A Kosambi-Karhunen–Loève Learning Approach to Cooperative Spectrum Sensing in Cognitive Radio Networks

Oluyomi Simpson; Yusuf Abdulkadir; Yichuang Sun

doi:10.1109/IWCMC.2018.8450532

A Kosambi-Karhunen–Loève Learning Approach to Cooperative Spectrum Sensing in Cognitive Radio Networks

Oluyomi Simpson, Yusuf Abdulkadir, Yichuang Sun

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Abstract

This paper focuses on the issues of cooperative
spectrum sensing (CSS) in a large cognitive radio network (CRN)
where cognitive radio (CR) nodes can cooperative with
neighboring nodes using spatial cooperation. A novel optimal
global primary user (PU) detection framework with geographical
cooperation using a deflection coefficient metric measure to
characterize detection performance is proposed. It is assumed that
only a small fraction of CR nodes communicate with the fusion
center (FC). Optimal cooperative techniques which are global for
class deterministic PU signals are proposed. By establishing the
relationship between the CSS technique design issues and
Kosambi-Karhunen–Loève transform (KLT) the problem is
solved efficiently and the impact on detection performance is
evaluated using simulation.

Original language	English
Pages (from-to)	1094-1098
Number of pages	5
Journal	International Wireless Communications and Mobile Computing Conference, IWCMC
Volume	14
DOIs	https://doi.org/10.1109/IWCMC.2018.8450532
Publication status	Published - 30 Aug 2018

Keywords

Cognitive radio networks
cooperative spectrum sensing
global cooperation
Kosambi-Karhunen-Loève transform
learning

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10.1109/IWCMC.2018.8450532

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Cite this

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title = "A Kosambi-Karhunen–Lo{\`e}ve Learning Approach to Cooperative Spectrum Sensing in Cognitive Radio Networks",

abstract = "This paper focuses on the issues of cooperativespectrum sensing (CSS) in a large cognitive radio network (CRN)where cognitive radio (CR) nodes can cooperative withneighboring nodes using spatial cooperation. A novel optimalglobal primary user (PU) detection framework with geographicalcooperation using a deflection coefficient metric measure tocharacterize detection performance is proposed. It is assumed thatonly a small fraction of CR nodes communicate with the fusioncenter (FC). Optimal cooperative techniques which are global forclass deterministic PU signals are proposed. By establishing therelationship between the CSS technique design issues andKosambi-Karhunen–Lo{\`e}ve transform (KLT) the problem issolved efficiently and the impact on detection performance isevaluated using simulation.",

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author = "Oluyomi Simpson and Yusuf Abdulkadir and Yichuang Sun",

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doi = "10.1109/IWCMC.2018.8450532",

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AU - Simpson, Oluyomi

AU - Abdulkadir, Yusuf

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PY - 2018/8/30

Y1 - 2018/8/30

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KW - cooperative spectrum sensing

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JO - International Wireless Communications and Mobile Computing Conference, IWCMC

JF - International Wireless Communications and Mobile Computing Conference, IWCMC

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A Kosambi-Karhunen–Loève Learning Approach to Cooperative Spectrum Sensing in Cognitive Radio Networks

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Keywords

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