Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

Jaakko Pihlajasalo; Dani Korpi; Taneli Riihonen; Jukka Talvitie; Mikko A. Uusitalo; Mikko Valkama

doi:10.1109/SPAWC51304.2022.9834021

Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

Jaakko Pihlajasalo
, Dani Korpi
, Taneli Riihonen
, Jukka Talvitie
, Mikko A. Uusitalo
, Mikko Valkama

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

5 Sitaatiot (Scopus)

51 Lataukset (Pure)

Abstrakti

With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.

Alkuperäiskieli	Englanti
Otsikko	2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Kustantaja	IEEE
ISBN (elektroninen)	9781665494557
ISBN (painettu)	9781665494564
DOI - pysyväislinkit	https://doi.org/10.1109/SPAWC51304.2022.9834021
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Signal Processing Advances in Wireless Communication - Oulu, Suomi Kesto: 4 heinäk. 2022 → 6 heinäk. 2022

Julkaisusarja

Nimi	SPAWC
ISSN (painettu)	1948-3244
ISSN (elektroninen)	1948-3252

Conference

Conference	IEEE International Workshop on Signal Processing Advances in Wireless Communication
Maa/Alue	Suomi
Kaupunki	Oulu
Ajanjakso	4/07/22 → 6/07/22

Rahoitus

In this paper, we demonstrated how different ML-based receiver architectures can be trained to deal with various hardware impairments. Our study covered two ML-based receivers, one of which is operating only in the frequency domain (DeepRx), and another one that has processing layers both in time and frequency domains (HybridDeepRx). We provided extensive numerical results in 28 GHz mmWave network context, which indicate that a frequency-domain neural network is well-suited for mitigating the effects of PN, whereas a hybrid time/frequency-domain network is required for dealing with IQ imbalance and nonlinear distortion. The results also indicated that both ML-based receivers outperform conventional baseline receivers in the appropriate impairment ACKNOWLEDGMENT This work was supported in part by Nokia Bell Labs and in part by the Academy of Finland under the grants #319994, #332361, and #338224.

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Pääsy asiakirjaan

10.1109/SPAWC51304.2022.9834021

Detection of Impaired OFDM Waveforms
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Accepted author manuscript, 136 KBLisenssi: Unspecified

https://urn.fi/URN:NBN:fi:tuni-202210077499Lisenssi: Unspecified

Siteeraa tätä

@inproceedings{397a8d520a90444b9c37d5fc40dbf676,

title = "Detection of Impaired OFDM Waveforms Using Deep Learning Receiver",

abstract = "With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.",

keywords = "5G NR, 6G, deep learning, hardware impairments, IQ imbalance, machine learning, mmWave, nonlinear distortion, phase noise, power amplifier, sub-THz",

author = "Jaakko Pihlajasalo and Dani Korpi and Taneli Riihonen and Jukka Talvitie and Uusitalo, \{Mikko A.\} and Mikko Valkama",

note = "Funding Information: In this paper, we demonstrated how different ML-based receiver architectures can be trained to deal with various hardware impairments. Our study covered two ML-based receivers, one of which is operating only in the frequency domain (DeepRx), and another one that has processing layers both in time and frequency domains (HybridDeepRx). We provided extensive numerical results in 28 GHz mmWave network context, which indicate that a frequency-domain neural network is well-suited for mitigating the effects of PN, whereas a hybrid time/frequency-domain network is required for dealing with IQ imbalance and nonlinear distortion. The results also indicated that both ML-based receivers outperform conventional baseline receivers in the appropriate impairment ACKNOWLEDGMENT This work was supported in part by Nokia Bell Labs and in part by the Academy of Finland under the grants \#319994, \#332361, and \#338224. Publisher Copyright: {\textcopyright} 2022 IEEE. JUFOID=88648; IEEE International Workshop on Signal Processing Advances in Wireless Communication ; Conference date: 04-07-2022 Through 06-07-2022",

year = "2022",

doi = "10.1109/SPAWC51304.2022.9834021",

language = "English",

isbn = "9781665494564",

series = "SPAWC",

publisher = "IEEE",

booktitle = "2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022",

address = "United States",

}

Pihlajasalo, J, Korpi, D, Riihonen, T , Talvitie, J, Uusitalo, MA & Valkama, M 2022, Detection of Impaired OFDM Waveforms Using Deep Learning Receiver. julkaisussa 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022. SPAWC, IEEE, IEEE International Workshop on Signal Processing Advances in Wireless Communication, Oulu, Suomi, 4/07/22. https://doi.org/10.1109/SPAWC51304.2022.9834021

TY - GEN

T1 - Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

AU - Pihlajasalo, Jaakko

AU - Korpi, Dani

AU - Riihonen, Taneli

AU - Talvitie, Jukka

AU - Uusitalo, Mikko A.

AU - Valkama, Mikko

N1 - Funding Information: In this paper, we demonstrated how different ML-based receiver architectures can be trained to deal with various hardware impairments. Our study covered two ML-based receivers, one of which is operating only in the frequency domain (DeepRx), and another one that has processing layers both in time and frequency domains (HybridDeepRx). We provided extensive numerical results in 28 GHz mmWave network context, which indicate that a frequency-domain neural network is well-suited for mitigating the effects of PN, whereas a hybrid time/frequency-domain network is required for dealing with IQ imbalance and nonlinear distortion. The results also indicated that both ML-based receivers outperform conventional baseline receivers in the appropriate impairment ACKNOWLEDGMENT This work was supported in part by Nokia Bell Labs and in part by the Academy of Finland under the grants #319994, #332361, and #338224. Publisher Copyright: © 2022 IEEE. JUFOID=88648

PY - 2022

Y1 - 2022

N2 - With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.

AB - With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.

KW - 5G NR

KW - 6G

KW - deep learning

KW - hardware impairments

KW - IQ imbalance

KW - machine learning

KW - mmWave

KW - nonlinear distortion

KW - phase noise

KW - power amplifier

KW - sub-THz

U2 - 10.1109/SPAWC51304.2022.9834021

DO - 10.1109/SPAWC51304.2022.9834021

M3 - Conference contribution

AN - SCOPUS:85136025859

SN - 9781665494564

T3 - SPAWC

BT - 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022

PB - IEEE

T2 - IEEE International Workshop on Signal Processing Advances in Wireless Communication

Y2 - 4 July 2022 through 6 July 2022

ER -

Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

Abstrakti

Julkaisusarja

Conference

Rahoitus

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä