Physics-Informed Neural Networks for Path Loss Estimation by Solving Electromagnetic Integral Equations

Accurately and efficiently modeling wireless channels, especially estimating path loss value, is crucial to wireless system design and performance optimization. This paper proposes a knowledge and data jointly driven path loss estimation method named PEFNet. PEFNet can predict the total electric field based on its mathematical relationship with the incident field by introducing the Volume Integration Equation into the loss function and scenario environmental information as input. Then, we let PEFNet learn in a supervised manner to compensate for residual error against measured data for refinement. We conduct extensive experiments on two publicly available path loss datasets, RadioMapSeer and RSRPSet, to exhibit the superiority of PEFNet over other state-of-the-art baselines. By comparing overall estimation performance, carrying out ablation study, testing estimation efficiency, and evaluating generalization ability, the results prove that PEFNet is an accurate and efficient method for estimating path loss.