An Efficient Channel Estimation Technique for Hybrid IRS Assisted Multiuser Wireless Communication System Based on Tensor Modelling

We address the challenging problem of channel estimation in a narrowband multiuser communication that in-corporates intelligent reflecting surfaces (IRS). To overcome the limitations of the two-hop channel in passive IRS system, we adopt a Hybrid IRS architecture, which involves integrating active s...

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Bibliographic Details
Published inTENCON 2023 - 2023 IEEE Region 10 Conference (TENCON) pp. 1198 - 1203
Main Authors N, Varshini, G, Murali Krishna A., Sameer, S. M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 31.10.2023
Subjects
BALS algorithm
Channel estimation
Hybrid IRS
Layout
Mean square error methods
PARAFAC tensor completion
Sensor phenomena and characterization
Tensors
Training
Wireless communication
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Summary:We address the challenging problem of channel estimation in a narrowband multiuser communication that in-corporates intelligent reflecting surfaces (IRS). To overcome the limitations of the two-hop channel in passive IRS system, we adopt a Hybrid IRS architecture, which involves integrating active sensors into a few elements of the IRS. By exploiting the sparse characteristics of the channel, we utilize parallel factor analysis (PARAFAC) tensor models to represent the training signals. We propose an algebraic algorithm to estimate the channel and evaluate the normalized mean square error (NMSE) between the estimated and actual parameters. Additionally, we explore the uniqueness of the canonical polyadic decomposition (CPD) and analyze the essential conditions necessary for achieving accurate channel estimation. Through simulation studies, we validate the effectiveness of our proposed algorithm in estimating channels in a multiuser IRS system.
ISSN:2159-3450
DOI:10.1109/TENCON58879.2023.10322468