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A modified three-event energy detection scheme using decision threshold optimization for sensing performance improvement in a cognitive radio system

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Sudipta Mallick
Sudipta Mallick
Sudipta Mallick
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Susmita Das
Susmita Das
Susmita Das
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Arun Kumar Ray
Arun Kumar Ray
Arun Kumar Ray
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract and Figures

Dynamic spectrum access has been promoted as a key technology in cognitive radio to achieve better spectrum utilization. It allows unauthorized secondary users to utilize the authorized primary user’s spectrum opportunistically, when primary user is absent. Therefore, it is an important task for secondary users to observe primary user activity in the channel. Implementation of such spectrum access scheme, cognitive radio requires a fast and reliable spectrum sensing technique to monitor primary user activity. Among all the available spectrum sensing schemes, Energy detection is most widely used because of its low complexity. However, the conventional energy detection method produces poor performance in a lower signal regime, resulting in longer sensing duration and low detection probability. To overcome these challenges, we have proposed an adaptive decision threshold approach instead of a fixed decision threshold in a modified Three Event Energy Detection framework. Additionally, a new objective function is formulated prioritizing the PU over SU using a weight factor along with spectrum utilization factor which achieves a better trade-off between the miss detection and false alarm probability. Simulation results illustrate that the proposed approach has improved efficacy in decision error probability and detection performance compared to the existing methods.
Illustration of a cognitive radio network with a frame structure
Illustration of a cognitive radio network with a frame structure
… 
Illustration of 3EED system model
Illustration of 3EED system model
… 
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Optimum decision threshold (λopt)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\lambda _{opt})$$\end{document} vs SNR for different weight factors (W1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W_{1}$$\end{document}) and spectrum utilization factors (P(H1))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P(H_{1}))$$\end{document}
… 
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Optimum decision threshold (λopt)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\lambda _{opt})$$\end{document} vs spectrum utilization ratio (P(H1))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P(H_{1}))$$\end{document} at SNR=-20dB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {SNR} = -20\,{\text {dB}}$$\end{document}
… 
Decision error probability (Pe)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P_{e})$$\end{document} vs SNR for P(H1)=0.1,0.5,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(H_{1}) = 0.1, 0.5, $$\end{document} and 0.9
+5
Decision error probability (Pe)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P_{e})$$\end{document} vs SNR for P(H1)=0.1,0.5,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(H_{1}) = 0.1, 0.5, $$\end{document} and 0.9
… 
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ORIGINAL PAPER
A modified three-event energy detection scheme using decision
threshold optimization for sensing performance improvement
in a cognitive radio system
Sudipta Mallick
1
•Susmita Das
1
•Arun Kumar Ray
2
Accepted: 24 March 2023 / Published online: 28 April 2023
The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
Dynamic spectrum access has been promoted as a key technology in cognitive radio to achieve better spectrum utilization.
It allows unauthorized secondary users to utilize the authorized primary user’s spectrum opportunistically, when primary
user is absent. Therefore, it is an important task for secondary users to observe primary user activity in the channel.
Implementation of such spectrum access scheme, cognitive radio requires a fast and reliable spectrum sensing technique to
monitor primary user activity. Among all the available spectrum sensing schemes, Energy detection is most widely used
because of its low complexity. However, the conventional energy detection method produces poor performance in a lower
signal regime, resulting in longer sensing duration and low detection probability. To overcome these challenges, we have
proposed an adaptive decision threshold approach instead of a fixed decision threshold in a modified Three Event Energy
Detection framework. Additionally, a new objective function is formulated prioritizing the PU over SU using a weight
factor along with spectrum utilization factor which achieves a better trade-off between the miss detection and false alarm
probability. Simulation results illustrate that the proposed approach has improved efficacy in decision error probability and
detection performance compared to the existing methods.
Keywords Cognitive radio networks Spectrum sensing Three-event energy detection Adaptive decision threshold
1 Introduction
Due to the popularization of intelligent wireless applica-
tions in all sectors, the increasing demand for spectrum
resources has dramatically increased. In this situation, the
traditional spectrum allocation policy can not accommo-
date the growing demand [1,2], resulting in poor utiliza-
tion of spectrum resources. However, Federal
Communication Commission (FCC) reported that the
maximum portion of the spectrum band is underutilized by
primary users (PU). In order to solve the spectrum
underutilization problem, cognitive radio (CR) has been
proposed as a meaningful alternative by enabling dynamic
spectrum access (DSA). It allows secondary users (SU) to
utilize the PUs frequency band opportunistically when the
PU is absent in the channel. As long as a PU appears in the
channel, SU must immediately free up the frequency bands
to avoid interference. Therefore, an efficient spectrum
sensing technique is required to monitor PU activity con-
tinuously [3].
Among all the spectrum sensing techniques, the energy
detection (ED) method is commonly used because of its
simplicity, low complexity, and sensing PU without having
any prior information [4]. A thorough and detailed review
of commonly known spectrum sensing techniques such as
Cyclostationary feature based, Waveform based, Matched
filtering based, Covariance based, Bayeasian compressive
and Feature template based can be seen in [5,6].
&Sudipta Mallick
sudiptamallick91@gmail.com
Susmita Das
sdas@nitrkl.ac.in
Arun Kumar Ray
ak.ray@itr.drdo.in
1
Department of Electrical Engineering, National Institute of
Technology, Rourkela, Odisha, India
2
Department of Range Radar Division, DRDO Integrated Test
Range, Chandipur, Odisha, India
123
Wireless Networks (2023) 29:2747–2758
https://doi.org/10.1007/s11276-023-03349-x(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ResearchGate has not been able to resolve any citations for this publication.
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