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Enhancing user fairness in OFDMA radio access networks through machine learning

  • Ioan-Sorin Comşa
    ,
  • Sijing Zhang
    ,
  • Mehmet Emin Aydin
    ,
  • Pierre Kuonen
    ,
  • Ramona Trestian
    ,
  • Gheorghiţă Ghinea
  • Brunel University London
    ,
  • University of the West of England
    ,
  • HEIA-FR
    ,
  • Middlesex University
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 13/06/2019

Publication status

Published - 13/06/2019

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

External Publication IDs

  • handle.net: 10547/624167
  • Scopus: 85068564502

Host publication title

2019 Wireless Days (WD)

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