Double-RIS Versus Single-RIS Aided Systems: Tensor-Based MIMO Channel Estimation and Design Perspectives

TL;DR

This paper compares double-RIS and single-RIS aided MIMO systems, proposing a tensor-based channel estimation method for D-RIS, demonstrating advantages in training efficiency and estimation accuracy for future wireless networks.

Contribution

It introduces a tensor-based channel estimation approach for double-RIS systems and analyzes their benefits over single-RIS systems in terms of training overhead and accuracy.

Findings

D-RIS systems can reduce training overhead compared to S-RIS.

Tensor-based estimation effectively separates cascaded MIMO channels.

D-RIS enhances coverage, capacity, and efficiency in wireless networks.

Abstract

Reconfigurable intelligent surfaces (RISs) have been proposed recently as new technology to tune the wireless propagation channels in real-time. However, most of the current works assume single-RIS (S-RIS)-aided systems, which can be limited in some application scenarios where a transmitter might need a multi-RIS-aided channel to communicate with a receiver. In this paper, we consider a double-RIS (D-RIS)-aided MIMO system and propose an alternating least-squared-based channel estimation method by exploiting the Tucker2 tensor structure of the received signals. Using the proposed method, the cascaded MIMO channel parts can be estimated separately, up to trivial scaling factors. Compared with the S-RIS systems, we show that if the RIS elements of a S-RIS system are distributed carefully between the two RISs in a D-RIS system, the training overhead can be reduced and the estimation accuracy can also be increased. Therefore, D-RIS systems can be seen as an appealing approach to further increase the coverage, capacity, and efficiency of future wireless networks compared to S-RIS systems.