See the published version of this article at EURASIP Journal on Advances in Signal Processing.
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Jin Lu
Yunnan University
Corresponding Author
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View the published article at EURASIP Journal on Advances in Signal Processing.
Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.
Keywords
spectrum sensing, stochastic resonance, likelihood ratio test, filter
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CURRENT STATUS: Published
Version 2
Posted 11 Aug, 2020
Published
On 06 Jan, 2021
No community comments so far
Editorial decision: Minor revision
On 29 Sep, 2020
Review #3 received
Received 28 Sep, 2020
Reviewer #3 agreed
On 07 Sep, 2020
Review #1 received
Received 06 Sep, 2020
Review #2 received
Received 05 Sep, 2020
Reviewer #2 agreed
On 04 Sep, 2020
Reviewer #1 agreed
On 09 Aug, 2020
Reviewers invited
Invitations sent on 07 Aug, 2020
Editor assigned
On 03 Aug, 2020
Submission checks complete
On 02 Aug, 2020
Editor invited
On 02 Aug, 2020
No comments provided
Editorial decision: Major revision
On 17 Jul, 2020
Review #3 received
Received 13 Jul, 2020
Review #2 received
Received 13 Jul, 2020
Review #1 received
Received 11 Jul, 2020
Reviewer #3 agreed
On 28 Jun, 2020
Reviewer #2 agreed
On 26 Jun, 2020
Reviewers invited
Invitations sent on 25 Jun, 2020
Reviewer #1 agreed
On 25 Jun, 2020
Editor assigned
On 24 Jun, 2020
Editor invited
On 23 Jun, 2020
Submission checks complete
On 19 Jun, 2020
First submitted
On 18 Jun, 2020
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Subject Areas
Systems and Networking
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This preprint is under consideration at EURASIP Journal on Advances in Signal Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Jin Lu
Yunnan University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 11 Aug, 2020
Published
On 06 Jan, 2021
No community comments so far
Editorial decision: Minor revision
On 29 Sep, 2020
Review #3 received
Received 28 Sep, 2020
Reviewer #3 agreed
On 07 Sep, 2020
Review #1 received
Received 06 Sep, 2020
Review #2 received
Received 05 Sep, 2020
Reviewer #2 agreed
On 04 Sep, 2020
Reviewer #1 agreed
On 09 Aug, 2020
Reviewers invited
Invitations sent on 07 Aug, 2020
Editor assigned
On 03 Aug, 2020
Submission checks complete
On 02 Aug, 2020
Editor invited
On 02 Aug, 2020
No comments provided
Editorial decision: Major revision
On 17 Jul, 2020
Review #3 received
Received 13 Jul, 2020
Review #2 received
Received 13 Jul, 2020
Review #1 received
Received 11 Jul, 2020
Reviewer #3 agreed
On 28 Jun, 2020
Reviewer #2 agreed
On 26 Jun, 2020
Reviewers invited
Invitations sent on 25 Jun, 2020
Reviewer #1 agreed
On 25 Jun, 2020
Editor assigned
On 24 Jun, 2020
Editor invited
On 23 Jun, 2020
Submission checks complete
On 19 Jun, 2020
First submitted
On 18 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Advances in Signal Processing.
Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This preprint is available for download as a PDF.
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