Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17748

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DC Field	Value	Language
dc.contributor.author	Comsa, IS	-
dc.contributor.author	Zhang, S	-
dc.contributor.author	Aydin, ME	-
dc.contributor.author	Kuonen, P	-
dc.contributor.author	Lu, Y	-
dc.contributor.author	Trestian, R	-
dc.contributor.author	Ghinea, G	-
dc.date.accessioned	2019-03-20T14:21:27Z	-
dc.date.available	2018-12-01	-
dc.date.available	2019-03-20T14:21:27Z	-
dc.date.issued	2018-08-06	-
dc.identifier.citation	IEEE Transactions on Network and Service Management, 2018, 15 (4), pp. 1661 - 1675	en_US
dc.identifier.issn	http://dx.doi.org/10.1109/TNSM.2018.2863563	-
dc.identifier.issn	1932-4537	-
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/17748	-
dc.description.sponsorship	European Union	en_US
dc.format.extent	1661 - 1675	-
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.subject	5G	en_US
dc.subject	Packet Scheduling	en_US
dc.subject	Optimization	en_US
dc.subject	Radio Resource Management	en_US
dc.subject	Reinforcement Learning	en_US
dc.subject	Neural Networks	en_US
dc.title	Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management	en_US
dc.type	Article	en_US
dc.identifier.doi	http://dx.doi.org/10.1109/TNSM.2018.2863563	-
dc.relation.isPartOf	IEEE Transactions on Network and Service Management	-
pubs.issue	4	-
pubs.publication-status	Published	-
pubs.volume	15	-
dc.identifier.eissn	1932-4537	-
Appears in Collections:	Dept of Computer Science Research Papers

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