Abstract-We formulate the resource and power assignment problem of maximizing the spectral efficiency of a wireless system subject to user satisfaction constraints in the multiservice scenario. We show that although this optimization problem is nonlinear, it can be converted to an integer linear program. In this way, standard techniques can be used to obtain the optimal solution. Motivated by the high computational complexity of the optimal solution, we propose a fast suboptimal algorithm. Simulation results show that our proposal achieves near-optimal performance in low and medium loads with a much lower computational complexity compared with the algorithm used to obtain the optimal solution. Therefore, our proposed algorithm achieves a good tradeoff between performance and computational complexity. We also show that the addition of adaptive power allocation renders significant performance gains in the considered scenario.Index Terms-Multiservice, quality of service (QoS), rate maximization, resource and power assignment.
Resumo-Motivado pela crescente demanda por maior eficiência no uso dos recursos energéticos em rede móveis, neste trabalho, estudamos o impacto de métricas de Eficiência Energética (EE) no problema de satisfação de Quality of Service (QoS) apresentado em [6] que originalmente teve como objetivo maximizar a eficiência espectral. Formulamos dois problemas: Problema de Minimização da Potência (PMP) e Problema de Maximização da Eficiência Energética (PMEE) que em sua forma original são não lineares. Após algumas manipulações algébricas e inserção de novas variáveis e restrições nos problemas de otimização convertemos os problemas PMP e PMEE para Integer Linear Program (ILP) e Mixed integer linear programming (MILP), respectivamente. Através de simulações computacionais, estudamos o impacto dessas novas métricas de EE no problema em [6]. Verificamos que a solução PMEE apresenta-se como o melhor compromisso em relação a taxa de dados transmitida e economia de potência quando comparada a solução original em [6] e PMP.
This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three partsclustering, grouping, and scheduling -and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.
Resumo-Neste artigo revisaremos o problema de alocação de recursos de rádio para maximizar a taxa de dados de transmissão total sujeita a garantias de satisfação de Qualidade de Serviço ou Quality of Service (QoS). Este problema foi anteriormente estudado na perspectiva da alocação de recursos de frequência (apenas) considerando alocação de potência igualitária. No presente estudo, além de estudar a alocação de recursos de frequência, estudaremos também este problema assumindo alocação adaptativa de potência de transmissão. Como uma das contribuições deste trabalho, temos a elaboração do problema na forma de um problema de otimização não linear inteiro que depois,é simplificado para um problema inteiro e linear após considerarmos algumas suposições razoáveis. Através da obtenção da respostaótima do problema por meio de simulações computacionais, fazemos uma análise dos benefícios em desempenho que podem ser obtidos através do uso de alocação adaptativa de potência para garantias de QoS.
In this paper, we formulate and solve two Energy Efficiency (EE) problems, namely the Power Minimization Problem (PMP) and the Maximization of Energy Efficiency Problem (MEEP), for a wireless system using power and frequency resource allocation considering Quality of Service (QoS) requirements and multiple services. Despite those problems are nonlinear, they can be converted into Integer Linear Problems (ILPs). Therefore, the optimal solution for both PMP and MEEP can be obtained by well-known methods. Additionally, we propose two fast suboptimal algorithms as to avoid the high computational complexity of obtaining optimal solution for MEEP. Our results show that the MEEP has a better trade-off between transmitted data rate and power saving than the PMP solution. Moreover, the suboptimal algorithms present good performance compared to the optimal solution for moderated loads but with a much lower computational complexity, thus achieving a remarkable trade-off between performance and computational complexity.
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