Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder

Hasan Nayir; Erhan Karakoca; Ali Gorcin; Khalid Qaraqe

doi:10.1109/VTC2022-Fall57202.2022.10013010

Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder

Hasan Nayir^*, Erhan Karakoca, Ali Gorcin, Khalid Qaraqe

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

Original language	English
Title of host publication	2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454681
DOIs	https://doi.org/10.1109/VTC2022-Fall57202.2022.10013010
Publication status	Published - 2022
Externally published	Yes
Event	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom Duration: 26 Sept 2022 → 29 Sept 2022

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2022-September
ISSN (Print)	1550-2252

Conference

Conference	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/Territory	United Kingdom
City	London
Period	26/09/22 → 29/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

ACKNOWLEDGMENT This publication was made possible by the NPRP award [NPRP12S-0225-190152] from the Qatar National Research Fund, a member of The Qatar Foundation. The statements made herein are solely the responsibility of the authors.

Funders	Funder number
Qatar National Research Fund

Keywords

Convolutional autoencoder
hybrid-field channel
massive MIMO
mmWave
spectral efficiency

Access to Document

10.1109/VTC2022-Fall57202.2022.10013010

Cite this

Nayir, H., Karakoca, E., Gorcin, A., & Qaraqe, K. (2022). Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings (IEEE Vehicular Technology Conference; Vol. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2022-Fall57202.2022.10013010

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title = "Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder",

abstract = "Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.",

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author = "Hasan Nayir and Erhan Karakoca and Ali Gorcin and Khalid Qaraqe",

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year = "2022",

doi = "10.1109/VTC2022-Fall57202.2022.10013010",

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Nayir, H, Karakoca, E, Gorcin, A & Qaraqe, K 2022, Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder. in 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. IEEE Vehicular Technology Conference, vol. 2022-September, Institute of Electrical and Electronics Engineers Inc., 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022, London, United Kingdom, 26/09/22. https://doi.org/10.1109/VTC2022-Fall57202.2022.10013010

Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder. / Nayir, Hasan; Karakoca, Erhan; Gorcin, Ali et al.
2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE Vehicular Technology Conference; Vol. 2022-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nayir, Hasan

AU - Karakoca, Erhan

AU - Gorcin, Ali

AU - Qaraqe, Khalid

PY - 2022

Y1 - 2022

N2 - Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

AB - Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

KW - Convolutional autoencoder

KW - hybrid-field channel

KW - massive MIMO

KW - mmWave

KW - spectral efficiency

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M3 - Conference contribution

AN - SCOPUS:85146982468

T3 - IEEE Vehicular Technology Conference

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022

Y2 - 26 September 2022 through 29 September 2022

ER -

Nayir H, Karakoca E, Gorcin A, Qaraqe K. Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE Vehicular Technology Conference). doi: 10.1109/VTC2022-Fall57202.2022.10013010

Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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