Minimizing path loss prediction error using k-means clustering and fuzzy logic

This research proposes an algorithmic scheme based on k-means clustering and fuzzy logic to minimize pathloss prediction error. The proposed k-means fuzzy scheme concurrently utilizes the area topographical variability andmultiple path loss prediction models to mitigate the prediction error inherent in the independent use of a conventionalpath loss model. Vegetation density, manmade structures, and transmission-receiver distances are the fuzzy inputsand the conventional path loss models the output: the free space loss, Walfisch–Ikegami, HATA, ECC-33, StanfordUniversity Interim, and ERICSSON models. The experimental results show that the path loss prediction error of thek-mean fuzzy scheme is only 2.67% compared to the the drive-test measurement, and this is the lowest relative to thatof the conventional models. The k-mean fuzzy scheme offers a novel means to approximate path loss in localities withdiverse topographical features and also efficiently mitigates the prediction error inherent in the independent use of theconventional prediction models.

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