Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

Yanbin He; Geethu Joseph

doi:10.1109/ICASSP49357.2023.10095932

Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (SciVal)

139 Downloads (Pure)

Abstract

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.

Original language	English
Title of host publication	Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of Publication	Piscataway
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	978-1-7281-6327-7
ISBN (Print)	978-1-7281-6328-4
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10095932
Publication status	Published - 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Cascaded channel
Kronecker product
compressed sensing
structured sparsity
alternating minimization
singular value decomposition

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Structure-Aware_Sparse_Bayesian_Learning-Based_Channel_Estimation_for_Intelligent_Reflecting_Surface-Aided_MIMOFinal published version, 1.02 MB

Cite this

@inproceedings{f780ed719f3f48c498cf5f11f1549cfc,

title = "Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO",

abstract = "This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.",

keywords = "Cascaded channel, Kronecker product, compressed sensing, structured sparsity, alternating minimization, singular value decomposition",

author = "Yanbin He and Geethu Joseph",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

year = "2023",

doi = "10.1109/ICASSP49357.2023.10095932",

language = "English",

isbn = "978-1-7281-6328-4",

booktitle = "Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

address = "United States",

}

He, Y & Joseph, G 2023, Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO. in Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Piscataway, 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10095932

Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO. / He, Yanbin ; Joseph, Geethu.
Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2023.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - He, Yanbin

AU - Joseph, Geethu

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.

AB - This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.

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KW - Kronecker product

KW - compressed sensing

KW - structured sparsity

KW - alternating minimization

KW - singular value decomposition

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M3 - Conference contribution

SN - 978-1-7281-6328-4

BT - Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

CY - Piscataway

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023

Y2 - 4 June 2023 through 10 June 2023

ER -

Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

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Keywords

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