compute the performance of the detector itself. The main parameters of interest are the probability of detection and the required number of samples. Since the exact performance analysis involves complicated functions of two variables, such as the regularized lower incomplete Gamma function, we introduce new low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. The numerical comparison of the proposed approximations with the exact analysis highlights the good accuracy of the low-complexity computation approach.",

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AU - Rugini, Luca

AU - Banelli, Paolo

AU - Leus, G.

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N2 - We focus on the performance of the energy detector for cognitive radio applications. Our aim is to incorporate, into the energy detector, low-complexity algorithms that compute the performance of the detector itself. The main parameters of interest are the probability of detection and the required number of samples. Since the exact performance analysis involves complicated functions of two variables, such as the regularized lower incomplete Gamma function, we introduce new low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. The numerical comparison of the proposed approximations with the exact analysis highlights the good accuracy of the low-complexity computation approach.

AB - We focus on the performance of the energy detector for cognitive radio applications. Our aim is to incorporate, into the energy detector, low-complexity algorithms that compute the performance of the detector itself. The main parameters of interest are the probability of detection and the required number of samples. Since the exact performance analysis involves complicated functions of two variables, such as the regularized lower incomplete Gamma function, we introduce new low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. The numerical comparison of the proposed approximations with the exact analysis highlights the good accuracy of the low-complexity computation approach.

KW - Random variables

KW - Gaussian approximation

KW - Signal to noise ratio

KW - Detectors

KW - Performance evaluation

KW - Europe

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M3 - Conference contribution

SN - 978-1-5090-1891-8

SP - 1608

EP - 1612

BT - 2016 24th European Signal Processing Conference, EUSIPCO 2016

PB - IEEE

CY - Piscataway, NJ

T2 - EUSIPCO 2016

Y2 - 29 August 2016 through 2 September 2016

ER -

Spectrum Sensing Using Energy Detectors with Performance Computation Capabilities

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