A Material-Aware Channel Model for Efficient CKM Generation via Environment Reconstruction

Abstract

Channel knowledge map (CKM) is a promising technology for environment-aware wireless communication, sensing, and localization in 6G networks. Accurate CKM generation requires precise reconstruction of the environment, including 3D geometries and scatterer materials, typically from multi-modal sensory observations such as LiDAR point clouds and sparse channel measurements. While the former is relatively easy to acquire, materials remain difficult to obtain directly from sparse channel measurements due to the lack of an explicit channel model linking them. To fill this gap, this paper proposes a material-aware channel model that explicitly characterizes the influence of scatterer materials on the wireless channel. Based on this model, an iterative gradient descent based material reconstruction algorithm is proposed. Full wave simulation results validate the developed model and the proposed algorithm, demonstrating their potentials for efficient CKM generation via environment reconstruction.