论文标题
通过深度学习分类对时间相关的MIMO通道的递归CSI量化
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification
论文作者
论文摘要
在频划分双面（FDD）多输入多输出（MIMO）无线通信中，有限的通道状态信息（CSI）反馈是支持高级单用户和多用户MIMO Beam Forming/Predoding的中心工具。为了达到给定的CSI质量，CSI量化代码簿的大小必须随着天线的数量而成倍增长，从而导致量化复杂性，以及用于大型MIMO系统的反馈开销问题。我们最近提出了一种多阶段递归晶体量化器，可显着降低CSI量化。在本文中，我们表明该递归量化器可以有效地与深度学习分类相结合，以进一步降低复杂性，并且可以利用时间通道相关性以减少CSI反馈开销。
In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.
