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    Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning

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    Type
    Article
    Authors
    Alqerm, Ismail cc
    Shihada, Basem cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-07-25
    Online Publication Date
    2017-07-25
    Print Publication Date
    2017-12
    Permanent link to this record
    http://hdl.handle.net/10754/625251
    
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    Abstract
    The multi-tier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multi-tier 5G network structure using a non-cooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.
    Citation
    AlQerm I, Shihada B (2017) Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning. IEEE Transactions on Vehicular Technology: 1–1. Available: http://dx.doi.org/10.1109/TVT.2017.2731798.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Vehicular Technology
    DOI
    10.1109/TVT.2017.2731798
    Additional Links
    http://ieeexplore.ieee.org/document/7990595/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVT.2017.2731798
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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