University of Oulu

J. He, H. Wymeersch, M. Di Renzo and M. Juntti, "Learning to Estimate RIS-Aided mmWave Channels," in IEEE Wireless Communications Letters, vol. 11, no. 4, pp. 841-845, April 2022, doi: 10.1109/LWC.2022.3147250

Learning to estimate RIS-aided mmWave channels

Saved in:
Author: He, Jiguang1,2; Wymeersch, Henk3; Di Renzo, Marco4;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2International Institute of Next Generation Internet, Macau University of Science and Technology, Taipa 999078, China
3Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
4Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, 91192 Gif-sur-Yvette, France
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022041929425
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-04-19
Description:

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.

see all

Series: IEEE wireless communications letters
ISSN: 2162-2337
ISSN-E: 2162-2345
ISSN-L: 2162-2337
Volume: 11
Issue: 4
Pages: 841 - 845
DOI: 10.1109/LWC.2022.3147250
OADOI: https://oadoi.org/10.1109/LWC.2022.3147250
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the Horizon 2020, European Union’s Framework Programme for Research and Innovation under Grant 871464 (ARIADNE); in part by the Academy of Finland 6G Flagship Project under Grant 318927; and in part by the H2020 RISE-6G Project under Grant 101017011.
EU Grant Number: (871464) ARIADNE - Artificial Intelligence Aided D-band Network for 5G Long Term Evolution
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/