🤖 AI Summary
In RIS-assisted terahertz (THz) communications, channel state information (CSI) acquisition is severely hindered by passive reflecting surfaces and hybrid beamforming, while beam squint further degrades estimation accuracy. To address this, we propose a low-complexity two-stage CSI estimation method. First, sparse subcarrier sampling is innovatively employed to extract angle parameters at both transmitter and receiver. Second, spatial-direction mapping and angle-independent path gain modeling enable efficient full-bandwidth CSI reconstruction. The method deeply integrates sparse sampling, angular parameterization, spatial-domain matching, and rigorous error analysis—explicitly tailored to THz-specific beam squint characteristics. Simulation and theoretical analysis demonstrate that the proposed scheme achieves significantly lower normalized mean square error than state-of-the-art approaches, with computational complexity reduced by over an order of magnitude. It thus offers both high estimation accuracy and real-time feasibility for practical THz-RIS systems.
📝 Abstract
Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers, then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining subcarriers by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.