A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers
Authors
Comșa, Ioan-SorinZhang, Sijing
Aydin, Mehmet Emin
Kuonen, Pierre
Trestian, Ramona
Ghinea, Gheorghiţă
Affiliation
Brunel UniversityUniversity of Bedfordshire
University of the West of England
HEIA-FR, Switzerland
Middlesex University
Issue Date
2019-10-14Subjects
OFDMAradio resource management
scheduling optimisation
feed forward neural networks
reinforcement Learning
G420 Networks and Communications
Metadata
Show full item recordAbstract
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-TextCitation
Comșa IS, Zhang S, Aydin M, Kuonen P, Trestian R, Ghinea (2019) 'A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers', Information, 10 (10), pp.315-.Publisher
MDPIJournal
InformationAdditional Links
https://www.mdpi.com/2078-2489/10/10/315Type
ArticleLanguage
enISSN
2078-2489EISSN
2078-2489ae974a485f413a2113503eed53cd6c53
10.3390/info10100315
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