A path forward : 6G resource allocation from a deep Q-learning perspective.
Resumen:
The 6G paradigm presents a myriad of challenges, as it promises complex features such as managing diverse traffic profiles under a unified infrastructure. While many studies propose deep-Q learning (DQN) approaches for resource management in Network Slicing (NS) schemes, these algorithms often face a core issue: they are not easily reproducible in real-world environments due to their high dimensionality. In this study, we analyze a distributed DQN-based radio resource allocation methodology, designed to efficiently meet specific Service Level Agreements (SLAs). Our contribution includes making the code publicly available for further research and evaluation. We then assess its performance through a comparison with a Baseline DQN approach, highlighting the strengths and limitations of both models.
| 2024 | |
| CSIC R&D project : 5/6G Optical Network Convergence: an holistic view | |
|
6G mobile communication Q-learning Codes Network slicing Resource management Faces Service level agreements 6G Resource Allocation Deep Q-Learning Network Slicing |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/48408 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1877555102360797184 |
|---|---|
| author | Inglés, Lucas |
| author2 | Rattaro, Claudina Belzarena, Pablo |
| author2_role | author author |
| author_facet | Inglés, Lucas Rattaro, Claudina Belzarena, Pablo |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Inglés Lucas, Universidad de la República (Uruguay). Facultad de Ingeniería. Rattaro Claudina, Universidad de la República (Uruguay). Facultad de Ingeniería. Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Inglés, Lucas Rattaro, Claudina Belzarena, Pablo |
| dc.date.accessioned.none.fl_str_mv | 2025-02-14T18:02:32Z |
| dc.date.available.none.fl_str_mv | 2025-02-14T18:02:32Z |
| dc.date.issued.none.fl_str_mv | 2024 |
| dc.description.abstract.none.fl_txt_mv | The 6G paradigm presents a myriad of challenges, as it promises complex features such as managing diverse traffic profiles under a unified infrastructure. While many studies propose deep-Q learning (DQN) approaches for resource management in Network Slicing (NS) schemes, these algorithms often face a core issue: they are not easily reproducible in real-world environments due to their high dimensionality. In this study, we analyze a distributed DQN-based radio resource allocation methodology, designed to efficiently meet specific Service Level Agreements (SLAs). Our contribution includes making the code publicly available for further research and evaluation. We then assess its performance through a comparison with a Baseline DQN approach, highlighting the strengths and limitations of both models. |
| dc.description.sponsorship.none.fl_txt_mv | CSIC R&D project : 5/6G Optical Network Convergence: an holistic view |
| dc.format.extent.es.fl_str_mv | 5 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Inglés, L., Rattaro, C. y Belzarena, P. "A path forward : 6G resource allocation from a deep Q-learning perspective" [en línea]. EN: 2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/48408 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | 2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | 6G mobile communication Q-learning Codes Network slicing Resource management Faces Service level agreements 6G Resource Allocation Deep Q-Learning Network Slicing |
| dc.title.none.fl_str_mv | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | The 6G paradigm presents a myriad of challenges, as it promises complex features such as managing diverse traffic profiles under a unified infrastructure. While many studies propose deep-Q learning (DQN) approaches for resource management in Network Slicing (NS) schemes, these algorithms often face a core issue: they are not easily reproducible in real-world environments due to their high dimensionality. In this study, we analyze a distributed DQN-based radio resource allocation methodology, designed to efficiently meet specific Service Level Agreements (SLAs). Our contribution includes making the code publicly available for further research and evaluation. We then assess its performance through a comparison with a Baseline DQN approach, highlighting the strengths and limitations of both models. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_f74771cc5fcb540e68b11bf50f2cc7e1 |
| identifier_str_mv | Inglés, L., Rattaro, C. y Belzarena, P. "A path forward : 6G resource allocation from a deep Q-learning perspective" [en línea]. EN: 2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/48408 |
| publishDate | 2024 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | karina.camps@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Inglés Lucas, Universidad de la República (Uruguay). Facultad de Ingeniería.Rattaro Claudina, Universidad de la República (Uruguay). Facultad de Ingeniería.Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.2025-02-14T18:02:32Z2025-02-14T18:02:32Z2024Inglés, L., Rattaro, C. y Belzarena, P. "A path forward : 6G resource allocation from a deep Q-learning perspective" [en línea]. EN: 2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5.https://hdl.handle.net/20.500.12008/48408The 6G paradigm presents a myriad of challenges, as it promises complex features such as managing diverse traffic profiles under a unified infrastructure. While many studies propose deep-Q learning (DQN) approaches for resource management in Network Slicing (NS) schemes, these algorithms often face a core issue: they are not easily reproducible in real-world environments due to their high dimensionality. In this study, we analyze a distributed DQN-based radio resource allocation methodology, designed to efficiently meet specific Service Level Agreements (SLAs). Our contribution includes making the code publicly available for further research and evaluation. We then assess its performance through a comparison with a Baseline DQN approach, highlighting the strengths and limitations of both models.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2025-02-03T14:03:10Z No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 13adb202270a5f7cee03e795b33133c4 (MD5) IRB24.pdf: 1207444 bytes, checksum: bc6090cedade6d943d6ff099ac2b00e0 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2025-02-14T13:35:47Z (GMT) No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 13adb202270a5f7cee03e795b33133c4 (MD5) IRB24.pdf: 1207444 bytes, checksum: bc6090cedade6d943d6ff099ac2b00e0 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2025-02-14T18:02:32Z (GMT). No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 13adb202270a5f7cee03e795b33133c4 (MD5) IRB24.pdf: 1207444 bytes, checksum: bc6090cedade6d943d6ff099ac2b00e0 (MD5) Previous issue date: 2024CSIC R&D project : 5/6G Optical Network Convergence: an holistic view5 p.application/pdfenengIEEE2024 IEEE URUCON, Montevideo, Uruguay, 18-20 nov. 2024, pp. 1-5.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)6G mobile communicationQ-learningCodesNetwork slicingResource managementFacesService level agreements6GResource AllocationDeep Q-LearningNetwork SlicingA path forward : 6G resource allocation from a deep Q-learning perspective.Ponenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaInglés, LucasRattaro, ClaudinaBelzarena, PabloLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/48408/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/48408/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | A path forward : 6G resource allocation from a deep Q-learning perspective. Inglés, Lucas 6G mobile communication Q-learning Codes Network slicing Resource management Faces Service level agreements 6G Resource Allocation Deep Q-Learning Network Slicing |
| status_str | publishedVersion |
| title | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| title_full | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| title_fullStr | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| title_full_unstemmed | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| title_short | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| title_sort | A path forward : 6G resource allocation from a deep Q-learning perspective. |
| topic | 6G mobile communication Q-learning Codes Network slicing Resource management Faces Service level agreements 6G Resource Allocation Deep Q-Learning Network Slicing |
| url | https://hdl.handle.net/20.500.12008/48408 |
