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

Comşa, Ioan-Sorin; Comsa, Ioan; Zhang, Sijing; Aydin, Mehmet; Kuonen, Pierre; Trestian, Romana; Ghinea, George; Ghinea, Gheorghiţa


Ioan-Sorin Comşa

Ioan Comsa

Sijing Zhang

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Dr Mehmet Aydin
Senior Lecturer in Networks and Mobile Computing

Pierre Kuonen

Romana Trestian

George Ghinea

Gheorghiţa Ghinea


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.

Journal Article Type Article
Publication Date Oct 14, 2019
Journal Information
Electronic ISSN 2078-2489
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 10
Issue 10
Pages 315
APA6 Citation Ghinea, G., Comşa, I. S., Comsa, I., Zhang, S., Aydin, M., Kuonen, P., …Ghinea, G. (2019). A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers. Information, 10(10), 315.
Keywords Information Systems


A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers (954 Kb)


Copyright Statement
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 (

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