Optimization methods applied to nonlinear signal interference models
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In wireless mesh networks, it is important to establish the transmission capacity of the links, taking into account the presence of noise that interferes with the transmission and consequently degrades the signal sent from one device to another. This signal degradation is calculated from interference models, such as SNR (Signal-to-Noise Ratio), SIR (Signal-to-Interference Ratio) and SINR (Signal-to-Interference-plus-Noise Ratio). In these models, the link capacity is calculated according to a decreasing of power levels, depending on the noise or interference present, which needs to be adjusted to acceptable levels, in order to avoid committing the signal emission besides not causing health damage to people close to the device. Different models can be used to estimate the noise present in an environment. In wireless transmission, however, it is possible to calculate the noise by means of nonlinear equations, which are able to estimate the interference levels present in the network links. From these elements, it is possible to maximize the capacity of the network links, using models of nonlinear programming. As these models are difficult to be solved analytically, this work compares the results of different nonlinear programming models, based on the main interference models, with the results obtained by a classical approach for solving nonlinear models: The simulated annealing metaheuristic. In this paper, it will analyze the behavior of the heuristic algorithm, regarding the quality of the solution obtained and the processing time, as the network size increases.