RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

Erhan Karakoca; Hasan Nayir; Ali Görçin; Khalid Qaraqe

doi:10.1109/VTC2023-Spring57618.2023.10199774

RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

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

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350311143
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10199774
Publication status	Published - 2023
Externally published	Yes
Event	97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2023-June
ISSN (Print)	1550-2252

Conference

Conference	97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/Territory	Italy
City	Florence
Period	20/06/23 → 23/06/23

Keywords

Channel estimation
Feature attention
Generative adversarial network
massive MIMO
one-bit ADC

Access to Document

10.1109/VTC2023-Spring57618.2023.10199774

Cite this

Karakoca, E., Nayir, H., Görçin, A., & Qaraqe, K. (2023). RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems. In 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings (IEEE Vehicular Technology Conference; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199774

@inproceedings{3b6aae97142d48d5968f80edbb2eb087,

title = "RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems",

abstract = "The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.",

keywords = "Channel estimation, Feature attention, Generative adversarial network, massive MIMO, one-bit ADC",

author = "Erhan Karakoca and Hasan Nayir and Ali G{\"o}r{\c c}in and Khalid Qaraqe",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/VTC2023-Spring57618.2023.10199774",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings",

address = "United States",

}

Karakoca, E, Nayir, H, Görçin, A & Qaraqe, K 2023, RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems. in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. IEEE Vehicular Technology Conference, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 97th IEEE Vehicular Technology Conference, VTC 2023-Spring, Florence, Italy, 20/06/23. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199774

RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems. / Karakoca, Erhan; Nayir, Hasan; Görçin, Ali et al.
2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Vehicular Technology Conference; Vol. 2023-June).

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

TY - GEN

T1 - RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

AU - Karakoca, Erhan

AU - Nayir, Hasan

AU - Görçin, Ali

AU - Qaraqe, Khalid

PY - 2023

Y1 - 2023

N2 - The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

AB - The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

KW - Channel estimation

KW - Feature attention

KW - Generative adversarial network

KW - massive MIMO

KW - one-bit ADC

UR - https://www.scopus.com/pages/publications/85169844898

U2 - 10.1109/VTC2023-Spring57618.2023.10199774

DO - 10.1109/VTC2023-Spring57618.2023.10199774

M3 - Conference contribution

AN - SCOPUS:85169844898

T3 - IEEE Vehicular Technology Conference

BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring

Y2 - 20 June 2023 through 23 June 2023

ER -

RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

Abstract

Publication series

Conference

Keywords

Access to Document

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EX-QNRF-NPRPC-8: ReSolVE (Resilient Solutions for Vulnerabilities and Emergencies): An Effective National Risk Management Plan for Qatar

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RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

EX-QNRF-NPRPC-8: ReSolVE (Resilient Solutions for Vulnerabilities and Emergencies): An Effective National Risk Management Plan for Qatar

Cite this