In beyond fifth-generation (B5G) era, massive multiple-input multiple-output (M-MIMO) will be a key technology to offer higher network capacities. Due to the different frequency of uplink and downlink channels in FDD systems, the channel state information (CSI) feedback from user terminal to the base station is necessary, but this reduces the spectrum efficiency. This letter proposes a deep learning based solution to predict the downlink CSI in frequency division duplex (FDD) systems, which is termed as complex-valued three dimensional convolutional neural network (CV-3DCNN). The proposed network uses a complex-valued neural network in complex domain to deal with the complex CSI matrices, and adopts three-dimensional convolution operations for feature extraction. The proposed scheme aims to make full use of the hidden information of the complex matrices of the CSI data, and to minimize information loss caused by data processing. The experimental results demonstrate that the proposed architecture can improve accuracy of the downlink CSI prediction by approximately 6 dB.

中文翻译：

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CV-3DCNN：用于FDD大规模MIMO系统中CSI预测的复值深度学习

在超过第五代（B5G）时代，大规模多输入多输出（M-MIMO）将成为提供更高网络容量的关键技术。由于FDD系统中上行链路和下行链路信道的频率不同，因此从用户终端到基站的信道状态信息（CSI）反馈是必要的，但这会降低频谱效率。这封信提出了一种基于深度学习的解决方案，以预测频分双工（FDD）系统中的下行链路CSI，该解决方案被称为复值三维卷积神经网络（CV-3DCNN）。拟议的网络使用复杂域中的复值神经网络来处理复杂的CSI矩阵，并采用三维卷积运算进行特征提取。所提出的方案旨在充分利用CSI数据的复杂矩阵的隐藏信息，并使由数据处理引起的信息损失最小化。实验结果表明，所提出的体系结构可以将下行链路CSI预测的准确性提高大约6 dB。