Please use this identifier to cite or link to this item:
Title: Transfer learning for wireless networks: a comprehensive survey
Authors: Nguyen, Cong T.
Van Huynh, Nguyen
Chu, Nam H.
Saputra, Yuris Mulya
Hoang, Dinh Thai
Nguyen, Diep N.
Pham, Quoc-Viet
Niyato, Dusit
Dutkiewicz, Eryk
Hwang, Won-Joo
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Nguyen, C. T., Van Huynh, N., Chu, N. H., Saputra, Y. M., Hoang, D. T., Nguyen, D. N., Pham, Q., Niyato, D., Dutkiewicz, E. & Hwang, W. (2022). Transfer learning for wireless networks: a comprehensive survey. Proceedings of the IEEE, 110(8), 1073-1115.
Project: AISG2-RP-2020-019
Journal: Proceedings of the IEEE
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.
ISSN: 0018-9219
DOI: 10.1109/JPROC.2022.3175942
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

Updated on Feb 1, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.