Show simple item record

dc.contributor.authorComșa, Ioan-Sorinen
dc.contributor.authorZhang, Sijingen
dc.contributor.authorAydin, Mehmet Eminen
dc.contributor.authorKuonen, Pierreen
dc.contributor.authorTrestian, Ramonaen
dc.contributor.authorGhinea, Gheorghiţăen
dc.date.accessioned2019-12-17T13:07:41Z
dc.date.available2019-12-17T13:07:41Z
dc.date.issued2019-10-14
dc.identifier.citationComșa 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-.en
dc.identifier.issn2078-2489
dc.identifier.doi10.3390/info10100315
dc.identifier.urihttp://hdl.handle.net/10547/623639
dc.description.abstractDue 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
dc.language.isoenen
dc.publisherMDPIen
dc.relation.urlhttps://www.mdpi.com/2078-2489/10/10/315en
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectOFDMAen
dc.subjectradio resource managementen
dc.subjectscheduling optimisationen
dc.subjectfeed forward neural networksen
dc.subjectreinforcement Learningen
dc.subjectG420 Networks and Communicationsen
dc.titleA comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulersen
dc.typeArticleen
dc.identifier.eissn2078-2489
dc.contributor.departmentBrunel Universityen
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentUniversity of the West of Englanden
dc.contributor.departmentHEIA-FR, Switzerlanden
dc.contributor.departmentMiddlesex Universityen
dc.identifier.journalInformationen
dc.date.updated2019-12-17T13:03:30Z
dc.description.noteopen access article with cc licence
html.description.abstractDue 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


Files in this item

Thumbnail
Name:
information-10-00315.pdf
Size:
940.1Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

Green - can archive pre-print and post-print or publisher's version/PDF
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF