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 letter, 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.

中文翻译：

---

基于深度学习分类的时间相关 MIMO 信道的递归 CSI 量化

在频分双工 (FDD) 多输入多输出 (MIMO) 无线通信中，有限信道状态信息 (CSI) 反馈是支持高级单用户和多用户 MIMO 波束成形/预编码的核心工具。为了实现给定的 CSI 质量，CSI 量化码本大小必须随着天线数量呈指数增长，从而导致量化复杂度以及较大 MIMO 系统的反馈开销问题。我们最近提出了一种多级递归格拉斯曼量化器，它可以显着降低 CSI 量化的复杂性。在这封信中，我们展示了这种递归量化器可以有效地与深度学习分类结合以进一步降低复杂性，并且它可以利用时间信道相关性来减少 CSI 反馈开销。