Body Shadowing and Furniture Effects for Accuracy Improvement of Indoor Wave Propagation Models

Generally empirical models are developed on the basis of measurements achieved in empty buildings. But in practice human bodies and furniture presence induce a considerable fluctuation leading to huge differences between predictions and real measurements. In this paper a new indoor large scale path loss empirical model is presented. The model design, in addition to the considered phenomena in conventional empirical formulation, integrates additional suggestions recommended by electromagnetic techniques such as body shadowing and furniture effects. To achieve this work, a large number of experimental measurements have been carried on and saved in consequently voluminous databases. Their management and exploitation have considered data mining and especially neural networks to perform the new model called neural model. To prove model enhancement and accuracy we compare the “neural model” predictions with measurements. Obtained results show that the mean error is close to zero, the standard deviation is about 4.47 dB with a correlation factor of 97%.

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