Correlation-Based Multi-Stage Channel Estimation for RIS-Assisted Multi-User mmWave Systems

This paper proposes a correlation-based multi-stage channel estimation strategy for reconfigurable intelligent surface (RIS)-assisted multi-user (MU) millimeter wave (mmWave) systems, in which the base station (BS), the RIS and the users are all equipped with a uniform planar array (UPA). Specifically, by exploiting the correlation of angle information during multiple consecutive channel coherence blocks, we estimate the full channel state information in the first coherence block (FCB) and only update the gain information in each subsequent block. Then, based on the ambiguity property, a low-pilot-overhead two-stage method is adopted in the FCB. In Stage 1, all users jointly transmit pilot to estimate the angles of arrival at the BS and the correlation factors between different paths of the common BS-RIS channel via introducing a set of matching matrices and a designed RIS phase shift matrix, which achieves a significant MU diversity gain. By exploiting the mmWave cascaded channels' ambiguity property, an ambiguous common BS-RIS channel is constructed with the estimated correlation factors. In Stage 2, each user independently transmits a limited number of pilots to estimate their individual ambiguous RIS-user channel, leading to a comprehensive cascaded channel estimation. For the remaining blocks, we adopt a straightforward least-squares method for rapid gain estimation. Simulation results confirm the method's effectiveness in providing accurate estimation with reduced pilot overhead, outperforming existing methods.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-09-30
DOI
https://doi.org/10.1109/tvt.2024.3471869
Citations
1
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Correlation-Based Multi-Stage Channel Estimation for RIS-Assisted Multi-User mmWave Systems

Cyril Leung, Zhendong Peng, Ruizhe Wang, Cunhua Pan et al.
1 citations
IEEE Transactions on Vehicular Technology
Millimeter-Wave Propagation and Modeling
0.21
article

Correlation-Based Multi-Stage Channel Estimation for RIS-Assisted Multi-User mmWave Systems

Cyril Leung, Zhendong Peng, Ruizhe Wang, Cunhua Pan, Ruisong Weng, Hong Ren
article en
1 citations

Abstract

This paper proposes a correlation-based multi-stage channel estimation strategy for reconfigurable intelligent surface (RIS)-assisted multi-user (MU) millimeter wave (mmWave) systems, in which the base station (BS), the RIS and the users are all equipped with a uniform planar array (UPA). Specifically, by exploiting the correlation of angle information during multiple consecutive channel coherence blocks, we estimate the full channel state information in the first coherence block (FCB) and only update the gain information in each subsequent block. Then, based on the ambiguity property, a low-pilot-overhead two-stage method is adopted in the FCB. In Stage 1, all users jointly transmit pilot to estimate the angles of arrival at the BS and the correlation factors between different paths of the common BS-RIS channel via introducing a set of matching matrices and a designed RIS phase shift matrix, which achieves a significant MU diversity gain. By exploiting the mmWave cascaded channels' ambiguity property, an ambiguous common BS-RIS channel is constructed with the estimated correlation factors. In Stage 2, each user independently transmits a limited number of pilots to estimate their individual ambiguous RIS-user channel, leading to a comprehensive cascaded channel estimation. For the remaining blocks, we adopt a straightforward least-squares method for rapid gain estimation. Simulation results confirm the method's effectiveness in providing accurate estimation with reduced pilot overhead, outperforming existing methods.

IEEE Transactions on Vehicular TechnologyVol. 74(2)
University of British Columbia (CA), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 48%
Millimeter-Wave Propagation and Modeling
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.