arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.03303cs.ITeess.SPmath.IT
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面向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

Jiajia Guo, Xi Yang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

AI总结:

针对RIS辅助无线通信中CSI反馈效率问题,提出RIS-CoCsiNet深度学习框架,通过划分共享与独有CSI、增设基站解码融合模块、结合LSTM及幅度相关相位策略,在不修改UE的前提下显著降低反馈开销,经两类数据集仿真验证有效。

摘要:

在可重构智能表面(reconfigurable intelligent surface, RIS)辅助无线通信领域,高效的信道状态信息(channel state information, CSI)反馈至关重要。本文提出RIS-CoCsiNet,这是一种新型基于深度学习的框架,旨在大幅提升反馈效率。利用邻近用户设备(user equipments, UEs)之间的固有相关性,我们的方法策略性地将RIS-UE CSI划分为共享数据集与独有数据集。这种精细化的划分可显著降低反馈开销,因为共享数据无需被冗余传输。与传统自编码器系统不同,我们在基站侧增设了一个额外解码器和一个组合神经网络,这些增强模块负责精准恢复并融合共享数据与个体数据,且值得注意的是,所有这些创新均无需修改UE端。对于配备多天线的UE,我们的设计无缝集成了长短期记忆(long short-term memory)模块,以捕捉天线间复杂的相关性。鉴于RIS-UE CSI相位的非稀疏特性,我们开创性地提出了两种幅度相关的相位反馈策略,这些策略巧妙地结合了统计CSI幅度数据与实时CSI幅度数据。来自两个不同信道数据集的仿真结果充分验证了RIS-CoCsiNet的有效性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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