Alocação de recursos e posicionamento de funções virtualizadas em redes de acesso por rádio desagregadas

• Autor(a) principal: Almeida, Gabriel Matheus Faria de
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/13055
• topic: Redes de acesso por rádio virtualizadas; Posicionamento de funções virtualizadas; Divisão funcional da pilha de rádio; Alocação de recursos; Otimização; Aprendizado por reforço profundo; Algoritmo genético; Virtualized radio access network; Virtualized function placement; Radio stack functional splits; Resource allocation; Optimization; Deep reinforcement learning; Genetic algorithm; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Jointly choosing a functional split of the protocol stack and placement of network functions in a virtualized RAN is critical to efficiently using the access network resources. This problem represents a current research topic in 5G and Post-5G networks, which involves the challenge of simultaneously choosing the placement of virtualized functions, the routes for traffic and the management of available computing resources. In this work, we present three approaches to solve this problem considering the planning scenario and two approaches considering the network operation scenario. The first result is a Mixed Integer Linear Programming (MILP) model, considering a generic set of processing nodes and multipath routing. The second approach uses artificial intelligence and machine learning concepts, in which we formulate a deep reinforcement learning agent. The third approach used is based on search meta-heuristics, through a genetic algorithm. The last two approaches are Markov Decision Process (MDP) formulations that consider dynamic demand on radio units. In all formulations, the objective is to maximize the network function’s centralization while minimizing positioning cost. Analysis of the solutions and comparison of their results show that exact approaches such as MILP naturally provide the best solution. However, in terms of efficiency, the genetic algorithm has the best search time, finding a high quality solution in a few seconds. The deep reinforcement learning agent presents a high convergence, finding high quality solutions for the problem and showing problem generalization capacity with different topologies. Finally, the formulations considering the network operation scenario with dynamic demand are highly complex due to the size of the action space