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

Oluyomi Simpson, Yusuf Abdulkadir, Yichuang Sun

Research output: Contribution to journalConference articlepeer-review

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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 languageEnglish
Pages (from-to)1094-1098
Number of pages5
JournalInternational Wireless Communications and Mobile Computing Conference, IWCMC
Volume14
DOIs
Publication statusPublished - 30 Aug 2018

Keywords

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

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