A Novel Material Parameter Calibration Algorithm for 6G Ray-Tracing Channel Modeling

Ray-tracing (RT) emerges as a highly effective tool for channel modeling and radio wave characteristics analysis. However, the accuracy and reliability of RT simulations hinge on the precise calibration of input model parameters, particularly the electromagnetic parameters of materials. Conventional material parameter calibration methods rely on extensive RT simulation samples, which are both computationally intensive and time-consuming. To address these challenges, a multi-stage surrogate model-assisted material calibration (MS-SAMC) algorithm is proposed, which integrates received power calibration with delay power spectral density (Delay PSD) calibration. By embedding regression models into a multi-stage optimization framework and coupling this with a genetic algorithm (GA), the proposed algorithm is able to calibrate material parameters efficiently. The validating results based on channel measurement data demonstrate that the proposed algorithm significantly reduces computational complexity while maintaining high accuracy, thus accelerating the RT calibration process.