Enhancing user fairness in OFDMA radio access networks through machine learning

Comsa, Ioan-Sorin (Department of Computer science, Brunel University London, London, UK) ; Zhang, Sijing (School of Bedfordshire, Luton, UK) ; Aydin, Mehmet (Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK) ; Kuonen, Pierre (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Trestian, Ramona (Faculty of Science and Technology, Middlesex University London, London, UK) ; Ghinea, Gheorghita (Department of Computer science, Brunel University London, London, UK)

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.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Publisher:
Manchester, United Kingdom, 24-26 April 2019
Date:
2019-04
Manchester, United Kingdom
24-26 April 2019
Pagination:
8 p.
Published in:
proceedings of 11th Wireless Days Conference, 24-26 April 2019, Manchester, United Kingdom
ISBN:
978-1-7281-0117-0
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 Record created 2020-01-07, last modified 2020-01-14

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