Navegando por Assunto "MMWave"

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Acesso aberto (Open Access)
Análise do QoE para streaming de vídeo em redes aéreas 5G mmWave de conectividade dupla
(Universidade Federal do Pará, 2022-02-18) PEREIRA, Ronilson Williame da Silva; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497
The evolution in mobile telecommunications has allowed the emergence of new network formats aimed at meeting the growing demand for wireless data, mainly fueled by more viewing of video content. The scarce spectrum available in current cellular networks does not seem to be able to meet this explosion of wireless data, motivating a shift to explore new frequency bands. Currently, one of the major problems of current mobile networks is congestion in denser areas such as concerts, sporting events, and festivals (temporary events), as we have a greater concentration of users, consequently increasing the volume of data traffic. Millimeter-wave (mmWave) communication, frequency bands between 30 and 300 GHz emerges as an essential part for the next generation of fifth-generation cellular networks (5G), as they adopt higher carrier frequencies offering high bandwidth and lower latency. However, system performance degrades due to high propagation loss and the sensitivity of the links to obstacles. To mitigate such factors, this dissertation proposes a dual connectivity 5G mmWave architecture using LTE (Long Term Evolution) and the use of UAVs (Unmanned Aerial Vehicles) as airbase stations to provide connectivity guarantees and quality of experience. in video streaming, one scenario where the proposal might be a suitable solution is to provide coverage for temporary events. Through simulations, an analysis of the Quality of Experience (QoE) was presented about the transmission of 4K videos in order to evaluate the transmission and quality of videos perceived by users. The simulated results show the efficiency of the system for multimedia applications using videos, improving the QoE of wireless users by 43%.
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.