The recent surge in real-time traffic has led to serious energy efficiency concerns in cognitive radio networks (CRNs). Network infrastructure such as base stations (BSs) host different service classes of traffic with stringent quality-of-service (QoS) requirements that need to be satisfied. Thus, maintaining the desired QoS in an energy efficient manner requires a good trade-off between QoS and energy saving. To deal with this problem, this paper proposes a deep learning-based computational-resource-aware energy consumption technique. The proposed scheme uses an exploration technique of the systems' state-space and traffic load prediction to come up with a better trade-off between QoS and energy saving. The simulation results show that the proposed exploration technique performs 9% better than the traditional random tree technique even when the provisioning priority shifts away from energy saving towards QoS, i.e., \alpha > 0.5.


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    Title :

    Optimization and Learning in Energy Efficient Resource Allocation for Cognitive Radio Networks


    Contributors:


    Publication date :

    2019-04-01


    Size :

    625689 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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