NB-IoT non-terrestrial network path loss estimation with precise 3D modeling of urban environment

This study presents a novel simulation framework for Non-Terrestrial Networks (NTNs) that incorporates a realistic 3D ground model, including buildings and detailed environmental features, to enhance the accuracy of channel propagation modeling. Traditional approaches often rely on simplified terrain classifications, limiting their ability to capture the complexities of urban environments. To address this limitation, we integrate advanced ray-tracing techniques using Wireless InSite, MATLAB-based satellite constellation modeling, and high-resolution 3D urban mapping via Blender. This comprehensive framework allows for precise evaluations of NTN performance, including line-of-sight (LoS) and non-line-of-sight (N-LoS) conditions, by analyzing signal propagation characteristics with unprecedented granularity. The results demonstrate that detailed environmental modeling significantly impacts satellite visibility, signal attenuation, and multipath effects, highlighting the necessity of incorporating realistic urban structures in NTN simulations. The framework supports large-scale data analytics, enabling the application of machine learning techniques for network optimization, adaptive resource allocation, and enhanced connectivity planning. By bridging the gap between theoretical models and practical deployment scenarios, this work provides a powerful tool for advancing NTN research and improving global connectivity solutions.