This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels’ sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels’ structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers’ channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches.

中文翻译：

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基于模型驱动的深度学习的毫米波大规模混合 MIMO 系统的信道估计和反馈


本文提出了一种基于模型驱动的深度学习（MDDL）的宽带毫米波（mmWave）大规模混合多输入多输出（MIMO）系统的信道估计和反馈方案，其中角延迟域信道的稀疏度为用于减少开销。首先，我们考虑时分双工系统的上行链路信道估计。为了减少从基站（BS）有限数量的射频（RF）链估计高维信道的上行链路导频开销，我们建议联合训练相移网络和信道估计器作为自动编码器。特别是，通过利用先验模型中通道的结构化稀疏性并从数据样本中学习集成的可训练参数，所提出的多测量向量学习近似消息传递（MMV-LAMP）网络与设计的冗余字典可以联合恢复多个子载波通道，性能显着增强。此外，我们还考虑了频分双工系统的下行链路信道估计和反馈。类似地，BS处的导频和用户处的信道估计器可以分别联合训练为编码器和解码器。此外，为了进一步减少信道反馈开销，仅将部分子载波上的接收导频反馈给BS，BS可以利用MMV-LAMP网络来重建空间频率信道矩阵。数值结果表明，所提出的基于 MDDL 的信道估计和反馈方案优于最先进的方法。