Deep Learning-Based CSI Feedback for RIS-Assisted Multi-User Systems

In the domain of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is crucial. This paper proposes RIS-CoCsiNet, a novel deep learning-based framework aimed at significantly enhancing feedback efficiency. The proposed method leverages the inherent correlation among neighboring user equipments (UEs) by categorizing RIS-UE CSI information into two parts: shared information among nearby UEs and unique information specific to each individual UE. By exploiting the correlation in RIS-UE CSI, redundant transmission of shared information can be substantially reduced, thereby minimizing the overhead associated with repeatedly feeding back this shared data. Unlike conventional autoencoder-based CSI feedback frameworks, our approach incorporates an additional decoder and a combination neural network (NN) at the base station. These components recover the shared information from the feedback CSI of two neighboring UEs and combine it with the individual information, respectively, without requiring any modifications at the UEs. Through end-to-end learning, the encoders at neighboring UEs are trained to collaboratively feedback shared information while independently feeding back the unique information. For UEs equipped with multiple antennas, a baseline NN architecture with long short-term memory (LSTM) modules is introduced to capture the correlation among nearby antennas. Additionally, since the RIS-UE CSI phase is not sparse, we propose magnitude-dependent phase feedback strategies that incorporate statistical or instantaneous CSI magnitude information into the phase feedback process. Extensive simulations across two diverse channel datasets validate the effectiveness of RIS-CoCsiNet.