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Title	Accurate Fifth Generation Mobile Network Coverage Prediction In Smart Cities With Machine Learning
ID_Doc	6021
Authors	Yang T.J.; Nordin R.; Abdullah N.F.B.; Fauzi M.F.A.; Hliang N.W.
Year	2024
Published	2024 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024
DOI	http://dx.doi.org/10.1109/APACE62360.2024.10877345
Abstract	This study uses machine learning in smart city environments to address challenges in predicting the Received Signal Reference Power (RSRP) performance in Fifth Generation (5G) networks. We applied and validated several established Machine Learning (ML) models to train extensive 5G drive test datasets representing urban and sub-urban environments in Malaysia. Drive tests were conducted around Putrajaya (urban) and UKM (sub-urban) areas to collect 5G Non-Standalone (NSA) datasets to develop a Random Forest-based machine learning model by integrating both 4G LTE and 5G network datasets and design a machine learning-based online coverage estimating application for 5G networks. This study is the second improved version of the Machine Learning-based Online Coverage Estimator (MLOEv2), which was developed with a MATLAB-based graphical user interface facilitating online RSRP predictions for teaching and learning purposes. This study aids in understanding 5G coverage in Malaysia, laying a foundation for practical mobile network deployment. © 2024 IEEE.
Author Keywords	5G Network; Coverage Estimator; Machine Learning; Random Forest; Received Signal Reference Power

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