Hybrid - Field Full-Dimensional Channel Estimation for Reconfigurable Intelligent Surfaces with Extremely-Large Aperture

The Extremely-large Aperture Reconfigurable In-telligent Surface (RIS) stands out as a promising technology for future 6G communications. However, existing far-field or near-field channel models struggle to adapt effectively to channel estimation in the context of Extremely-large Aperture RIS-assisted wireless communication under a hybrid field. To address this challenge, this paper introduces an efficient hybrid-field channel estimation scheme tailored for Extremely-large Aperture RIS-assisted wireless communication. In this scheme, we initially extend the one-dimensional polar coordinate dictionary to a full-dimensional spherical coordinate dictionary to achieve a more uniform distribution of grid points in the spherical coordinate-domain. Subsequently, we propose a hybrid passive/active RIS architecture, utilizing a limited number of Radio Frequency (RF) chains to acquire channel observations. Finally, we introduce a hybrid-field channel estimation scheme designed to estimate both far-field and near-field components. Simulation results demonstrate that the proposed scheme outperforms purely far-field or near-field schemes.