This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.

中文翻译：

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大型智能表面的深度通道学习辅助毫米波大规模 MIMO 系统

这封信介绍了在大型智能表面 (LIS) 辅助大规模 MIMO（多输入多输出）系统中引入用于信道估计的深度学习 (DL) 框架的第一项工作。设计了一个双卷积神经网络 (CNN) 架构，并用接收到的导频信号来估计直接和级联信道。在多用户场景中，每个用户都可以访问 CNN 来估计自己的频道。对所提出的 DL 方法的性能进行了评估，并与最先进的基于 DL 的技术进行了比较，并证明了其优越的性能。