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A survey on machine learning-based performance improvement of wireless networks : PHY, MAC and Network Layer

(2021) ELECTRONICS. 10(3).
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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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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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