Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/24134Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Comsa, IS | - |
| dc.contributor.author | Trestian, R | - |
| dc.contributor.author | Muntean, GM | - |
| dc.contributor.author | Ghinea, G | - |
| dc.date.accessioned | 2022-02-18T10:12:20Z | - |
| dc.date.available | 2020-06-01 | - |
| dc.date.available | 2022-02-18T10:12:20Z | - |
| dc.date.issued | 2019-12-19 | - |
| dc.identifier.citation | Comșa, I.-S., Trestian, R., Muntean, G. and Ghinea, G. (2020) '5MART: A 5G SMART Scheduling Framework for Optimizing QoS Through Reinforcement Learning', IEEE Transactions on Network and Service Management, 17 (2), pp. 1110 - 1124. doi: 10.1109/TNSM.2019.2960849. | en_US |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24134 | - |
| dc.description.sponsorship | European Union Horizon 2020 Research and Innovation Programme for the NEWTON project (Grant Number: 688503); 10.13039/501100001602-Science Foundation Ireland (SFI) Research Centres Programme (Grant Number: 12/RC/2289 (Insight Centre for Data Analytics) and 16/SP/3804 (ENABLE)). | en_US |
| dc.format.extent | 1110 - 1124 | - |
| dc.format.medium | Electronic | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | Copyright © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
| dc.subject | 5G | en_US |
| dc.subject | radio resource management | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | scheduling | en_US |
| dc.subject | traffic prioritization | en_US |
| dc.subject | QoS optimization | en_US |
| dc.title | 5MART: A 5G SMART Scheduling Framework for Optimizing QoS through Reinforcement Learning | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1109/TNSM.2019.2960849 | - |
| dc.relation.isPartOf | IEEE Transactions on Network and Service Management | - |
| pubs.issue | 2 | - |
| pubs.publication-status | Published | - |
| pubs.volume | 17 | - |
| dc.identifier.eissn | 1932-4537 | - |
| Appears in Collections: | Dept of Computer Science Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | 2.69 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.