论文标题
RIS辅助多用户系统的基于深度学习的CSI反馈
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
论文作者
论文摘要
在可重新配置的智能表面（RIS）辅助无线通信的领域中，有效的通道状态信息（CSI）反馈至关重要。本文介绍了RIS-Cocsinet，这是一种新型的基于深度学习的框架，旨在极大地提高反馈效率。通过利用接近用户设备（UES）之间的固有相关性，我们的方法从战略上将RIS-UE CSI分类为共享和唯一的数据集。这种细微的理解允许大幅减少反馈开销，因为共享数据不再冗余地中继。将RIS-COCSINET设置为除传统的自动编码器系统外，我们在基站组合了一个额外的解码器和组合神经网络。这些增强功能是按照共享和个人数据的精确检索和融合的任务。值得注意的是，所有这些创新都是在不修改UE的情况下实现的。对于那些拥有多个天线的UE，我们的设计无缝地整合了长期的短期记忆模块，从而捕获了天线之间的复杂相关性。在认识到RIS-ue CSI阶段的非相对性性质的同时，我们开创了两个级依赖性相位反馈策略。这些策略擅长于统计和实时CSI幅度数据编织。通过从两个不同的通道数据集绘制的引人注目的模拟结果，RIS-Cocsinet的效力得到了进一步巩固。
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.
