Pathloss Prediction

Pathloss prediction aims to accurately estimate signal attenuation in wireless communication environments, crucial for optimizing network design and performance. Current research focuses on developing efficient and accurate prediction models, employing machine learning techniques like gradient boosting (e.g., LightGBM, CatBoost), neural networks (including diffusion models and physics-informed networks), and leveraging data augmentation with simulation to overcome data scarcity. These advancements offer significant improvements in prediction speed and accuracy compared to traditional ray-tracing methods, impacting network planning, resource allocation, and the development of self-driving networks.