In the realm of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is paramount. This paper introduces RIS-CoCsiNet, a novel deep learning-based framework designed to greatly enhance feedback efficiency. By leveraging the inherent correlation among proximate user equipments (UEs), our approach strategically categorizes RIS-UE CSI into shared and unique data sets. This nuanced understanding allows for significant reductions in feedback overhead, as the shared data is no longer redundantly relayed. Setting RIS-CoCsiNet apart from traditional autoencoder systems, we incorporate an additional decoder and a combination neural network at the base station. These enhancements are tasked with the precise retrieval and fusion of shared and individual data. And notably, all these innovations are achieved without modifying the UEs. For those UEs boasting multiple antennas, our design seamlessly integrates long short-term memory modules, capturing the intricate correlations between antennas. With a recognition of the non-sparse nature of the RIS-UE CSI phase, we pioneer two magnitude-dependent phase feedback strategies. These strategies adeptly weave in both statistical and real-time CSI magnitude data. The potency of RIS-CoCsiNet is further solidified through compelling simulation results drawn from two diverse channel datasets.

翻译：在可重构智能表面（RIS）辅助无线通信领域，高效的通道状态信息（CSI）反馈至关重要。本文提出RIS-CoCsiNet，一种新颖的基于深度学习框架，旨在大幅提升反馈效率。通过利用邻近用户设备（UE）之间的固有相关性，我们的方法策略性地将RIS-UE CSI划分为共享数据集和唯一数据集。这种细致的理解使得反馈开销显著降低，因为共享数据不再被冗余传输。RIS-CoCsiNet与传统自编码器系统的区别在于，我们在基站端额外引入了一个解码器和一个组合神经网络。这些增强功能负责精确检索和融合共享数据与个体数据。值得注意的是，所有这些创新均无需修改UE即可实现。对于配备多天线的UE，我们的设计无缝集成长短期记忆模块，以捕捉天线间的复杂相关性。考虑到RIS-UE CSI相位的非稀疏特性，我们率先提出了两种幅度依赖的相位反馈策略。这些策略巧妙地融合了统计和实时CSI幅度数据。通过两个不同通道数据集的令人信服的仿真结果，RIS-CoCsiNet的有效性得到了进一步验证。