An Autoregressive Model-Based Differential Framework With Learnable Regularization for CSI Feedback in Time-Varying Massive MIMO Systems

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2024-12-09 DOI:10.1109/LCOMM.2024.3512537

Yangyang Zhang;Danyang Yu;Xichang Zhang;Yi Liu

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引用次数: 0

Abstract

In frequency division duplex (FDD) mode, the substantial feedback overhead in massive multi-input multi-output (MIMO) systems needs to be mitigated. Existing channel feedback methods that utilize channel temporal correlation exhibit limited performance under low compression ratios (CRs) or high-speed user equipment (UE) in the outdoor scenario. To address these challenges, we propose an autoregressive (AR) model-based differential framework incorporating a regularization learning network (RE-LENet) for channel state information (CSI) feedback in time-varying massive MIMO systems. The proposed AR model-based differential framework can capture the channel temporal correlation more effectively, reducing the degradation of channel reconstruction performance over time. We also design a convolutional neural network (CNN)-based RE-LENet to enhance the reconstruction performance of both the channel differential terms and the initial channel simultaneously. Numerical results indicate that the proposed CSI feedback framework outperforms existing methods.

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时变大规模MIMO系统CSI反馈的可学习正则化自回归模型差分框架

在频分双工（FDD）模式下，需要减少大规模多输入多输出（MIMO）系统中大量的反馈开销。现有的利用信道时间相关的信道反馈方法在室外低压缩比（CRs）或高速用户设备（UE）情况下表现出有限的性能。为了解决这些挑战，我们提出了一个基于自回归（AR）模型的差分框架，该框架结合了一个正则化学习网络（RE-LENet），用于时变大规模MIMO系统中的信道状态信息（CSI）反馈。提出的基于AR模型的差分框架可以更有效地捕获信道时间相关性，减少信道重建性能随时间的退化。我们还设计了一种基于卷积神经网络（CNN）的RE-LENet，以同时提高信道差分项和初始信道的重建性能。数值结果表明，所提出的CSI反馈框架优于现有的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.