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^*

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9798350311143
DOI'lar	https://doi.org/10.1109/VTC2023-Spring57618.2023.10199774
Yayın durumu	Yayınlandı - 2023
Harici olarak yayınlandı	Evet
Etkinlik	97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy Süre: 20 Haz 2023 → 23 Haz 2023

Yayın serisi

Adı	IEEE Vehicular Technology Conference
Hacim	2023-June
ISSN (Basılı)	1550-2252

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Ülke/Bölge	Italy
Şehir	Florence
Periyot	20/06/23 → 23/06/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Finansman

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.

Finansörler	Finansör numarası
National Authority TÜB˙TAK	121N350
Qatar National Research Fund	101007321
Horizon 2020 Framework Programme

Belgeye Erişim

10.1109/VTC2023-Spring57618.2023.10199774

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199774

@inproceedings{7a0bc501d26b4f83b14cd51393845b9a,

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, hac. 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; Hac. 2023-June).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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