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Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum

Merkebu Tekaw Girmay (UGent) , Adnan Shahid (UGent) , Vasilis Maglogiannis (UGent) , Dries Naudts (UGent) and Ingrid Moerman (UGent)
(2021) IEEE ACCESS. 9. p.42959-42974
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Abstract
In the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not.
Keywords
Wireless fidelity, Long Term Evolution, 5G mobile communication, Wireless sensor networks, Wireless networks, Sensors, Analytical models, Wi-Fi saturation, traffic load estimation, coexistence, unlicensed spectrum, machine learning, convolutional neural networks

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MLA
Girmay, Merkebu Tekaw, et al. “Machine Learning Enabled Wi-Fi Saturation Sensing for Fair Coexistence in Unlicensed Spectrum.” IEEE ACCESS, vol. 9, 2021, pp. 42959–74, doi:10.1109/ACCESS.2021.3066052.
APA
Girmay, M. T., Shahid, A., Maglogiannis, V., Naudts, D., & Moerman, I. (2021). Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum. IEEE ACCESS, 9, 42959–42974. https://doi.org/10.1109/ACCESS.2021.3066052
Chicago author-date
Girmay, Merkebu Tekaw, Adnan Shahid, Vasilis Maglogiannis, Dries Naudts, and Ingrid Moerman. 2021. “Machine Learning Enabled Wi-Fi Saturation Sensing for Fair Coexistence in Unlicensed Spectrum.” IEEE ACCESS 9: 42959–74. https://doi.org/10.1109/ACCESS.2021.3066052.
Chicago author-date (all authors)
Girmay, Merkebu Tekaw, Adnan Shahid, Vasilis Maglogiannis, Dries Naudts, and Ingrid Moerman. 2021. “Machine Learning Enabled Wi-Fi Saturation Sensing for Fair Coexistence in Unlicensed Spectrum.” IEEE ACCESS 9: 42959–42974. doi:10.1109/ACCESS.2021.3066052.
Vancouver
1.
Girmay MT, Shahid A, Maglogiannis V, Naudts D, Moerman I. Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum. IEEE ACCESS. 2021;9:42959–74.
IEEE
[1]
M. T. Girmay, A. Shahid, V. Maglogiannis, D. Naudts, and I. Moerman, “Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum,” IEEE ACCESS, vol. 9, pp. 42959–42974, 2021.
@article{8703420,
  abstract     = {{In the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not.}},
  author       = {{Girmay, Merkebu Tekaw and Shahid, Adnan and Maglogiannis, Vasilis and Naudts, Dries and Moerman, Ingrid}},
  issn         = {{2169-3536}},
  journal      = {{IEEE ACCESS}},
  keywords     = {{Wireless fidelity,Long Term Evolution,5G mobile communication,Wireless sensor networks,Wireless networks,Sensors,Analytical models,Wi-Fi saturation,traffic load estimation,coexistence,unlicensed spectrum,machine learning,convolutional neural networks}},
  language     = {{eng}},
  pages        = {{42959--42974}},
  title        = {{Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum}},
  url          = {{http://dx.doi.org/10.1109/ACCESS.2021.3066052}},
  volume       = {{9}},
  year         = {{2021}},
}
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