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

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