Radio Environment Electrical Parameter Calibration for Ray Tracing Channel Simulation Using Neural Networks

Integration of Ray Tracing (RT) and Artificial Intelligence (AI) represents a transformative approach that has received significant attention in 6G communications. The electrical parameters of materials in environment are crucial for the accuracy and reliability of RT channel simulations, and these material characteristics require precise calibration based on measurements. Conventional electrical parameters calibration methods are based on global optimization algorithms that iterate over pre-configured materials. These methods are computationally intensive and struggle to provide a sufficiently detailed description of the electrical characteristics of environment. In this paper, we propose a neural network-based method for electrical parameters calibration. The results demonstrate that the simulated channel shows high accuracy after calibration with a root mean square error less than 2.5 dB.