Navegando por Assunto "Beam-selection"

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Acesso aberto (Open Access)
5G MIMO and LIDAR data for machine learning: mmWave beam-selection using deep learning
(Universidade Federal do Pará, 2019-08-29) DIAS, Marcus Vinicius de Oliveira; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Modern communication systems can exploit the increasing number of sensor data currently used in advanced equipment and reduce the overhead associated with link configuration. Also, the increasing complexity of networks suggests that machine learning (ML), such as deep neural networks, can effectively improve 5G technologies. The lack of large datasets make harder to investigate the application of deep learning in wireless communication. This work presents a simulation methodology (RayMobTime) that combines a vehicle traffic simulation (SUMO) with a ray-tracing simulator (Remcom’s Wireless InSite), to generate channels that represents realistic 5G scenarios, as well as the creation of LIDAR sensor data (via Blensor). The created dataset is utilized to investigate beam-selection techniques on vehicle-to-infrastructure using millimeter waves on different architectures, such as distributed architecture (usage of the information of only a selected vehicle, and processing of data on the vehicle) and centralized architectures (usage of all present information provided by the sensors in a given moment, processing at the base station). The results indicate that deep convolutional neural networks can be utilized to select beams under a top-M classification framework. It also shows that a distributed LIDAR-based architecture provides robust performance irrespective of car penetration rate, outperforming other architectures, as well as can be used to detect line-of-sight (LOS) with reasonable accuracy.
Acesso aberto (Open Access)
Beam-selection otimizado por aprendizado de máquina : uma abordagem multimodal
(Universidade Federal do Pará, 2023-12-30) FERREIRA, Jamelly Freitas; GOMES, Diego de Azevedo; http://lattes.cnpq.br/5116561408505726; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
This 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.