This dissertation presents a methodology that aims to assist the planning of indoor wireless network systems, which require prior knowledge of the environments in which they will be deployed. Thus, accurate signal analysis is necessary by means of a statistical empirical approach, which takes into account some factors that influence the propagation of the indoor signal: architecture of the buildings; arrangement of furniture inside the compartments; numbers of walls and floors of various materials, and the spread of radio waves. The methodology adopted is based on measurements with a cross-layer approach, which demonstrates the impact of the physical layer in relation to the application layer, in order to predict the behavior of the Quality of Experience (QoE) metric, called Peak signal- to-noise ratio (PSNR), in 4K video streams on 802.11ac wireless networks in the indoor environment. In order to do so, measurements were performed, which demonstrate how the signal / video degrades in the studied environment. It is possible to model this degradation by means of a computational intelligence technique, called Artificial Neural Networks (RNA), in which input parameters are inserted as, for example, the distance from the transmitter to the receiver and the number of walls crossed in order to predict loss of propagation and loss of PSNR. In order to evaluate the predictive capacity of the proposed methods, the values of the Root Mean Sqare (RMS) errors between the measured and predicted data were obtained by the prediction methods loss of propagation and loss of PSNR, with respective values of 2.17 dB and 2.81 dB.
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