QoS-constrained radio resource allocation on OFDMA cooperative networks and on energy-harvesting-aided massive MIMO systems

• Ano de defesa: 2019
• Autor(a) principal: Saraiva, Juno Vitorino
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Alocação de recursos de rádio; Qualidade de serviço; Redes cooperativas; Múltiplos relays; Eficiência energética; Justiça max-min; Teoria da programação fracionária e programação fracionária generalizada; MIMO massivo; Colheita de energia; Radio resource allocation; Quality of service; Cooperative networks; Multiple relays; Energy efficiency; Max-min fairness; Theory of fractional programming and generalized fractional programming; Massive MIMO; Energy harvesting; CNPQ::ENGENHARIAS
• Link de acesso: http://repositorio.ufc.br/handle/riufc/78407

In this master’s thesis, we first study radio resource allocation (RRA) for cooperative networks with multiple relays and destination nodes employing orthogonal frequency-division multiple access (OFDMA). RRA in our scenario includes relay selection, subcarrier pairing, and assignment, as well as transmit power allocation. Specifically, we analyze the impact of quality of service (QoS) when maximizing energy efficiency (EE). Three different problems are addressed in the first part of this work: total EE maximization, total power minimization, and minimum individual EE maximization. The last problem ensures fairness in the system regarding EE. In all three problems, we assume QoS constraints at the destination nodes. Although some of these problems are fractional and non-linear, we provide optimal solutions using iterative algorithms based on the theory of fractional programming and generalized fractional programming. Furthermore, we present and demonstrate an interesting property that exploits the use of the decode and forward (DF) protocol in the relay, and we show how it can be applied in the three problems discussed to simplify them. As a result, we can significantly reduce the number of variables and constraints in these problems, thereby reducing their computational complexity. Finally, through simulation results, we evaluate the performance of the proposed solutions in terms of total EE, EE fairness, and QoS. Part of this master’s thesis is dedicated to investigating transmit power allocation in an energy harvesting (EH)-aided distributed massive multiple input multiple output (MIMO) system. This distributed massive MIMO system involves a random distribution of a large number of singleantenna hybrid energy access points (H-APs) that simultaneously serve a much smaller number of single-antenna users over the same time/frequency resources. Additionally, we consider that each H-AP is powered by both an independent EH source and the electrical grid. The use of the electrical grid compensates for the intermittency and randomness of EH sources and allows for the provision of QoS guarantees. In offline scenarios, where prior knowledge of the EH profile is assumed (non-causal), we specifically investigate the max-min fairness problem by maximizing the minimum system signal-to-interference-plus-noise ratio (SINR) while fulfilling QoS requirements. We also model a problem constraint that allows the system operator to control the amount of energy consumed from the grid and renewable sources. Given that the formulated problem has a fractional framework, we guarantee its optimal solution by re-employing the theory of generalized fractional programming. However, we also provide an alternative approach to solve this problem optimally. Through numerical results, we show that in the simulated scenario, the alternative solution presents a performance loss of only 10−1% compared to the optimal solution when configured for 10 iterations. Moreover, it also accelerates the convergence of the generalized Dinkelbach algorithm and offers an interesting trade-off between energy consumption and performance loss relative to the optimal solution. Lastly, we discuss the impact of the problem variables on system performance.