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    Design optimization of resource allocation in OFDMA-based cognitive radio-enabled internet of vehicles (IoVs)∗

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    Authors
    Eze, Joy C.
    Eze, Elias Chinedum
    Zhang, Sijing
    Liu, Enjie
    Affiliation
    University of Bedfordshire
    Issue Date
    2019-11-11
    Subjects
    game theory
    cognitive radio
    WAVE
    Internet of Vehicles (IoVs)
    VANET
    Nash bargaining solution
    OFDMA
    
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    Abstract
    The problem of joint optimal subcarrier and transmit power allocation with quality of service (QoS) guarantee for enhanced packet transmission over a cognitive radio-enabled IoVs network system is considered in this paper. A novel Symmetric Nash bargaining solution (SNBS) based wireless radio resource scheduling scheme in orthogonal frequency division multiple access (OFDMA) cognitive radio (CR)-enabled IoVs network systems is proposed. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced and by applying time-sharing variable transformation, closed-form analytical solutions were obtained. To avoid the iterative processes associated with searching the optimal solution numerically through iterative programming methods, this study developed a low-complexity algorithm. Theoretical analysis and simulation results demonstrate that under similar conditions, the proposed solutions outperform reference scheduler schemes.
    Citation
    Eze J, Eze E, Zhang S, Liu E (2019) 'Design optimization of resource allocation in OFDMA-based cognitive radio-enabled internet of vehicles (IoVs)∗', 2019 25th International Conference on Automation and Computing (ICAC) - Lancaster, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/624351
    DOI
    10.23919/IConAC.2019.8895191
    Additional Links
    https://ieeexplore.ieee.org/document/8895191
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781861376664
    ae974a485f413a2113503eed53cd6c53
    10.23919/IConAC.2019.8895191
    Scopus Count
    Collections
    Computing

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