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

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

ACKNOWLEDGMENT This publication was made possible in parts by NPRP13S-0130-200200 and by NPRP14C-0909-210008 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. We thank to StorAIge project that has received funding from the KDT Joint Undertaking (JU) under Grant Agreement No. 101007321. The JU receives support from the European Union’s Horizon 2020 research and innovation programme in France, Belgium, Czech Republic, Germany, Italy, Sweden, Switzerland, Türkiye, and National Authority TÜB˙TAK with project ID 121N350.

Funders	Funder number
National Authority TÜB˙TAK	121N350
Qatar National Research Fund	101007321
Horizon 2020 Framework Programme

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

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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",

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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

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AU - Qaraqe, Khalid

PY - 2023

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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.

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KW - feature attention

KW - generative adversarial network

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KW - one-bit ADC

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ER -

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

Abstract

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Keywords

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