Channel estimation for RIS-assisted light rail train terahertz MIMO communications

The terahertz (THz) band, while offering immense bandwidth, presents significant challenges for 6G communication due to severe path loss and dynamic link blockages. This paper investigates the use of a Reconfigurable Intelligent Surface (RIS) to enhance signal propagation in THz MIMO systems for high-speed light rail train (LRT) scenarios in urban environments. By establishing a cascaded channel model, we employ a Bilinear Alternating Least Squares (BALS) algorithm to achieve high-accuracy, decoupled estimation of the Channel State Information (CSI). Numerical simulations demonstrate that the proposed algorithm achieves superior estimation accuracy compared to existing benchmark methods.