Análise de técnicas de inteligência artificial para o projeto de enlaces de fibras ópticas

• Ano de defesa: 2019
• Autor(a) principal: Lima, Bruno Cesar dos Santos
• Orientador(a): Oliveira, Rafael Euzébio Pereira de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Palavras-chave em Português: inteligência artificial; machine learning; rede neural artificial; bayes; Comunicações ópticas
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24506

Society currently seeks competitiveness for its business, high performance and a low cost support platform, idealizing the contemporary scenario where the world is interconnected by communication networks. Thus the need for optical networks arises because of its advantages of reaching long distances and high speeds with good bandwidth compared to the wired system, but the optical system itself is limited in its resources favoring the need for research to soften the data. damage caused by distortion of optical signals. The purpose of this paper will use machine learning and artificial intelligence techniques to construct a conceptual model capable of predicting signal distortions in optical link designs and their regeneration and thus ensuring their autonomous optimization, with the aim of reducing the project cost of implementing fiber optic links. The computational potential has increased in the last decades favoring the execution of machine learning algorithms and promoting the conditions for this work. It will be made a comparative analysis between three algorithms already used in the literature in optical communications application seeking to find the most suitable algorithm for the construction of this machine learning model that needs a composite output in its predictions, considering the range of variables necessary to elaborate a optical link. The results presented are motivating, showing a high accuracy of predictions of machine learning algorithms around 99% and in the validation of predictions made an optimized link with a BER 1.10−06 evidencing the application of machine learning algorithms in the projects of optical links.