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

Comsa, Ioan-Sorin ORCID logoORCID: https://orcid.org/0000-0002-9121-0286, Zhang, Sijing, Aydin, Mehmet ORCID logoORCID: https://orcid.org/0000-0002-4890-5648, Kuonen, Pierre, Trestian, Ramona ORCID logoORCID: https://orcid.org/0000-0003-3315-3081 and Ghinea, Gheorghita ORCID logoORCID: https://orcid.org/0000-0003-2578-5580 (2019) A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers. Information, 10 (10) , e315. ISSN 2078-2489 [Article] (doi:10.3390/info10100315)

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Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings.

Item Type: Article
Keywords (uncontrolled): OFDMA, radio resource management, scheduling optimization, feed forward neural networks, reinforcement learning
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 27855
Notes on copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
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Depositing User: Jisc Publications Router
Date Deposited: 16 Oct 2019 09:50
Last Modified: 16 Oct 2019 09:50
URI: https://eprints.mdx.ac.uk/id/eprint/27855

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