Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation

Erhan Karakoca; Hasan Nayir; Ali Gorcin; Khalid Qaraqe

doi:10.1109/GCWkshps56602.2022.10008723

Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation

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

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1555-1560
Sayfa sayısı	6
ISBN (Elektronik)	9781665459754
DOI'lar	https://doi.org/10.1109/GCWkshps56602.2022.10008723
Yayın durumu	Yayınlandı - 2022
Harici olarak yayınlandı	Evet
Etkinlik	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Virtual, Online, Brazil Süre: 4 Ara 2022 → 8 Ara 2022

Yayın serisi

Adı	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

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

???event.eventtypes.event.conference???	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Ülke/Bölge	Brazil
Şehir	Virtual, Online
Periyot	4/12/22 → 8/12/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Finansman

ACKNOWLEDGMENT This publication is made partially possible by NPRP award [NPRP12S-0225-190152] from 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/GCWkshps56602.2022.10008723

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Karakoca, E., Nayir, H., Gorcin, A., & Qaraqe, K. (2022). Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings (pp. 1555-1560). (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCWkshps56602.2022.10008723

@inproceedings{ba98a9b9e822466eb5f8a490ff527b61,

title = "Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation",

abstract = "In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.",

keywords = "AMP, beamspace MIMO, blind denoising, channel estimation, deep learning, Millimeter wave, neural networks, residual learning",

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

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 ; Conference date: 04-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/GCWkshps56602.2022.10008723",

language = "English",

series = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

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

pages = "1555--1560",

booktitle = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

address = "United States",

}

Karakoca, E, Nayir, H, Gorcin, A & Qaraqe, K 2022, Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation. in 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1555-1560, 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022, Virtual, Online, Brazil, 4/12/22. https://doi.org/10.1109/GCWkshps56602.2022.10008723

Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation. / Karakoca, Erhan; Nayir, Hasan; Gorcin, Ali et al.
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1555-1560 (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation

AU - Karakoca, Erhan

AU - Nayir, Hasan

AU - Gorcin, Ali

AU - Qaraqe, Khalid

PY - 2022

Y1 - 2022

N2 - In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.

AB - In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.

KW - AMP

KW - beamspace MIMO

KW - blind denoising

KW - channel estimation

KW - deep learning

KW - Millimeter wave

KW - neural networks

KW - residual learning

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U2 - 10.1109/GCWkshps56602.2022.10008723

DO - 10.1109/GCWkshps56602.2022.10008723

M3 - Conference contribution

AN - SCOPUS:85146836066

T3 - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

SP - 1555

EP - 1560

BT - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

Y2 - 4 December 2022 through 8 December 2022

ER -

Karakoca E, Nayir H, Gorcin A, Qaraqe K. Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1555-1560. (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings). doi: 10.1109/GCWkshps56602.2022.10008723

Feature Attention Based Blind Denoising Network for mmWave Beamspace Channel Estimation

Özet

Yayın serisi

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

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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