User Sensing and Localization With Reconfigurable Intelligent Surface for Terahertz Massive MIMO Systems.

With the evolution of the sixth generation (6G) mobile communication technology, the terahertz (THz) spectrum has attracted much attention in wireless communication applications due to its high bandwidth and low signal transmission delay. However, the introduction of the THz spectrum leads to higher path transmission losses and complex signal attenuation. This makes user sensing and localization in THz massive multiple-input multiple-output (MIMO) systems more challenging. In this paper, we investigate the user sensing and localization problem in THz massive MIMO systems with the assistance of reconfigurable intelligent surfaces (RIS). Firstly, the received signal is modeled as a tensor, and a parallel factor (PARAFAC) method is proposed. The minimum description length (MDL) is then utilized to detect the number of scattering paths in the channel. On this basis, the alternating least squares (ALS) algorithm is employed to estimate the factor matrices, followed by the utilization of a straightforward correlation-based approach to obtain channel parameter information. Finally, the positions of users and scattering points are estimated based on the geometric relationship between channel parameters and location coordinates. The simulation results have verified the effectiveness of the proposed scheme compared to the existing competitive algorithms, and indicate that the proposed scheme exhibits superior parameter estimation performance and can achieve localization accuracy at the decimeter level.