2025-01-092025-01-092023-12-30FERREIRA, Jamelly Freitas. Beam-selection otimizado por aprendizado de máquina : uma abordagem multimodal. Orientador: Aldebaro Barreto da Rocha Klautau Júnior.; Coorientador: Diego de Azevedo Gomes. 2023. 50 f. Dissertação (Mestrado em Engenharia Elétrica) - Instituto de Tecnologia, Universidade Federal do Pará, Belém, 2023. Disponível em:https://repositorio.ufpa.br/jspui/handle/2011/16706 . Acesso em:.https://repositorio.ufpa.br/jspui/handle/2011/16706This dissertation aims to investigate the use of machine learning models using multimodal data as input to optimize the Beam-Selection process in millimeter-wave based networks. The use of Deep Learning has intensified in different areas, and it is possible to obtaing performance equal or superior to human performance, so its use is also promising in wireless communication scenarios. This work used data from different sources, which proved to be convenient since it is possible to adjust the model according to the quality/availability of this data. After executing the experiments and obtaining the results, it was observed that it is possible to obtain significant performance in different metrics even with simpler data such as image and coordinate.Acesso AbertoAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/Aprendizado de máquinaAprendizagem profundaRedes neuraisSeleção de feixeMachine learningDeep learningNeural networkBeam-selectionDissertaçãoCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOESTELECOMUNICAÇÕES