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Multi-layered reinforcement learning approach for radio resource management
dc.contributor.author | Kevin, Collados |
dc.contributor.author | Gorricho Moreno, Juan Luis |
dc.contributor.author | Serrat Fernández, Juan |
dc.contributor.author | Hu, Zheng |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica |
dc.date.accessioned | 2014-02-12T14:25:35Z |
dc.date.created | 2014-02-07 |
dc.date.issued | 2014-02-07 |
dc.identifier.citation | Kevin, C. [et al.]. Multi-layered reinforcement learning approach for radio resource management. "Lecture Notes in Electrical Engineering", 07 Febrer 2014, vol. 277, p. 1191-1199. |
dc.identifier.issn | 1876-1100 |
dc.identifier.uri | http://hdl.handle.net/2117/21540 |
dc.description.abstract | In this paper we face the challenge of designing self-tuning systems governing the working parameters of base stations on a mobile network system to optimize the quality of service and the economic benefit of the operator. In order to accomplish this double objective, we propose the combined use of fuzzy logic and reinforcement learning to implement a self-tuning system using a novel approach based on a two-agent system. Different combinations of reinforcement learning techniques, on both agents, have been tested to deduce the optimal approach. The best results have been obtained applying the Q-learning technique on both agents, clearly outperforming the alternative of using non-learning algorithms. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils |
dc.subject.lcsh | Mobile communication systems |
dc.subject.other | Radio resource management |
dc.subject.other | Cellular networks |
dc.subject.other | Reinforcement learning |
dc.subject.other | Fuzzy logic |
dc.title | Multi-layered reinforcement learning approach for radio resource management |
dc.type | Article |
dc.subject.lemac | Comunicacions mòbils, Sistemes de |
dc.contributor.group | Universitat Politècnica de Catalunya. MAPS - Management, Pricing and Services in Next Generation Networks |
dc.identifier.doi | 10.1007/978-3-319-01766-2_135 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007/978-3-319-01766-2_135 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 13028891 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Kevin, C.; Gorricho, J.; Serrat, J.; Hu, Z. |
local.citation.publicationName | Lecture Notes in Electrical Engineering |
local.citation.volume | 277 |
local.citation.startingPage | 1191 |
local.citation.endingPage | 1199 |
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