University of Oulu

R. Schroeder, J. He and M. Juntti, "Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization," 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022, pp. 1219-1224, doi: 10.1109/ICCWorkshops53468.2022.9814534

Channel estimation for hybrid RIS aided MIMO communications via atomic norm minimization

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Author: Schroeder, Rafaela1; He, Jiguang1; Juntti, Markku1
Organizations: 1Centre for Wireless Communications, FI-90014, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-09-30


Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating blockages in millimeter wave (mmWave) and terahertz (THz) communications networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Tra-ditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for extracting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.

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Series: IEEE International Conference on Communications workshop
ISSN: 2164-7038
ISSN-E: 2694-2941
ISSN-L: 2164-7038
ISBN: 978-1-6654-2672-5
ISBN Print: 978-1-6654-2671-8
Pages: 1219 - 1224
DOI: 10.1109/ICCWorkshops53468.2022.9814534
Host publication: 2022 IEEE International Conference on Communications Workshops (ICC Workshops)
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
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), and Academy of Finland 6Genesis Flagship (grant 318927).
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)
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