OMP Channel Estimation for RIS-Assisted MIMO Systems in 6G Terahertz Communications

The advent of the sixth generation (6G) of wireless communication networks promises to revolutionize connectivity by exploiting the vast bandwidth available in the Terahertz (THz) frequency spectrum. However, the propagation characteristics of THz waves, particularly severe path loss and molecular absorption, necessitate the deployment of massive Multiple-Input Multiple-Output (MIMO) architectures and Reconfigurable Intelligent Surfaces (RIS) to ensure reliable link quality and extensive coverage. A critical challenge in realizing the potential of RIS-assisted THz MIMO systems is the acquisition of accurate Channel State Information (CSI), which is complicated by the high dimensionality of the channel and the passive nature of RIS elements. This paper presents a comprehensive study on the application of Orthogonal Matching Pursuit (OMP) algorithms for channel estimation in this specific domain. We exploit the inherent sparsity of THz channels in the angular domain to formulate the channel estimation problem as a sparse signal recovery task within the framework of compressed sensing. We provide a detailed theoretical analysis of the OMP algorithm's efficacy in resolving the cascade channel coefficients with reduced pilot overhead compared to conventional least squares estimation. Furthermore, we investigate the impact of dictionary design and grid mismatch on estimation accuracy. Extensive simulation results demonstrate that the proposed OMP-based approach significantly outperforms traditional methods in terms of normalized mean square error and spectral efficiency, offering a computationally efficient solution for ultra-high-speed 6G networks.