Alocação de recursos de rádio para WPCN com noma baseado em rádio cognitivo sob sic imperfeito

• Ano de defesa: 2023
• Autor(a) principal: Melo, Jhenifer de Oliveira
• Orientador(a): Lima, Francisco Rafael Marques
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Link de acesso: http://repositorio.ufc.br/handle/riufc/76263
• topic: Otimização; NOMA; WPCN; SIC Imperfeito; QoS; Optimization; NOMA; WPCN; Imperfect SIC; QoS

This master’s thesis studies radio resource allocation strategies in a WPCN (Wireless Powered Communication Network) system using NOMA (Non-Orthogonal Multiple Access) inspired on cognitive radio, that is, CR-NOMA (Cognitive Radio Inspired NOMA). SIC (Successive Interference Cancellation) is used to decode user signals at the access point. For a more realistic system model, the imperfect SIC is considered in the study. Furthermore, there is a high-priority user, which is sensitive to information transmission delay, while the rest of the users have secondary priority but have a higher data transfer rate requirement. Optimization problems were formulated to improve system performance in terms of increasing data transfer rates and reducing interruption probabilities. The first optimization problem consists of maximizing the data transfer rate of secondary users, respecting the QoS (Quality of Service) criterion of the priority user. The second optimization problem is similar to the first one, but includes the possibility of power control. The third optimization problem incorporates features that are not present in the previous scenarios, such as the use of multiple subcarriers, and consists of maximizing the minimum data transfer rate of secondary users, which sets up a fairness maximization problem in the system. In this work, heuristics are proposed for the presented optimization problems. Computer simulations were performed to compare the performance of each solution. Finally, from the simulation results, it is possible to see that the proposed heuristics achieve results close to the respective optimal solutions, with the benefit of reduced computational cost.