Deep Learning-Based CSI Feedback for RIS-Aided Massive MIMO Systems With Time Correlation

In this letter, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system. In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial uplink bandwidth resources in RIS-aided communication systems. In this letter, we propose a deep learning (DL)-based scheme to reduce the overhead of CSI feedback by compressing the cascaded CSI. In the practical RIS-aided communication systems, the cascaded channel at the adjacent slots inevitably has time correlation. We use long short-term memory to learn time correlation, which can help the neural network to improve the recovery quality of the compressed CSI. Moreover, the attention mechanism is introduced to further improve the CSI recovery quality. Simulation results demonstrate that our proposed DL-based scheme can significantly outperform other DL-based methods in terms of the CSI recovery quality.