Nested Tensor-Based Integrated Sensing and Communication in RIS-Assisted THz MIMO Systems

In this paper, we propose a nested tensor-based algorithm for integrated sensing and communication (ISAC) in reconfigurable intelligent surface (RIS)-assisted downlink terahertz (THz) multiple-input multiple-output (MIMO) systems. By exploiting the multi-group Khatri-Rao space-time (KRST) coding scheme at the base station (BS) and the structure of RIS phase shifts, we formulate the received signal at the vehicle terminal (VT) as a nested tensor that is composed of multiple outer parallel factor (PARAFAC) tensors and an inner PARATUCK tensor. With the outer PARAFAC tensors, a low-complexity least squares Khatri-Rao factorization (LS-KRF) algorithm is developed for joint channel estimation and symbol detection. Then, with the inner PARATUCK tensor, an optimized five-linear alternating least squares (OF-ALS) algorithm is designed to precisely estimate the sensing parameters such as angles of departure (AoDs), angles of arrival (AoAs), and time delays for VT and scatterer positioning. The proposed algorithm can be effectively applied to the scenario where the number of paths is unknown. Simulation results show that the proposed algorithm has more relaxed parameter conditions and provides superior ISAC performance compared with the existing state-of-the-art algorithms.