Evaluating Path Loss Models in Heterogeneous Environments: A Land Cover Segmentation Approach
Resumo
Path loss models are advantageous for analyzing complex environments and offer valuable network planning and optimization insights. Given the numerous challenges, predictive models incorporating environmental characteristics are essential for more accurate results. This study compares the accuracy of traditional predictive models, such as Free Space and Log-Distance, against the Log-Distance Multi-Exponent model using signals transmitted by LoRa technology. The ability of the latter model to classify the terrain coverage into distinct categories makes it particularly relevant in complex environments. Initially, the laboratory performed device calibration, followed by measurement campaigns in environments characterized by obstructed and unobstructed paths. The results indicate that segmenting the signal path based on terrain coverage is a practical approach that provides greater precision in estimating signal loss. Specifically, a path-segmented predictive model demonstrated a mean absolute error of 2 dB for environments with uniform land cover, outperforming the Free Space Model by 65%. Similarly, for mixed coverage, the Mean Absolute Error was 2.96 dB, representing a significant improvement, exceeding the Free Space Model by 75.6% and the Log-Distance model by 32.1%. These findings validate the effectiveness of the Log-Distance Multi-Exponent model, contributing to enhanced network planning efficiency.
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