An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting
An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting
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DOI：
10.1109/tcomm.2020.2993626
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发表时间：
2020-08-01
影响因子：
8.3
通讯作者：
Ding, Zhi
中科院分区：
文献类型：
--
作者：
Liu, Zhenyu;Zhang, Lin;Ding, Zhi
信道状态信息（CSI）报告对于多输入多输出（MIMO）无线收发器在频分双工（FDD）模式中实现高容量和能量效率是重要的。用于大规模MIMO系统的CSI报告可能消耗大的带宽并且降低频谱效率。与信道特性集成的基于深度学习（DL）的CSI报告已经在改进CSI压缩和恢复方面展示了成功。为了进一步提高CSI反馈的编码效率，我们开发了一个高效的基于DL的压缩框架CQNet，以联合解决带宽约束下的CSI压缩，码字量化和恢复。CQNet与其他基于DL的CSI反馈工作直接兼容，以进一步增强。我们提出了一个更有效的量化方案在径向坐标通过引入一种新的幅度自适应相位量化框架。与传统的CSI报告相比，CQNet表现出上级CSI反馈效率和更好的CSI重建精度。
Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.