Learning to Estimate RIS-Aided mmWave Channels
This work addresses channel estimation for RIS-aided mmWave communications, which is an incremental improvement in a domain-specific area.
The paper tackles the problem of channel estimation in RIS-aided mmWave SIMO systems by applying a model-driven deep unfolding neural network, achieving better performance than the least squares method with reduced training overhead and computational complexity.
Inspired by the remarkable learning and prediction performance of deep neural networks (DNNs), we apply one special type of DNN framework, known as model-driven deep unfolding neural network, to reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) single-input multiple-output (SIMO) systems. We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.