In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based o...In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.展开更多
基金This work is carried out in the framework of the project supported by the Department of Science and Technology of Kien Giang,Vietnam.The authors would like to thank them for supporting this research。
文摘In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.
