Sensing-Based Channel Estimation for Extremely Large-Scale RIS-Assisted Millimeter-Wave Communication Systems

The concept of extremely large-scale reconfigurable intelligent surfaces (XL-RIS) holds great promise for enabling sixth-generation (6G) communications. However, the vast number of passive reflection coefficients and the transition from far-field to near-field electromagnetic radiation pose significant challenges for channel estimation, especially under tight pilot overhead constraints. To address these challenges, we propose a novel hybrid integrated sensing and communication architecture and a three-stage channel estimation scheme for XL-RIS-assisted millimeter wave communication systems. The proposed scheme leverages user position data, obtained through a sensing module, to accurately estimate near-field cascaded channels. First, we design an integrated base station architecture that combines a fully-digital sensing module with a hybrid communication module to achieve high-resolution distance and angle estimations using linear frequency modulation signals. Next, we introduce a distance-error-minimization based localization algorithm to effectively estimate user coordinates. To balance channel estimation performance and pilot overhead, we carefully select the appropriate number of position update iterations. Using these estimated coordinates, we calculate the channel fading coefficients for the near-field cascaded channels, facilitating accurate channel estimation. Simulation results validate the effectiveness of our proposed scheme, demonstrating reduced overhead while maintaining superior channel estimation performance.