Soluções baseadas em aprendizado de máquina para alocação de recursos em redes sem fio de próxima geração
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2024-05-06
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Universidade Federal de Goiás
Resumo
5G and beyond networks have been designed to support challenging services. Despite
important advances already introduced, resource allocation and management methods
remain critical tasks in this context. Although resource allocation methods based on
exact optimization have a long history in wireless networks, several aspects involved
in this evolution require approaches that can overcome the existing limitations. Recent
research has shown the potential of AI/ML-based resource allocation methods. In this
approach, resource allocation strategies can be built based on learning, in which the
complex relationships of these problems can be learned through the experience of agents
interacting with the environment. In this context, this thesis aimed to investigate AI/MLbased approaches for the development of dynamic resource allocation and management
methods. Two relevant problems were considered, the rst one related to user scheduling
and the allocation of radio resources in multiband MIMO networks, and the second one
focused on the challenges of allocating radio, computational, and infrastructure resources
involved in the VNF placement problem in disaggregated vRAN. For the rst problem,
an agent based on DRL was proposed. For the second problem, two approaches were
proposed, the rst one being based on an exact optimization method for dening the VNF
placement solution, and the second one based on a DRL agent for the same purpose.
Moreover, components adhering to the O-RAN architecture were proposed, creating the
necessary control for monitoring and dening new placement solutions dynamically,
considering aspects of cell coverage and demand. Simulations demonstrated the feasibility
of the proposals, with important improvements observed in different metrics.
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LOPES, V. H. L. Soluções Baseadas em Aprendizado de Máquina para Alocação de Recursos em Redes Sem Fio de Próxima Geração. 2024. 175 f. Tese (Doutorado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2024.