Mostrar o rexistro simple do ítem
Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems
dc.contributor.author | Pereira-Ruisánchez, Dariel | |
dc.contributor.author | Fresnedo, Óscar | |
dc.contributor.author | Pérez-Adán, Darian | |
dc.contributor.author | Castedo, Luis | |
dc.date.accessioned | 2023-12-19T18:25:09Z | |
dc.date.available | 2023-12-19T18:25:09Z | |
dc.date.issued | 2023-07 | |
dc.identifier.citation | D. Pereira-Ruisánchez, Ó. Fresnedo, D. Pérez-Adán and L. Castedo, "Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems," in IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 9099-9114, July 2023, doi: 10.1109/TVT.2023.3249353. | es_ES |
dc.identifier.issn | 0018-9545 | |
dc.identifier.uri | http://hdl.handle.net/2183/34562 | |
dc.description | © 2023 IEEE. This version of the article has been accepted for publication, after peer review. 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. The Version of Record is available online at: https://doi.org/10.1109/TVT.2023.3249353. | es_ES |
dc.description.abstract | [Abstract]: The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference. | es_ES |
dc.description.sponsorship | This work has been supported by grants ED431C 2020/15 and ED431G 2019/01 (to support the Centro de Investigación de Galicia “CITIC”) funded by Xunta de Galicia and ERDF Galicia 2014-2020; and by grants PID2019-104958RB-C42 (ADELE) and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/15 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104958RB-C42/ES/AVANCES EN CODIFICACIÓN Y PROCESADO DE SEÑAL PARA LA SOCIEDAD DIGITAL | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BES-2017-081955/ES/ | es_ES |
dc.relation.isversionof | https://doi.org/10.1109/TVT.2023.3249353 | |
dc.relation.uri | https://doi.org/10.1109/TVT.2023.3249353 | es_ES |
dc.rights | © 2023 IEEE. All rights reserved. Todos os dereitos reservados. | es_ES |
dc.subject | Deep contextual bandit | es_ES |
dc.subject | DDPG | es_ES |
dc.subject | Deep reinforcement learning | es_ES |
dc.subject | Intelligent reflecting surfaces | es_ES |
dc.subject | MIMO | es_ES |
dc.title | Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | IEEE Transactions on Vehicular Technology | es_ES |
UDC.volume | 72 | es_ES |
UDC.issue | 7 | es_ES |
UDC.startPage | 9099 | es_ES |
UDC.endPage | 9114 | es_ES |
dc.identifier.doi | 10.1109/TVT.2023.3249353 |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-GTEC - Artigos [193]