Transfer Learning for Wireless Networks: A Comprehensive Survey

Nguyen, CT; Van Huynh, N; Chu, NH; Saputra, YM; Hoang, DT; Nguyen, DN; Pham, QV; Niyato, D; Dutkiewicz, E; Hwang, WJ

Transfer Learning for Wireless Networks: A Comprehensive Survey

Nguyen, CT Van Huynh, N Chu, NH Saputra, YM Hoang, DT Nguyen, DN Pham, QV Niyato, D Dutkiewicz, E

Hwang, WJ

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: Proceedings of the IEEE, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, CT
dc.contributor.author	Van Huynh, N
dc.contributor.author	Chu, NH
dc.contributor.author	Saputra, YM
dc.contributor.author	Hoang, DT
dc.contributor.author	Nguyen, DN
dc.contributor.author	Pham, QV
dc.contributor.author	Niyato, D
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.contributor.author	Hwang, WJ
dc.date.accessioned	2022-07-28T03:26:37Z
dc.date.available	2022-07-28T03:26:37Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of the IEEE, 2022
dc.identifier.issn	0018-9219
dc.identifier.issn	1558-2256
dc.identifier.uri	http://hdl.handle.net/10453/159278
dc.description.abstract	With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DE210100651
dc.relation.ispartof	Proceedings of the IEEE
dc.relation.isbasedon	10.1109/JPROC.2022.3175942
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.title	Transfer Learning for Wireless Networks: A Comprehensive Survey
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-06-06T00:00:00+1000Z
dc.date.updated	2022-07-28T03:26:29Z
pubs.publication-status	Published

Abstract:

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

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http://hdl.handle.net/10453/159278