This preprint is under consideration at Wireless Personal Communications. 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.
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Research Article
venkateshkumar Udayamoorthy
Sri Krishna College of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4950-3069
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the Kth PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.
Keywords
cooperative spectrum sensing, classification, fusion centre, kernel, support vector machine
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This preprint is under consideration at Wireless Personal Communications. 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 Article
venkateshkumar Udayamoorthy
Sri Krishna College of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4950-3069
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: Under Review
Version 1
Posted 24 Mar, 2021
No community comments so far
Review #? received
Received 21 Mar, 2021
Editor assigned
On 08 Feb, 2021
First submitted
On 06 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the Kth PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.
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