A Stacking Ensemble Technique to Predict Signal Path Loss via 3D GIS

The recent developments in cellular communication technologies, especially the emergence of 6G, have increased the need for accurate and reliable signal path loss prediction models. The accuracy of predictions is reduced because traditional empirical approaches often fail to take into account the complex relationships between radio signals and the three-dimensional urban environment. Therefore, integrating advanced machine learning algorithms with diverse geographic data offers a promising direction for improving prediction performance and supporting next-generation network planning. This paper introduces an integrated methodology that combines Geographic Information Systems (GIS) with stacking ensemble machine learning models to enhance signal path loss prediction. The study made several key contributions, which are outlined below: (I) A GIS-based framework has been developed to integrate the Digital Twin (DT) of the study area with machine learning-based path loss models, incorporating 3D geographic data such as terrain height and building elevations. (II) The study assesses binary hybrid algorithms by examining three ensemble learning models (Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and CatBoost). The fusion of 3D spatial data with ensemble learning algorithms has led to notable advancements in mobile network design, improving the accuracy of signal attenuation predictions. (III) Lastly, the paper emphasizes the potential of GIS-assisted machine learning techniques for future network deployments, including applications in DT, 6G, and beyond.