NOn-parametric Bayesian channEls cLustering (NOBEL) Scheme for Wireless Multimedia Cognitive Radio Networks

Amjad Ali, Muhammad Ejaz Ahmed, Farman Ali, Nguyen H. Tran, Dusit Niyato, Sangheon Pack

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.

    Original languageEnglish
    Article number8792138
    Pages (from-to)2293-2305
    Number of pages13
    JournalIEEE Journal on Selected Areas in Communications
    Volume37
    Issue number10
    DOIs
    Publication statusPublished - 2019 Oct

    Bibliographical note

    Publisher Copyright:
    © 1983-2012 IEEE.

    Keywords

    • QoS-level quantification
    • Wireless multimedia applications
    • channel clustering
    • multi-channel
    • multimedia CRNs

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Electrical and Electronic Engineering

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