Prediction of Path Loss in Real-World Environments: Comparison of ITU-R P.1411 Models and Deep Learning

Accurate path loss estimation is essential for the design and optimization of wireless networks, especially in urban environments where propagation conditions can be extremely variable. This study compares traditional models such as the Free Space Path Loss (FSPL) model, ITU-R P.1411 model, and ITU-R P.1411 model with obstacles with an advanced machine learning approach using the ADAM algorithmEmpirical data were collected on the campus of the Pontifical Catholic University of Rio de Janeiro (PUCRio) at a frequency of 5.8 GHz. The comparison between the methods revealed that the machine learning approach offers greater accuracy, surpassing traditional deterministic models in terms of path loss estimation accuracy .