Comparative Analysis of ML Models for Power Prediction in Complex 3D Digital Twins Using Ray Tracing Environment

In this study, we present the development of a comprehensive ray tracing dataset tailored for versatile 3D digital twin environments that closely mimic real-life scenarios. For precise modeling in complicated environments, this dataset includes crucial factors like phase shift, propagation distance, path loss, propagation delay, and the number of ray contacts or reflections. Using this dataset, we forecast received power and other relevant variables using a variety of machine learning (ML) techniques. To improve the data's suitability for machine learning models, we do feature extraction and exploratory data analysis to find patterns in the data. By comparing the performance and error metrics across different ML approaches, our analysis provides a robust framework for network developers seeking optimal ML strategies for intricate environments featuring real-life channels and objects. The results provide important information for choosing appropriate machine learning models for improved precision and effectiveness in ray tracing applications, ultimately promoting AI-driven solutions in network development and digital twin settings.