Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks
Resumen:
The increased sophistication of mobile networks such as 5G and beyond, and the plethora of devices and novel use cases to be supported by these networks, make of the already complex problem of resource allocation in wireless networks a paramount challenge. We address the specific problem of user association, a largely explored yet open resource allocation problem in wireless systems. We introduce GROWS, a deep reinforcement learning (DRL) driven approach to efficiently assign mobile users to base stations, which combines a well-known extension of Deep Q Networks (DQNs) with Graph Neural Networks (GNNs) to better model the function of expected rewards. We show how GROWS can learn a user association policy which improves over currently applied assignation heuristics, as well as compared against more traditional Q-learning approaches, improving utility by more than 10%, while reducing user rejections up to 20%.
| 2022 | |
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Este trabajo se encuentra parcialmente financiado por la Agencia Nacional de Investigacion e Innovación (ANII) a través del proyecto "Inteligencia Artificial para redes 5G" (FMV 1 2019 1 155700), así como por el proyecto Austrian FFG ICT-of-the-Future DynAISEC (Adaptive AI/ML for Dynamic Cybersecurity Systems). Beca doctorado ANII |
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Deep learning Base stations Q-learning 5G mobile communication Wireless networks Benchmark testing Graph neural networks User Association Mobile Networks Reinforcement Learning |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://ieeexplore.ieee.org/document/10000511
https://hdl.handle.net/20.500.12008/35612 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899652771840 |
|---|---|
| author | Randall, Martín |
| author2 | Belzarena, Pablo Larroca, Federico Casas, Pedro |
| author2_role | author author author |
| author_facet | Randall, Martín Belzarena, Pablo Larroca, Federico Casas, Pedro |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Randall Martín, Universidad de la República (Uruguay). Facultad de Ingeniería. Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. Larroca Federico, Universidad de la República (Uruguay). Facultad de Ingeniería. Casas Pedro, AIT, Austrian Institute of Technology, Austria |
| dc.creator.none.fl_str_mv | Randall, Martín Belzarena, Pablo Larroca, Federico Casas, Pedro |
| dc.date.accessioned.none.fl_str_mv | 2023-02-02T21:51:59Z |
| dc.date.available.none.fl_str_mv | 2023-02-02T21:51:59Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | The increased sophistication of mobile networks such as 5G and beyond, and the plethora of devices and novel use cases to be supported by these networks, make of the already complex problem of resource allocation in wireless networks a paramount challenge. We address the specific problem of user association, a largely explored yet open resource allocation problem in wireless systems. We introduce GROWS, a deep reinforcement learning (DRL) driven approach to efficiently assign mobile users to base stations, which combines a well-known extension of Deep Q Networks (DQNs) with Graph Neural Networks (GNNs) to better model the function of expected rewards. We show how GROWS can learn a user association policy which improves over currently applied assignation heuristics, as well as compared against more traditional Q-learning approaches, improving utility by more than 10%, while reducing user rejections up to 20%. |
| dc.description.es.fl_txt_mv | Presentado y publicado en 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022, pp. 1-6. |
| dc.description.sponsorship.none.fl_txt_mv | Este trabajo se encuentra parcialmente financiado por la Agencia Nacional de Investigacion e Innovación (ANII) a través del proyecto "Inteligencia Artificial para redes 5G" (FMV 1 2019 1 155700), así como por el proyecto Austrian FFG ICT-of-the-Future DynAISEC (Adaptive AI/ML for Dynamic Cybersecurity Systems). Beca doctorado ANII |
| dc.format.extent.es.fl_str_mv | 6 p. |
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| dc.identifier.citation.es.fl_str_mv | Randall, M., Belzarena, P., Larroca, F. y otros. Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks. [Preprint]. Publicado en: 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022. pp. 1-6. DOI: 10.1109/LATINCOM56090.2022.10000511 |
| dc.identifier.uri.none.fl_str_mv | https://ieeexplore.ieee.org/document/10000511 https://hdl.handle.net/20.500.12008/35612 |
| dc.language.iso.none.fl_str_mv | en eng |
| 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 | Deep learning Base stations Q-learning 5G mobile communication Wireless networks Benchmark testing Graph neural networks User Association Mobile Networks Reinforcement Learning |
