Transfer learning for wireless networks: a comprehensive survey
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/163761 |
| _version_ | 1826111613859528704 |
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| author | 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 |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering 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 |
| author_sort | Nguyen, Cong T. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:53:38Z |
| format | Journal Article |
| id | ntu-10356/163761 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:53:38Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1637612022-12-16T01:25:53Z Transfer learning for wireless networks: a comprehensive survey 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 School of Computer Science and Engineering Engineering::Computer science and engineering Caching Cognitive Radios 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. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported in part by the Australian Government through the Australian Research Council's Discovery Projects Funding Scheme under Project DE210100651 (by Dr. Hoang Dinh); in part by the Joint Technology and Innovation Research Centre-a partnership between the University of Technology Sydney and the VNU Ho Chi Minh City University of Technology (VNU HCMUT); in part by the Programme DesCartes-the National Research Foundation, Prime Minister's Office, Singapore, through its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and its Emerging Areas Research Projects (EARP) Funding Initiative; in part by the Alibaba Group through the Alibaba Innovative Research (AIR) Program and the Alibaba-NTU Singapore Joint Research Institute (JRI); in part by the National Research Foundation, Singapore, through the AI Singapore Programme (AISG) under Grant AISG2-RP-2020-019; and in part by the Singapore Ministry of Education (MOE) Tier 1 under Grant RG16/20. The work of Cong T. Nguyen was supported in part by Vingroup JSC and in part by the Master, PhD Scholarship Programme of the Vingroup Innovation Foundation (VINIF), Institute of Big Data, Code 2021.TS.006. The work of Quoc-Viet Pham and Won-Joo Hwang was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant NRF-2019R1C1C1006143 and Grant NRF-2019R1I1A3A01060518, in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government (MSIT) under Grant 2020-0-01450 [Artificial Intelligence Convergence Research Center (Pusan National University)], and in part by the BK21 Four, Korean Southeast Center for the 4th Industrial Revolution Leader Education. 2022-12-16T01:25:53Z 2022-12-16T01:25:53Z 2022 Journal Article 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. https://dx.doi.org/10.1109/JPROC.2022.3175942 0018-9219 https://hdl.handle.net/10356/163761 10.1109/JPROC.2022.3175942 2-s2.0-85131757709 8 110 1073 1115 en AISG2-RP-2020-019 RG16/20 Proceedings of the IEEE © 2022 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Caching Cognitive Radios 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 Transfer learning for wireless networks: a comprehensive survey |
| title | Transfer learning for wireless networks: a comprehensive survey |
| title_full | Transfer learning for wireless networks: a comprehensive survey |
| title_fullStr | Transfer learning for wireless networks: a comprehensive survey |
| title_full_unstemmed | Transfer learning for wireless networks: a comprehensive survey |
| title_short | Transfer learning for wireless networks: a comprehensive survey |
| title_sort | transfer learning for wireless networks a comprehensive survey |
| topic | Engineering::Computer science and engineering Caching Cognitive Radios |
| url | https://hdl.handle.net/10356/163761 |
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