A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers

Comsa, Ioan-Sorin (Department of Computer Science, Nrunel University London, London, UK) ; Zhang, Sijing (School of Computer Science and Technology, University of Bedfordshire, Luton, UK) ; Aydin, Mehmet (Departmentof Computer Science and Creative Technologies, University of the West 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, Nrunel University London, London, UK)

Due to large-scale control problems in 5G access networks, the complexity of radio resource management is expected to increase significantly. Reinforcement learning is seen as a promising solution that can enable intelligent decision-making and reduce the complexity of different optimization problems for radio resource management. The packet scheduler is an important entity of radio resource management that allocates users’ data packets in the frequency domain according to the implemented scheduling rule. In this context, by making use of reinforcement learning, we could actually determine, in each state, the most suitable scheduling rule to be employed that could improve the quality of service provisioning. In this paper, we propose a reinforcement learning-based framework to solve scheduling problems with the main focus on meeting the user fairness requirements. This framework makes use of feed forward neural networks to map momentary states to proper parameterization decisions for the proportional fair scheduler. The simulation results show that our reinforcement learning framework outperforms the conventional adaptive schedulers oriented on fairness objective. Discussions are also raised to determine the best reinforcement learning algorithm to be implemented in the proposed framework based on various scheduler settings.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Date:
2019-10
Pagination:
28 p.
Published in:
Information
Numeration (vol. no.):
2019, vol. 10, no. 10, article no. 315
DOI:
ISSN:
2078-2489
Appears in Collection:



 Record created 2020-01-07, last modified 2020-10-27

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