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

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        KLT_IWCMC_Final_aam.pdf (PDF, 691Kb)
        Author
        Simpson, Oluyomi
        Abdulkadir, Yusuf
        Sun, Yichuang
        Attention
        2299/20617
        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.
        Publication date
        2018-08-30
        Published in
        International Wireless Communications and Mobile Computing Conference
        Published version
        https://doi.org/10.1109/IWCMC.2018.8450532
        Other links
        http://hdl.handle.net/2299/20617
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