Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
This addresses channel estimation for multi-user mm-Wave massive MIMO systems, but it is incremental as it applies DL to a new scenario.
The paper tackles channel estimation in large intelligent surface-aided massive MIMO systems by introducing a deep learning framework using a twin CNN architecture, achieving superior performance compared to state-of-the-art DL-based techniques.
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.