Channel estimation for reconfigurable intelligent surface-aided millimeter-wave massive multiple-input multiple-output system with deep residual attention network

We first model the channel estimation in sixth-generation (6G) systems as a super-resolution problem and adopt a deep residual attention approach to learn the nontrivial mapping from the received measurement to the reconfigurable intelligent surface (RIS) channel. Subsequently, we design a deep residual attention-based channel estimation framework (DRA-Net) to exploit the RIS channel distribution characteristics. Furthermore, to transfer the RIS channel feature maps extracted from the residual attention blocks (RABs) to the end of the estimator for accurate reconstruction, we propose a novel and effective feature fusion approach. The simulation results demonstrate that the proposed DRA-Net-based channel estimation method outperforms other deep learning-based and conventional algorithms.