| dc.title.none.fl_str_mv | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Presentado y publicado en 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022, pp. 1-6. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_4372f62d9db830da8b0b45e616811f7e |
| identifier_str_mv | Randall, M., Belzarena, P., Larroca, F. y otros. Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks. [Preprint]. Publicado en: 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022. pp. 1-6. DOI: 10.1109/LATINCOM56090.2022.10000511 |
| 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:20.500.12008/35612 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@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 | Randall Martín, Universidad de la República (Uruguay). Facultad de Ingeniería.Belzarena Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.Larroca Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.Casas Pedro, AIT, Austrian Institute of Technology, Austria2023-02-02T21:51:59Z2023-02-02T21:51:59Z2022Randall, M., Belzarena, P., Larroca, F. y otros. Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks. [Preprint]. Publicado en: 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022. pp. 1-6. DOI: 10.1109/LATINCOM56090.2022.10000511https://ieeexplore.ieee.org/document/10000511https://hdl.handle.net/20.500.12008/35612Presentado y publicado en 2022 IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, Brazil, 30 nov-2 dec. 2022, pp. 1-6.The increased sophistication of mobile networks such as 5G and beyond, and the plethora of devices and novel use cases to be supported by these networks, make of the already complex problem of resource allocation in wireless networks a paramount challenge. We address the specific problem of user association, a largely explored yet open resource allocation problem in wireless systems. We introduce GROWS, a deep reinforcement learning (DRL) driven approach to efficiently assign mobile users to base stations, which combines a well-known extension of Deep Q Networks (DQNs) with Graph Neural Networks (GNNs) to better model the function of expected rewards. We show how GROWS can learn a user association policy which improves over currently applied assignation heuristics, as well as compared against more traditional Q-learning approaches, improving utility by more than 10%, while reducing user rejections up to 20%.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2023-01-30T20:00:18Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) RBLC22a.pdf: 411651 bytes, checksum: 534c61c88131c56b87535fbd0175c17c (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2023-02-02T19:14:36Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) RBLC22a.pdf: 411651 bytes, checksum: 534c61c88131c56b87535fbd0175c17c (MD5)Made available in DSpace by Seroubian Mabel (mabel.seroubian@seciu.edu.uy) on 2023-02-02T21:51:59Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) RBLC22a.pdf: 411651 bytes, checksum: 534c61c88131c56b87535fbd0175c17c (MD5) Previous issue date: 2022Este trabajo se encuentra parcialmente financiado por la Agencia Nacional de Investigacion e Innovación (ANII) a través del proyecto "Inteligencia Artificial para redes 5G" (FMV 1 2019 1 155700), así como por el proyecto Austrian FFG ICT-of-the-Future DynAISEC (Adaptive AI/ML for Dynamic Cybersecurity Systems).Beca doctorado ANII6 p.application/pdfenengLas 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)Deep learningBase stationsQ-learning5G mobile communicationWireless networksBenchmark testingGraph neural networksUser AssociationMobile NetworksReinforcement LearningDeep reinforcement learning and graph neural networks for efficient resource allocation in 5G networksPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRandall, MartínBelzarena, PabloLarroca, FedericoCasas, PedroTelecomunicacionesAnálisis de Redes, Tráfico y Estadísticas de ServiciosLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/35612/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks Randall, Martín Deep learning Base stations Q-learning 5G mobile communication Wireless networks Benchmark testing Graph neural networks User Association Mobile Networks Reinforcement Learning |
| status_str | submittedVersion |
| title | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| title_full | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| title_fullStr | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| title_full_unstemmed | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| title_short | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| title_sort | Deep reinforcement learning and graph neural networks for efficient resource allocation in 5G networks |
| topic | Deep learning Base stations Q-learning 5G mobile communication Wireless networks Benchmark testing Graph neural networks User Association Mobile Networks Reinforcement Learning |
| url | https://ieeexplore.ieee.org/document/10000511 https://hdl.handle.net/20.500.12008/35612 |
