BIROn - Birkbeck Institutional Research Online

    Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

    Yoo, Paul (2019) Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels. IEEE Transactions on Sustainable Computing , ISSN 2377-3782. (In Press)

    [img] Text
    Censored Sensing-Final.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only until 25 February 2020.

    Download (2MB) | Request a copy

    Abstract

    The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corre- sponding probability of detection, probability of false alarm and sec- ondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Paul Yoo
    Date Deposited: 20 Mar 2019 18:16
    Last Modified: 27 Jul 2019 05:19
    URI: http://eprints.bbk.ac.uk/id/eprint/26768

    Statistics

    Downloads
    Activity Overview
    2Downloads
    24Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item