Dissertações em Engenharia Elétrica (Mestrado) - PPGEE/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2316

O Mestrado Acadêmico inicou-se em 1986 e pertence ao Programa de Pós-Graduação em Engenharia Elétrica (PPGEE) do Instituto de Tecnologia (ITEC) da Universidade Federal do Pará (UFPA).

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Navegando Dissertações em Engenharia Elétrica (Mestrado) - PPGEE/ITEC por Assunto "5G Mobile network"

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    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.
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    Alocação de dois níveis para uma arquitetura h-cran baseada em offloading
    (Universidade Federal do Pará, 2019-01-24) GONÇALVES, Mariane de Paula da Silva; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734
    The accelerated data and apps growth represents significant challenges to the next generation of mobile networks. Amongst them, it is highlighted the necessity for a co-existence of new and old patterns during the transition of architectures. Thus, this paper has investigated solutions for offloading into a hybrid architecture, also known as H-CRAN (Heterogeneous Cloud Radio Access Network Architecture), that centralizes processing and searches a better use of the network resources. The strategy of optimization was analyzed through the evolutive algorithm PSO (Particle Swarm Optimization), in order to find a suboptimal solution to the allocation of two levels (TLA) in the H-CRAN architecture and another one based on FIFO (First In, First Out), for benchmarking purposes. SNR (Noise Interference Signal) average, Maximum Bit Rate, the number of users with or without connections and number of connections in RRHs and macro were used as performance measurements. Through the results, it was noticed an improvement of approximately 60% in the Maximum Bit Rate when compared to the traditional approach, enabling a better service to the users.
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    Análise de desempenho de redes de acesso G.mgfast e fronthaul 5G baseado em cabos coaxiais
    (Universidade Federal do Pará, 2019-05-27) FREITAS, Marx Miguel Miranda de; NUNES, Diogo Lobato Acatauassú; http://lattes.cnpq.br/1972007941497086; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
    This work explores two possibilities of harnessing mature cabling technologies used in broadband networks of 4th generation systems in emerging next generation applications. Specifically, two proposals for using the cabling structure of Hybrid Fiber Coax and SAT TV (Satellite Television) systems are evaluated. The first one, as support in 5G network analog transport networks (fronthaul). The second evaluates the use of coaxial cables in access networks G.mgfast (Multi Gigabit G.fast). In the firs one, It is shown the relationships between the data rates and the number of antennas reached by the coaxial cable RG06, under a fixed power level and a target signal noise condition, considering different distances and two configurations of radio signals. It is shown that in the 5G analogue fronthaul analyzed, rates higher than 40 Gbps can be obtained in a RG06 coaxial cable, giving support to 140 antennas, meeting 3GPP transmission criteria. The second solution proposes a process to reduce power consumption in the network, by adapting the transmission power in the coaxial network, with higher bit load in the initial frequencies of the spectrum. Links with RG59, RG06 and RG11 coaxial cables are analyzed, considering rates ranging from 5 Gbps to 10 Gbps and two types of bit loading algorithms. It is shown that with these procedures the power saving obtained in single link with 100 m coaxial cable can be used to power another 28 cables of 50 m. On the other hand, it is shown that the power reduction is not relevant, from the point of view of redistribution, in cables whose length is less than or equal to 25 m.
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