Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels

Paschalis C. Sofotasios, Eric Rebeiz, Li Zhang, Theodoros A. Tsiftsis, Danijela Cabric, Steven Freear

Research output: Contribution to journalArticle

116 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1031-1040
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume62
Issue number3
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Energy Detection
Spectrum Sensing
Cognitive radio
Fading Channels
Fading
Fading channels
Extremes
Detectors
Fading (radio)
Radio communication
Radio systems
Probability of Detection
Cognitive Radio
Detector
Energy efficiency
Communication systems
Sensing
Cognitive Radio Networks
User Experience
Energy Efficiency

Keywords

  • κ-μ fading
  • Collaborative spectrum sensing
  • Diversity
  • Energy detector
  • Fading channels
  • Spectrum sensing
  • Unknown signal detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Aerospace Engineering
  • Automotive Engineering
  • Computer Networks and Communications
  • Applied Mathematics

Cite this

Sofotasios, P. C., Rebeiz, E., Zhang, L., Tsiftsis, T. A., Cabric, D., & Freear, S. (2013). Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels. IEEE Transactions on Vehicular Technology, 62(3), 1031-1040. https://doi.org/10.1109/TVT.2012.2228680

Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels. / Sofotasios, Paschalis C.; Rebeiz, Eric; Zhang, Li; Tsiftsis, Theodoros A.; Cabric, Danijela; Freear, Steven.

In: IEEE Transactions on Vehicular Technology, Vol. 62, No. 3, 2013, p. 1031-1040.

Research output: Contribution to journalArticle

Sofotasios, PC, Rebeiz, E, Zhang, L, Tsiftsis, TA, Cabric, D & Freear, S 2013, 'Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels', IEEE Transactions on Vehicular Technology, vol. 62, no. 3, pp. 1031-1040. https://doi.org/10.1109/TVT.2012.2228680
Sofotasios, Paschalis C. ; Rebeiz, Eric ; Zhang, Li ; Tsiftsis, Theodoros A. ; Cabric, Danijela ; Freear, Steven. / Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels. In: IEEE Transactions on Vehicular Technology. 2013 ; Vol. 62, No. 3. pp. 1031-1040.
@article{e72f1676e04f45689d2808c687d181ce,
title = "Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels",
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.",
keywords = "κ-μ fading, Collaborative spectrum sensing, Diversity, Energy detector, Fading channels, Spectrum sensing, Unknown signal detection",
author = "Sofotasios, {Paschalis C.} and Eric Rebeiz and Li Zhang and Tsiftsis, {Theodoros A.} and Danijela Cabric and Steven Freear",
year = "2013",
doi = "10.1109/TVT.2012.2228680",
language = "English",
volume = "62",
pages = "1031--1040",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Energy detection based spectrum sensing over κ-μ and κ-μ extreme fading channels

AU - Sofotasios, Paschalis C.

AU - Rebeiz, Eric

AU - Zhang, Li

AU - Tsiftsis, Theodoros A.

AU - Cabric, Danijela

AU - Freear, Steven

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - κ-μ fading

KW - Collaborative spectrum sensing

KW - Diversity

KW - Energy detector

KW - Fading channels

KW - Spectrum sensing

KW - Unknown signal detection

UR - http://www.scopus.com/inward/record.url?scp=84885106191&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885106191&partnerID=8YFLogxK

U2 - 10.1109/TVT.2012.2228680

DO - 10.1109/TVT.2012.2228680

M3 - Article

VL - 62

SP - 1031

EP - 1040

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 3

ER -