Learning to Estimate RIS-Aided mmWave Channels
Journal article, 2022

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.

Phase control

Optimization

deep neural network.

Deep unfolding

cascaded channel estimation

MIMO communication

Training

Radio frequency

Channel estimation

reconfigurable intelligent surface

Neural networks

Author

Jiguang He

University of Oulu

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Marco Di Renzo

University Paris-Saclay

Markku Juntti

University of Oulu

IEEE Wireless Communications Letters

2162-2337 (ISSN) 2162-2345 (eISSN)

Vol. 11 4 841-845

Reconfigurable Intelligent Sustainable Environments for 6G Wireless Networks

European Commission (EC) (EC/2020/101017011), 2021-01-01 -- 2023-12-31.

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/LWC.2022.3147250

More information

Latest update

12/27/2022