In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this paper, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios.

翻译：为了与频度分部-多输出(DFD)模式的大型多输入多输出(MIMO)系统数据率高的可靠通信,接收器的估计频道状态信息需要反馈回发机,然而,随着天线数量的增加,反馈管理费用变得过高。本文建议对大型MIMO系统采用两个阶段的低级(TSLR) CSI反馈计划,以减少基于模型驱动的深层次学习的反馈管理费用。此外,我们设计了一个称为FISTA-Net的深迭代神经网络,通过开发快速迭代收缩阈值算法(FISTA)实现更有效的CSI反馈。此外,基于关注机制的FISTA-Net(ST-Net)设计了一个收缩阈值网络(ST-Net),这个网络可以根据适应性选择阈值。模拟结果显示,拟议的 TSICSI反馈计划和FISTA-Net(FISTA-Net)超越了各种情景中的现有算法。