APPLYING MACHINE LEARNING ALGORITHMS FOR PATH LOSS PREDICTION OF MILLIMETER WAVES
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K-Nearest Neighbor; Linear Regression; Gradient Descent; 5G; Millimeter waves; Path loss models.Abstract
In this paper, we develop and apply Linear Regression and K-Nearest Neighbor algorithm to predict the path loss models of millimeter waves. The experimental data is obtained by using Wireless Insite software, the experiments in both line-of-sight and non-line-of-sight scenarios use a transmitter and receivers placed randomly at locations. The proposed method is applied to impove and adjust path loss models at 28 GHz and 38 GHz in Times City urban area and Nguyen Hue high school, Hanoi, Vietnam. The combination of these two algorithms to predict the path loss models of millimeter waves achieved the suitable results when compared with 3GPP and NYU Wireless path loss models, therefore improving the optimal results of path loss models.
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