  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.
