Learning to Estimate RIS-Aided mmWave Channels
- URL: http://arxiv.org/abs/2107.12631v1
- Date: Tue, 27 Jul 2021 06:57:56 GMT
- Title: Learning to Estimate RIS-Aided mmWave Channels
- Authors: Jiguang He and Henk Wymeersch and Marco Di Renzo and Markku Juntti
- Abstract summary: 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.
- Score: 50.15279409856091
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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