A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer
- Author
- Merima Kulin, Tarik Kazaz, Eli De Poorter (UGent) and Ingrid Moerman (UGent)
- Organization
- Abstract
- This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.
- Keywords
- CLASSIFICATION, machine learning, data science, deep learning, protocol layers, MAC, PHY, AI, performance optimization
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8700401
- MLA
- Kulin, Merima, et al. “A Survey on Machine Learning-Based Performance Improvement of Wireless Networks : PHY, MAC and Network Layer.” ELECTRONICS, vol. 10, no. 3, 2021, doi:10.3390/electronics10030318.
- APA
- Kulin, M., Kazaz, T., De Poorter, E., & Moerman, I. (2021). A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer. ELECTRONICS, 10(3). https://doi.org/10.3390/electronics10030318
- Chicago author-date
- Kulin, Merima, Tarik Kazaz, Eli De Poorter, and Ingrid Moerman. 2021. “A Survey on Machine Learning-Based Performance Improvement of Wireless Networks : PHY, MAC and Network Layer.” ELECTRONICS 10 (3). https://doi.org/10.3390/electronics10030318.
- Chicago author-date (all authors)
- Kulin, Merima, Tarik Kazaz, Eli De Poorter, and Ingrid Moerman. 2021. “A Survey on Machine Learning-Based Performance Improvement of Wireless Networks : PHY, MAC and Network Layer.” ELECTRONICS 10 (3). doi:10.3390/electronics10030318.
- Vancouver
- 1.Kulin M, Kazaz T, De Poorter E, Moerman I. A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer. ELECTRONICS. 2021;10(3).
- IEEE
- [1]M. Kulin, T. Kazaz, E. De Poorter, and I. Moerman, “A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer,” ELECTRONICS, vol. 10, no. 3, 2021.
@article{8700401, abstract = {{This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.}}, articleno = {{318}}, author = {{Kulin, Merima and Kazaz, Tarik and De Poorter, Eli and Moerman, Ingrid}}, issn = {{2079-9292}}, journal = {{ELECTRONICS}}, keywords = {{CLASSIFICATION,machine learning,data science,deep learning,protocol layers,MAC,PHY,AI,performance optimization}}, language = {{eng}}, number = {{3}}, pages = {{64}}, title = {{A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer}}, url = {{http://doi.org/10.3390/electronics10030318}}, volume = {{10}}, year = {{2021}}, }
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