Integration of AI and Deterministic Models for Accurate Path Loss Prediction at 5.8 GHz

Abstract

This paper evaluates classical deterministic path loss models and an Artificial Intelligence (AI)-based approach using empirical measurements collected at 5.8 GHz on the PUCRio campus. Free Space Path Loss (FSPL), Two-Ray models and their variants are compared against a neural network optimized with the ADAM algorithm. Results show that the AI model significantly outperforms deterministic approaches, achieving an R2 of 0.98 and an RMSE of 1.20 dB, demonstrating its effectiveness for accurate path loss prediction in complex environments.