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Path Loss Prediction Method Merged Conventional Models Effectively in Machine Learning for Mobile Communications
IEICE TRANSACTIONS on Communications
Publication Date: 2022/06/01
Online ISSN: 1745-1345
Type of Manuscript: Special Section PAPER (Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2020)
path loss prediction, propagation model, machine learning, fully connected neural network, merged conventional models,
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Basic characteristics for relating design and base station layout design in land mobile communications are provided through a propagation model for path loss prediction. Owing to the rapid annual increase in traffic data, the number of base stations has increased accordingly. Therefore, propagation models for various scenarios and frequency bands are necessitated. To solve problems optimization and creation methods using the propagation model, a path loss prediction method that merges multiple models in machine learning is proposed herein. The method is discussed based on measurement values from Kitakyushu-shi. In machine learning, the selection of input parameters and suppression of overlearning are important for achieving highly accurate predictions. Therefore, the acquisition of conventional models based on the propagation environment and the use of input parameters of high importance are proposed. The prediction accuracy for Kitakyushu-shi using the proposed method indicates a root mean square error (RMSE) of 3.68dB. In addition, predictions are performed in Narashino-shi to confirm the effectiveness of the method in other urban scenarios. Results confirm the effectiveness of the proposed method for the urban scenario in Narashino-shi, and an RMSE of 4.39dB is obtained for the accuracy.