论文标题
大规模的MIMO频道国家获取和反馈的深度学习
Deep Learning for Massive MIMO Channel State Acquisition and Feedback
论文作者
论文摘要
大量的多输入多输出（MIMO）系统是5G和未来一代无线网络中过度吞吐量要求的主要推动者，因为它们可以以高光谱和能源效率同时为许多用户提供服务。为了实现这一目标，大量的MIMO系统需要准确，及时的通道状态信息（CSI），这是由涉及试验传输，CSI估计和反馈的训练过程获得的。该培训过程会导致培训开销，该开销与天线，用户和子手机的数量相比。自该概念出现以来，减少大规模MIMO系统中的这种训练开销一直是研究的主要主题。最近，与传统技术相比，已经提出了基于深度学习（DL）的大规模MIMO培训方法。本文概述了如何在大规模MIMO系统的训练过程中使用神经网络（NNS），以通过减少CSI获取开销并降低复杂性来改善性能。
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 this 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 for massive MIMO training have been proposed and showed significant improvements compared to traditional techniques. This paper provides an overview of how neural networks (NNs) can be used in the training process of massive MIMO systems to improve the performance by reducing the CSI acquisition overhead and to reduce complexity.
