A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet-of-Things Systems

Zhou, X; Lin, Z; Gu, R; Ni, W; Jamalipour, A

A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet-of-Things Systems

Zhou, X Lin, Z Gu, R Ni, W

Jamalipour, A

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (7), pp. 11190-11201
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhou, X
dc.contributor.author	Lin, Z
dc.contributor.author	Gu, R
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2024-08-07T04:36:08Z
dc.date.available	2024-08-07T04:36:08Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (7), pp. 11190-11201
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180208
dc.description.abstract	With the advantages of super-high transmission rate, anti-electromagnetic interference and good confidentiality, optical wireless communication (OWC) systems play an important role in satellite Internet of Things (IoT). In this paper, we propose a new meta-learning-based channel estimation scheme (Meta-CE) to address the challenges of atmospheric turbulence and phase distortion in satellite OWC links. A neural network-based channel estimator outputs channel parameters, rather than categories, and utilizes meta-learning to enhance its convergence speed and adaptability to new environments. The Meta-CE exhibits superior estimation accuracy, fast convergence, and generalization, compared to baseline schemes, and even outperforms the minimum mean square error (MMSE) channel estimation, especially with short pilot symbols, low signal-to-noise ratio (SNR) and severe turbulence. In a 4×4 multiple-input multiple-output (MIMO) scheme with 8-bit pilot symbols and 0 dB SNR, the mean square error of the Meta-CE is about 35% lower than that of the MMSE in a strong Gamma-Gamma turbulence channel.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3329566
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet-of-Things Systems
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-08-07T04:36:00Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	7

Abstract:

With the advantages of super-high transmission rate, anti-electromagnetic interference and good confidentiality, optical wireless communication (OWC) systems play an important role in satellite Internet of Things (IoT). In this paper, we propose a new meta-learning-based channel estimation scheme (Meta-CE) to address the challenges of atmospheric turbulence and phase distortion in satellite OWC links. A neural network-based channel estimator outputs channel parameters, rather than categories, and utilizes meta-learning to enhance its convergence speed and adaptability to new environments. The Meta-CE exhibits superior estimation accuracy, fast convergence, and generalization, compared to baseline schemes, and even outperforms the minimum mean square error (MMSE) channel estimation, especially with short pilot symbols, low signal-to-noise ratio (SNR) and severe turbulence. In a 4×4 multiple-input multiple-output (MIMO) scheme with 8-bit pilot symbols and 0 dB SNR, the mean square error of the Meta-CE is about 35% lower than that of the MMSE in a strong Gamma-Gamma turbulence channel.

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http://hdl.handle.net/10453/180208