2025-01-162025-01-162023-03-10BASTOS, Felipe Henrique Bastos e. A Modular framework for AI/ML applied to B5G V2X networks co-simulations. Orientador: Aldebaro Barreto da Rocha Klautau Júnior. 2023. 59 f. Dissertação (Mestrado em Engenharia Elétrica) - Instituto de Tecnologia, Universidade Federal do Pará, Belém, 2023. Disponível em: https://repositorio.ufpa.br/jspui/handle/2011/16727. Acesso em:.https://repositorio.ufpa.br/jspui/handle/2011/16727The use of AI/Ml is a key feature for the new generations of mobile communications as the actual generations are becoming more complex in order to provide a faster connection for a large number of users, but in general, it requires large datasets in order to produce high-quality AI/ML models. Due to the costs of collecting real measurements, especially in vehicular and aerial scenarios, this dissertation proposes a modular methodology that enables the combination of different simulators in order to produce realistic datasets for V2X and aerial cellular communications. The methodology also brings the possibility to train AI/ML models in-loop with the simulations. Furthermore, this dissertation details benchmarks for CPU and simulation time in different simulation scenarios and also the results of a use case showing the data that can be extracted from the combinations of the used simulators, where it is possible to observe that the use of parallel computing can reduce the simulation time by approximately five times.Acesso AbertoAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/5G - Quinta geração de tecnologia de telecomunicações móveisV2X - Veículo-para-tudoUAV - Veículo aéreo não tripuladoIA/ML inteligência artificial/aprendizado de máquina5G Fifth-generation of mobile telecommunications technologyUAV unmanned aerial vehicle,V2X Vehicle-to-everythingAI/ML artificial intelligence/machine learningDissertaçãoCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOESINTELIGÊNCIA COMPUTACIONALCOMPUTAÇÃO APLICADA