University of Oulu

R. Schroeder, J. He, G. Brante and M. Juntti, "Two-Stage Channel Estimation for Hybrid RIS Assisted MIMO Systems," in IEEE Transactions on Communications, vol. 70, no. 7, pp. 4793-4806, July 2022, doi: 10.1109/TCOMM.2022.3176654

Two-stage channel estimation for hybrid RIS assisted MIMO systems

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Author: Schroeder, Rafaela1; He, Jiguang2,3; Brante, Glauber4;
Organizations: 1Centre for Wireless Communications-Radio Technologies, University of Oulu, 90014 Oulu
2Technology Innovation Institute, Abu Dhabi, United Arab Emirate
3Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
4Graduate Program in Electrical and Computer Engineering, Federal University of Technology—Paraná, Curitiba 80230901, Brazil
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-29


Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. However, the channel state information (CSI) acquisition is one of the major challenges for the practical deployment of the RIS. The passive RIS without any baseband processing capabilities brings difficulty on the channel estimation (CE), since the individual channels or the cascaded one can be estimated only at base station (BS) via uplink training or mobile station (MS) via downlink training. In order to facilitate the CSI acquisition, we focus on the hybrid RIS architecture, where a small number of elements are active and able to receive and process the pilot signals at the RIS. The CE is performed in two stages by following the atomic norm minimization to recover the channel parameters, i.e., angles of departure (AoDs), angles of arrival (AoAs), and propagation path gains. Simulation results show that the proposed scheme can outperform the passive RIS CE under the same training overhead. Furthermore, we also study the theoretical performance limits in terms of mean square error (MSE) via Cramér-Rao lower bound (CRLB) analyses.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 70
Issue: 7
Pages: 4793 - 4806
DOI: 10.1109/tcomm.2022.3176654
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been financially supported in part by the Academy of Finland (EERA project), European Union’s Horizon 2020 Framework Programme for Research and Innovation (ARIADNE project, under grant agreement no.871464), Academy of Finland 6Genesis Flagship (grant 318927), CAPES(Finance Code 001) and CNPq Brazil.
EU Grant Number: (871464) ARIADNE - Artificial Intelligence Aided D-band Network for 5G Long Term Evolution
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see