Efficient and Accurate Surrogate-Assisted Electromagnetic Parameter Calibration for 6G Digital Twin Channels
TL;DR
A Surrogate-assisted Grey Wolf Optimizer with Individual Memory (SGWO-IM) algorithm that simultaneously improves computational efficiency and calibration accuracy and effectively balances efficiency and accuracy for electromagnetic environment reconstruction is proposed.
Abstract
High-fidelity ray-tracing (RT) channel reconstruction is a fundamental step toward building digital twins for the era of 6G wireless communications. However, precise calibration of complex electromagnetic material parameters remains a dual challenge characterized by massive computational overhead and strict accuracy requirements. To overcome this bottleneck, we propose a Surrogate-assisted Grey Wolf Optimizer with Individual Memory (SGWO-IM) algorithm that simultaneously improves computational efficiency and calibration accuracy. In terms of computational efficiency, an online surrogate model is seamlessly embedded into the evaluation workflow for candidate pre-screening, substantially reducing the reliance on highly time-consuming real RT simulations. Regarding calibration accuracy, adaptive convergence and individual memory strategies are incorporated to optimize the global parameter search path, effectively enhancing the consistency between the reconstructed channel and measured data. Validated against measured channel data from a high-density urban scenario, the proposed algorithm requires only 225 real RT simulation calls compared to the 600 calls needed by the standard Grey Wolf Optimizer (GWO), cutting computational overhead by 62.5%. Concurrently, the final Root Mean Square Error (RMSE) is substantially reduced from the 3.65 dB of GWO to 2.97 dB. The results demonstrate that the SGWO-IM algorithm achieves significant advancements in both efficiency and precision, providing a solution that effectively balances efficiency and accuracy for electromagnetic environment reconstruction.