.png){kind=icon}
.png){kind=icon}
Return
Tensor Train-Based Channel Estimation for RIS-Assisted mmWave MIMO Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/LWC.2026.3665781.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter, we consider a Reconfigurable intelligent surface (RIS)-assisted point to point cascade channel in millimeter wave multiple-input multiple-output orthogonal frequency division multiplexing (mmWave MIMO-OFDM) systems. Unlike most existing studies that adopt low-dimensional channel models, by exploiting the structure of the RIS-assisted MIMO channel across multiple dimensions, we model it in form of a low-rank higher-order CANDECOMP/PARAFAC (CP) tensor. Motivated by the inherent Vandermonde structure and higher-order low-rank characteristics of the tensor, we exploit Tensor-train (TT)-based processing to efficiently estimate the RIS-assisted channel. Numerical results confirm that the proposed method outperforms existing approaches in terms of both estimation accuracy and computational complexity.
Keywords:
Tensor train decomposition
Reconfigurable intelligent surface (RIS)
mmWave MIMO
channel estimation
Journal
I
IF:
5.5
Papers:
662
Citations:
0