Navegando por Autor "OLIVEIRA, Ailton Pinto de"

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Acesso aberto (Open Access)
Beam tracking using deep learning applied to 6G MIMO
(Universidade Federal do Pará, 2024-12-16) OLIVEIRA, Ailton Pinto de; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
This work explores the application of machine learning to enhance beam tracking in 6G MIMO Vehicle-to-Infrastructure (V2I) communications. Beam tracking, essential for sustaining reliable mmWave connections, remains challenging due to the high mobility of vehicular environments and the significant overhead associated with millimeter wave MIMO beamforming. While beam selection has been extensively studied, ML-based beam tracking is relatively underexplored, largely due to the scarcity of comprehensive datasets. To bridge this gap, this study introduces a novel public multimodal dataset, designed in accordance with 3GPP requirements, which combines wireless channel data with multimodal sensor information. This dataset supports the evaluation of advanced data fusion algorithms specifically tailored to V2I scenarios. Furthermore, a custom recurrent neural network (RNN) architecture is proposed as a robust solution for effective beam tracking, leveraging temporal and multimodal data to address the challenges of dynamic vehicular communications.
Acesso aberto (Open Access)
Simulation of machine learning-based 6G systems in virtual worlds
(International Telecommunication Union, 2021) OLIVEIRA, Ailton Pinto de; NASCIMENTO, Arthur Matheus do; COSTA, Walter Tadeu Neves Frazão da; TRINDADE, Isabela Pamplona; BASTOS, Felipe Henrique Bastos e; GOMES, Diego de Azevedo; MÜLLER, Francisco Carlos Bentes Frey; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha
Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.