Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

中文翻译：

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大规模 MIMO 信道状态采集和反馈的深度学习

大规模多输入多输出 (MIMO) 系统是 5G 和下一代无线网络中过度吞吐量需求的主要推动因素，因为它们可以同时为许多用户提供高频谱和能源效率。为了实现这种大规模 MIMO 系统，需要准确及时的信道状态信息 (CSI)，这是通过涉及导频传输、CSI 估计和反馈的训练过程获取的。此训练过程会产生训练开销，该开销随天线、用户和子载波的数量而变化。自从概念出现以来，减少大规模 MIMO 系统中的训练开销一直是研究的主要课题。最近，与传统技术相比，基于深度学习 (DL) 的方法已被提出并证明可以显着减少大规模 MIMO 系统中的 CSI 获取和反馈开销。在本文中，我们概述了用于 CSI 获取和反馈的最先进的 DL 架构和算法，并提供了进一步的研究方向。