00 CAMPUS ARISTÓTELES CALAZANS SIMÕES (CAMPUS A. C. SIMÕES) IC - INSTITUTO DE COMPUTAÇÃO Dissertações e Teses defendidas na UFAL - IC
Use este identificador para citar ou linkar para este item: http://www.repositorio.ufal.br/jspui/handle/123456789/15686
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Campo DCValorIdioma
dc.contributor.advisor1Barboza, Erick de Andrade-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/1049532071774598pt_BR
dc.contributor.referee1Martins Filho, Joaquim Ferreira-
dc.contributor.referee2Santos Neto, Baldoino Fonseca dos-
dc.creatorPinheiro Filho, José Carlos-
dc.creator.Latteshttp://lattes.cnpq.br/5154031187733493pt_BR
dc.date.accessioned2025-03-10T11:24:18Z-
dc.date.available2025-03-10-
dc.date.available2025-03-10T11:24:18Z-
dc.date.issued2024-07-28-
dc.identifier.citationPINHEIRO FILHO, José Carlos. Defining optical amplifiers gains using reinforcement learning. 2025. 39 f. Dissertação (Mestrado em Informática.) – Programa de Pós-Graduação em Informática, Instituto de Computação, Universidade Federal de Alagoas, Maceió, 2024.pt_BR
dc.identifier.urihttp://www.repositorio.ufal.br/jspui/handle/123456789/15686-
dc.description.abstract.pt_BR
dc.languageporpt_BR
dc.publisherUniversidade Federal de Alagoaspt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.programPrograma de Pós-Graduação em Informáticapt_BR
dc.publisher.initialsUFALpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectAmplificadores ópticospt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectComunicação ópticapt_BR
dc.subjectOptical amplifierspt_BR
dc.subjectArtificial intelligencept_BR
dc.subjectoptical communicationpt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOpt_BR
dc.titleDefining optical amplifiers gains using reinforcement learningpt_BR
dc.typeDissertaçãopt_BR
dc.description.resumoThe dynamic nature of future optical networks requires that amplifiers autonomously ad just their gain in response to changing network conditions, such as the addition or removal of channels, to maintain signal power and General Signal-to-Noise Ratio (GSNR) across a cascade of amplifiers. This challenge is known as the Adaptive Control of Optical Amplifier Operating Point (ACOP). Solutions for the ACOP problem have been proposed using tech niques such as cognitive learning, supervised learning, and evolutionary algorithms. Among these, the evolutionary approach has achieved the best results in terms of transmission qual ity. However, it has a relatively high response time, which is a significant drawback for operational deployment. On the other hand, reinforcement learning techniques are impor tant in the field of artificial intelligence to solve problems in real-time with trained models. This work proposes the first modeling of the ACOP problem using reinforcement learning,, specifically employing the Proximal Policy Optimization (PPO) algorithm integrated with the GNPy simulator. The objective is to improve signal quality by maximizing the GSNR through interaction with the gains of the amplifiers in the link. In four scenarios with varying numbers of channels, this approach achieved results close to the evolutionary approach, but with a speed-up of 300 times.pt_BR
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