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    Enhanced Online Q-Learning Scheme for Energy Efficient Power Allocation in Cognitive Radio Networks

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    Type
    Conference Paper
    Authors
    AlQerm, Ismail
    Shihada, Basem cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-01
    Online Publication Date
    2019-11-01
    Print Publication Date
    2019-04
    Permanent link to this record
    http://hdl.handle.net/10754/660617
    
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    Abstract
    The considerable growth in demands for wireless services have led to spectrum scarcity challenge. Cognitive radio came into practice to deal with the scarcity problem by granting cognitive users access to the licensed spectrum. However, this solution requires efficient power allocation strategies to guarantee QoS for cognitive system, reduce power consumption, and protect primary users from the cognitive users' interference impact. In this paper, we investigate the energy efficient power allocation problem for cognitive radio networks in underlay mode. We propose a novel approximated online Q-learning scheme for power allocation in which cognitive users learn with conjecture feature to select the most appropriate power level. The power allocation problem is formulated as an optimization problem with the goal to maximize energy efficiency under QoS and interference constraints. The scheme is evaluated using software defined radio testbed and simulations. The evaluation results demonstrate the scheme capability to guarantee SINR for both primary and cognitive systems and mitigate interference with minimum power consumption in comparison with other schemes.
    Citation
    AlQerm, I., & Shihada, B. (2019). Enhanced Online Q-Learning Scheme for Energy Efficient Power Allocation in Cognitive Radio Networks. 2019 IEEE Wireless Communications and Networking Conference (WCNC). doi:10.1109/wcnc.2019.8885623
    Publisher
    IEEE
    Conference/Event name
    2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
    DOI
    10.1109/WCNC.2019.8885623
    Additional Links
    https://ieeexplore.ieee.org/document/8885623/
    ae974a485f413a2113503eed53cd6c53
    10.1109/WCNC.2019.8885623
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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