Identification of Distorted RF Components via Deep Multi-Task Learning

Mehmet Ali Aygul; Ebubekir Memisoglu; Hakan Ali Cirpan; Huseyin Arslan

doi:10.1109/VTC2022-Fall57202.2022.10012986

Identification of Distorted RF Components via Deep Multi-Task Learning

Mehmet Ali Aygul^*
, Ebubekir Memisoglu
, Hakan Ali Cirpan
, Huseyin Arslan

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

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

1 Atıf (Scopus)

Özet

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781665454681
DOI'lar	https://doi.org/10.1109/VTC2022-Fall57202.2022.10012986
Yayın durumu	Yayınlandı - 2022
Etkinlik	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom Süre: 26 Eyl 2022 → 29 Eyl 2022

Yayın serisi

Adı	IEEE Vehicular Technology Conference
Hacim	2022-September
ISSN (Basılı)	1550-2252

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

???event.eventtypes.event.conference???	96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Ülke/Bölge	United Kingdom
Şehir	London
Periyot	26/09/22 → 29/09/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Finansman

This work was supported in part by the Scientific and Technological Research Council of Turkey (TÜB˙TAK) under Grant No. 5200030 with the cooperation of VESTEL and Istanbul Medipol University.

Finansörler	Finansör numarası
VESTEL
İstanbul Medipol Üniversitesi
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	5200030

Belgeye Erişim

10.1109/VTC2022-Fall57202.2022.10012986

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Aygul, M. A., Memisoglu, E., Cirpan, H. A., & Arslan, H. (2022). Identification of Distorted RF Components via Deep Multi-Task Learning. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings (IEEE Vehicular Technology Conference; Hac. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012986

@inproceedings{982fdb1b79cc4f698ba559f6d3e39150,

title = "Identification of Distorted RF Components via Deep Multi-Task Learning",

abstract = "High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.",

keywords = "Deep learning, RF impairments, distorted RF components identification, multi-task learning",

author = "Aygul, \{Mehmet Ali\} and Ebubekir Memisoglu and Cirpan, \{Hakan Ali\} and Huseyin Arslan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 ; Conference date: 26-09-2022 Through 29-09-2022",

year = "2022",

doi = "10.1109/VTC2022-Fall57202.2022.10012986",

language = "English",

series = "IEEE Vehicular Technology Conference",

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

booktitle = "2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings",

address = "United States",

}

Aygul, MA, Memisoglu, E, Cirpan, HA & Arslan, H 2022, Identification of Distorted RF Components via Deep Multi-Task Learning. in 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. IEEE Vehicular Technology Conference, hac. 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-Fall57202.2022.10012986

Identification of Distorted RF Components via Deep Multi-Task Learning. / Aygul, Mehmet Ali; Memisoglu, Ebubekir; Cirpan, Hakan 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; Hac. 2022-September).

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

TY - GEN

T1 - Identification of Distorted RF Components via Deep Multi-Task Learning

AU - Aygul, Mehmet Ali

AU - Memisoglu, Ebubekir

AU - Cirpan, Hakan Ali

AU - Arslan, Huseyin

PY - 2022

Y1 - 2022

N2 - High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

AB - High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

KW - Deep learning

KW - RF impairments

KW - distorted RF components identification

KW - multi-task learning

UR - https://www.scopus.com/pages/publications/85147035617

U2 - 10.1109/VTC2022-Fall57202.2022.10012986

DO - 10.1109/VTC2022-Fall57202.2022.10012986

M3 - Conference contribution

AN - SCOPUS:85147035617

T3 - IEEE Vehicular Technology Conference

BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings

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 -

Identification of Distorted RF Components via Deep Multi-Task Learning

Özet

Yayın serisi

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

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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