Alocação de recursos de rádio para maximização da eficiência espectral e eficiência energética em redes de multisserviços com restrições de QoS

Abstract

We formulate the frequency resource assignment and power assignment allocation of maximizing the spectral eficiency in 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 into 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 suboptimal algorithm with polynomial complexity in the worst case. 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. In this dissertation we also propose some energy eficiency problems to a wireless system using power and resource allocation subject to user satisfaction constraint. More specificaly, we formulate the PMEE, PMP and PMDTP problems. We show that although those optimizations problems are non linear, the PMEE, problem can be converted to an MILP problem while PMP and PMDTP are converted to ILP problems. In this way, standard techniques can be used to obtain the optimal solution to these problems. Motivated by the high computational complexity of the optimal solution, we propose a suboptimal algorithm with polynomial complexity in the worst case. Simulation results show that PMEE presents the best tradeoff transmited data rate and power economy when compared to the PMP and PMDTP solutions. We also 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.