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首页> 外文期刊>Wireless Communications Letters, IEEE >Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback
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Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback

机译：在深度学习中利用双向通道互易性以实现低速率大规模MIMO CSI反馈

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摘要

Channel state information (CSI) feedback is important for multiple-input multiple-output (MIMO) wireless systems to achieve their capacity gain in frequency division duplex mode. For massive MIMO systems, CSI feedback may consume too much bandwidth and degrade spectrum efficiency. This letter proposes a learning-based CSI feedback framework based on limited feedback and bi-directional reciprocal channel characteristics. The massive MIMO base station exploits the available uplink CSI to help recovering the unknown downlink CSI from low rate user feedback. We propose two deep learning architectures, DualNet-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity. DualNet-MAG and DualNet-ABS can exploit the bi-directional correlation of the magnitude and the absolute value of real/imaginary parts of the CSI coefficients, respectively. The experimental results demonstrate that our architectures bring an obvious improvement compared with the downlink-based architecture.
机译：信道状态信息（CSI）反馈对于在频分双工模式下实现多输入多输出（MIMO）无线系统的容量增益很重要。对于大规模MIMO系统，CSI反馈可能会占用过多带宽并降低频谱效率。这封信提出了一种基于有限反馈和双向双向信道特性的基于学习的CSI反馈框架。大型MIMO基站利用可用的上行链路CSI来帮助从低速率用户反馈中恢复未知的下行链路CSI。我们提出了两种深度学习架构，DualNet-MAG和DualNet-ABS，以基于多径互惠性显着减少CSI反馈有效负载。 DualNet-MAG和DualNet-ABS可以分别利用CSI系数的实部/虚部的大小和绝对值的双向相关性。实验结果表明，与基于下行链路的体系结构相比，我们的体系结构具有明显的改进。

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