4-D Structured Tensor Decomposition-Based Channel Estimation for RIS-Aided mmWave MIMO-NOMA System in Internet of Vehicles

Severe Doppler shift and the obstruction of the Line-of-Sight (LoS) path significantly decrease the communication performance. This article considers a downlink channel estimation problem for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) system in Internet of Vehicles (IoV) with high-mobility scenarios. By introducing the concept of aggregated slot and half slot, a 5G subframe partitioning scheme without changing the standard 5G frame structure is first proposed to facilitate the formulation of the received signal. Then, the received signal is modeled as a quadrilinear tensor, meeting with a canonical polyadic decomposition (CPD) form, which separates Angle of Arrival (AoA), Angle of Departure (AoD), time delay, and Doppler shift into four corresponding factor matrices and avoids parameters coupling. Subsequently, by leveraging the Vandermonde structure of the factor matrix and the low-rank property of the mmWave channel, we design a four-dimension (4-D) structured tensor decomposition-based method to decompose the tensor into four factor matrices in a closed-form solution, which can avoid initialization and iteration. Accordingly, the channel parameters can be extracted by a simple correlation-based estimator. After obtaining the channel parameters, we construct a least squares problem to obtain the channel path gain, which can avoid solving the scaling matrix. Finally, numerical experiments are conducted to confirm the effectiveness of the proposed algorithm, in which the Cramér-Rao bound (CRB) results for channel parameters are derived as the benchmark.