Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information
Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information
复制标题
基于学习的相位压缩和量化，用于具有幅度辅助信息的大规模 MIMO CSI 反馈
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ta
中科院分区：
文献类型：
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作者：
Yu;Z. Ding;Zhenyu Liu;Ta
在频分双工（FDD）大规模多输入多输出（MIMO）无线系统中，深度学习技术被认为是CSI恢复最有效的解决方案之一。近年来，为了在基站实现更好的CSI震级恢复，先进的基于学习的CSI反馈解决方案将震级和相位恢复解耦，以充分利用当前CSI震级与先前时隙、上行频段和附近位置的震级之间的强相关性。然而，CSI相位恢复由于其复杂的模式，是进一步提高CSI采收率的主要挑战。在这封信中，我们提出了一个基于有限反馈和震级辅助信息的基于学习的CSI反馈框架。与之前的工作相比，我们提出的框架和提出的损失函数使端到端学习能够共同优化CSI幅度和相位恢复性能。数值模拟表明，在室内和室外情况下，所提出的损失函数优于其他相位恢复方法。我们还使用不同的核心层设计来检验所提出的框架的性能。
—In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) wireless systems, deep learning techniques are regarded as one of the most efficient solutions for CSI recovery. In recent times, to achieve better CSI magnitude recovery at base stations, advanced learning-based CSI feedback solutions decouple magnitude and phase recovery to fully leverage the strong correlation between current CSI magnitudes and those of previous time slots, uplink band, and near locations. However, the CSI phase recovery is a major challenge to further enhance the CSI recovery owing to its complicated patterns. In this letter, we propose a learning-based CSI feedback framework based on limited feedback and magnitude-aided information. In contrast to previous works, our proposed framework with a proposed loss function enables end-to-end learning to jointly optimize the CSI magnitude and phase recovery performance. Numerical simulations show that, the proposed loss function outperform alternate approaches for phase recovery over the overall CSI recovery in both indoor and outdoor scenarios. The performance of the proposed framework was also examined using different core layer designs.
影响因子：
8.3
作者：
Liu, Zhenyu;Zhang, Lin;Ding, Zhi
通讯作者：
Ding, Zhi
影响因子：
6.3
作者：
Liu, Zhenyu;Zhang, Lin;Ding, Zhi
通讯作者：
Ding, Zhi