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    Enhancing user fairness in OFDMA radio access networks through machine learning

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    Authors
    Comşa, Ioan-Sorin
    Zhang, Sijing
    Aydin, Mehmet Emin
    Kuonen, Pierre
    Trestian, Ramona
    Ghinea, Gheorghiţă
    Affiliation
    Brunel University
    University of Bedfordshire
    University of the West of England
    HEIA-FR
    Middlesex University London
    Issue Date
    2019-06-13
    Subjects
    OFDMA
    machine learning
    
    Metadata
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    Abstract
    The problem of radio resource scheduling subject to fairness satisfaction is very challenging even in future radio access networks. Standard fairness criteria aim to find the best trade-off between overall throughput maximization and user fairness satisfaction under various types of network conditions. However, at the Radio Resource Management (RRM) level, the existing schedulers are rather static being unable to react according to the momentary networking conditions so that the user fairness measure is maximized all time. This paper proposes a dynamic scheduler framework able to parameterize the proportional fair scheduling rule at each Transmission Time Interval (TTI) to improve the user fairness. To deal with the framework complexity, the parameterization decisions are approximated by using the neural networks as non-linear functions. The actor-critic Reinforcement Learning (RL) algorithm is used to learn the best set of non-linear functions that approximate the best fairness parameters to be applied in each momentary state. Simulations results reveal that the proposed framework outperforms the existing fairness adaptation techniques as well as other types of RL-based schedulers.
    Citation
    Comsa IS, Zhang S, Aydin M, Kuonen P, Trestian R, Ghinea G (2019) 'Enhancing user fairness in OFDMA radio access networks through machine learning', 2019 Wireless Days (WD) - Manchester, IEEE.
    Publisher
    IEEE
    Journal
    2019 WIRELESS DAYS (WD)
    URI
    http://hdl.handle.net/10547/624167
    DOI
    10.1109/WD.2019.8734262
    Additional Links
    https://ieeexplore.ieee.org/document/8734262
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISSN
    2156-9711
    ae974a485f413a2113503eed53cd6c53
    10.1109/WD.2019.8734262
    Scopus Count
    Collections
    Computing

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