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Open AccessFeature PaperArticle

A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

1
Department of Computer Science, Brunel University London, Kingston Lane, London UB8 3PH, UK
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School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK
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Department of Computer Science and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK
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Department of Communications and Information Technology, HEIA-FR, CH-1700 Fribourg, Switzerland
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Faculty of Science and Technology, Middlesex University London, Hendon, London NW4 4BT, UK
*
Author to whom correspondence should be addressed.
Information 2019, 10(10), 315; https://doi.org/10.3390/info10100315
Received: 29 August 2019 / Revised: 23 September 2019 / Accepted: 9 October 2019 / Published: 14 October 2019
(This article belongs to the Special Issue Emerging Topics in Wireless Communications for Future Smart Cities)
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. View Full-Text
Keywords: OFDMA; radio resource management; scheduling optimization; feed forward neural networks; reinforcement learning OFDMA; radio resource management; scheduling optimization; feed forward neural networks; reinforcement learning
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Comșa, I.-S.; Zhang, S.; Aydin, M.; Kuonen, P.; Trestian, R.; Ghinea, G. A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers. Information 2019, 10, 315.

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