Green-Learning Based Design of RIS-Assisted MIMO Systems Based on Implicit CSI

To attain diverse performance benchmarks for 6G systems, multi-input-multi-output (MIMO) systems assisted by reconfigurable intelligent surface (RIS) have been investigated intensively. Various deep learning (DL) based methods have been adopted to perform the non-convex optimization of such systems, which leads to significant enhancement in the spectral efficiency. However, the DL-based methods rely on the operations of neural network, which demands high computational cost and usually requires perfect channel state information (CSI). To tackle the challenge, we develop a green learning (GL) based method to optimize the system with the input of the received pilot signals. Bypassing the channel estimations, the proposed algorithm first extract the representations from the received pilot signals, following by selecting the most relevant representations for the precoding optimization. The model size of the proposed method can be greatly reduced by the analysis of the dominant subspace as well as relevant feature selection. It shows through simulation that the proposed method slightly outperforms the DL-based method with simply $1.3 \sim 1.8 \%$ floating-point operations (FLOPs).