Machine Learning-Based Path Loss Modeling with Simplified Features

arXiv:2405.10006v1 Announce Type: new
Abstract: Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. …

abstract accuracy arxiv cs.lg cs.ni cs.sy demand deployments eess.sy environment environmental features information knowledge loss machine machine learning modeling novel path planning predictions propagation simplified spectrum tool type wireless