Learning How to Demodulate from Few Pilots via Meta-Learning

Sangwoo Park; Hyeryung Jang; Osvaldo Simeone; Joonhyuk Kang

doi:10.1109/SPAWC.2019.8815426

Learning How to Demodulate from Few Pilots via Meta-Learning

Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.

Original language	English (US)
Title of host publication	2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538665282
DOIs	https://doi.org/10.1109/SPAWC.2019.8815426
State	Published - Jul 2019
Event	20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 - Cannes, France Duration: Jul 2 2019 → Jul 5 2019

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2019-July

Conference

Conference	20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Country/Territory	France
City	Cannes
Period	7/2/19 → 7/5/19

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

Keywords

IoT
MAML
Machine learning
demodulation
meta-learning

Access to Document

10.1109/SPAWC.2019.8815426

Cite this

Park, S., Jang, H., Simeone, O., & Kang, J. (2019). Learning How to Demodulate from Few Pilots via Meta-Learning. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 Article 8815426 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2019.8815426

Park, Sangwoo ; Jang, Hyeryung ; Simeone, Osvaldo et al. / Learning How to Demodulate from Few Pilots via Meta-Learning. 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

@inproceedings{da4f29e0f9c6443d8e1ce1cfa60b32ea,

title = "Learning How to Demodulate from Few Pilots via Meta-Learning",

abstract = "Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.",

keywords = "IoT, MAML, Machine learning, demodulation, meta-learning",

author = "Sangwoo Park and Hyeryung Jang and Osvaldo Simeone and Joonhyuk Kang",

note = "Funding Information: ACKNOWLEDGMENT The work of S. Park and J. Kang was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B2012698). The work of H. Jang and O. Simeone was supported by the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (grant agreement No. 725731). Publisher Copyright: {\textcopyright} 2019 IEEE.; 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 ; Conference date: 02-07-2019 Through 05-07-2019",

year = "2019",

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doi = "10.1109/SPAWC.2019.8815426",

language = "English (US)",

series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",

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Park, S, Jang, H, Simeone, O & Kang, J 2019, Learning How to Demodulate from Few Pilots via Meta-Learning. in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019., 8815426, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019, Cannes, France, 7/2/19. https://doi.org/10.1109/SPAWC.2019.8815426

Learning How to Demodulate from Few Pilots via Meta-Learning. / Park, Sangwoo; Jang, Hyeryung; Simeone, Osvaldo et al.
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8815426 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Simeone, Osvaldo

AU - Kang, Joonhyuk

N1 - Funding Information: ACKNOWLEDGMENT The work of S. Park and J. Kang was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B2012698). The work of H. Jang and O. Simeone was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 725731). Publisher Copyright: © 2019 IEEE.

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N2 - Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.

AB - Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.

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M3 - Conference contribution

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BT - 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019

Y2 - 2 July 2019 through 5 July 2019

ER -

Park S, Jang H, Simeone O, Kang J. Learning How to Demodulate from Few Pilots via Meta-Learning. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8815426. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/SPAWC.2019.8815426

Learning How to Demodulate from Few Pilots via Meta-Learning

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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