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Reconfigurable intelligent surfaces (RISs) have been proposed recently as newtechnology to tune the wireless propagation channels in real-time. However,most of the current works assume single-RIS (S-RIS)-aided systems, which can belimited in some application scenarios where a transmitter might need amulti-RIS-aided channel to communicate with a receiver. In this paper, weconsider a double-RIS (D-RIS)-aided MIMO system and propose an alternatingleast-squared-based channel estimation method by exploiting the Tucker2 tensorstructure of the received signals. Using the proposed method, the cascaded MIMOchannel 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-RISsystem are distributed carefully between the two RISs in a D-RIS system, thetraining overhead can be reduced and the estimation accuracy can also beincreased. Therefore, D-RIS systems can be seen as an appealing approach tofurther increase the coverage, capacity, and efficiency of future wirelessnetworks compared to S-RIS systems.

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