University of Oulu

R. Schroeder, J. He and M. Juntti, "Passive RIS vs. Hybrid RIS: A Comparative Study on Channel Estimation," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9448802

Passive RIS vs. hybrid RIS : a comparative study on channel estimation

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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, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021100850399
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-08
Description:

Abstract

The reconfigurable intelligent surface (RIS) plays an important role in maintaining the connectivity in millimeter wave (mmWave) MIMO systems when the direct channel between the transceivers is blocked. However, it is difficult to acquire the channel state information (CSI), which is essential for the design of RIS phase control matrix and beamforming vectors at the transceivers. In this paper, we compare the channel estimation (CE) performance and achieved spectral efficiency (SE) of the purely passive and hybrid RIS architectures. CE is done via atomic norm minimization (ANM). For the purely passive RIS, we follow a two-stage procedure to sequentially estimate the channel parameters, while for the hybrid RIS we estimate the individual channels at the RIS based on the observations from active RIS elements assuming alternating uplink and downlink training. The simulation results show that the purely passive RIS brings better CE and SE performance compared to the hybrid RIS under the same training overhead. We further consider different setups for the hybrid RIS and study the tradeoffs among them.

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Series: IEEE Vehicular Technology Conference
ISSN: 1090-3038
ISSN-L: 1090-3038
ISBN: 978-1-7281-8964-2
ISBN Print: 978-1-7281-8965-9
Article number: 9448802
DOI: 10.1109/VTC2021-Spring51267.2021.9448802
OADOI: https://oadoi.org/10.1109/VTC2021-Spring51267.2021.9448802
Host publication: 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Conference: IEEE Vehicular Technology Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work has been financially supported in part by the Academy of Finland (ROHM project, grant 319485), 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: 319485
871464
318927
Detailed Information: 319485 (Academy of Finland Funding decision)
871464 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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