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Abstract
Recently, the operation of LTE in unlicensed bands has been proposed to cope with the ever-increasing mobile traffic demand. However, the deployment of LTE in such bands implies sharing spectrum with mature technologies such as Wi-Fi. Several studies have discussed this coexistence problem by suggesting that LTE implements different adaptation mechanisms that allow transmission possibilities to Wi-Fi. While such adaptation mechanisms exist, they still negatively impactWi-Fi performance, mainly due to the lack of collaboration/coordination mechanisms that inform about the co-located networks' activities. In this paper, we propose a distributed spectrum management framework that enhances the performance of Wi-Fi, as a particular case, by detecting harmful co-located wireless networks and changes the Wi-Fi's operating central frequency to avoid them. The framework is based on a Convolutional Neural Network (CNN) that can identify different wireless technologies and provides spectrum usage statistics. Experiments were carried out in a real-life testbed, and the results show that Wi-Fi maintains its performance when using our framework. This translates in an increase of at least 40% on the overall throughput compared to a non-managed operation of Wi-Fi.
Keywords
Spectrum sharing, Machine learning, Cognitive radios, Coexistence, Experimental demonstration, LTE, SPECTRUM

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MLA
Soto, Paola, et al. “Augmented Wi-Fi : An AI-Based Wi-Fi Management Framework for Wi-Fi/LTE Coexistence.” 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), edited by C. Cetinkaya et al., IEEE, 2020, pp. 1–9, doi:10.23919/cnsm50824.2020.9269064.
APA
Soto, P., Camelo, M., Fontaine, J., Girmay, M. T., Shahid, A., Maglogiannis, V., … Latré, S. (2020). Augmented Wi-Fi : an AI-based Wi-Fi management framework for Wi-Fi/LTE coexistence. In C. Cetinkaya, M. S. Kim, S. Clayman, M. Sayit, M. Ulema, & N. ZincirHeywood (Eds.), 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) (pp. 1–9). online: IEEE. https://doi.org/10.23919/cnsm50824.2020.9269064
Chicago author-date
Soto, Paola, Miguel Camelo, Jaron Fontaine, Merkebu Tekaw Girmay, Adnan Shahid, Vasilis Maglogiannis, Eli De Poorter, Ingrid Moerman, Juan F. Botero, and Steven Latré. 2020. “Augmented Wi-Fi : An AI-Based Wi-Fi Management Framework for Wi-Fi/LTE Coexistence.” In 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), edited by C. Cetinkaya, M.S. Kim, S. Clayman, M. Sayit, M. Ulema, and N. ZincirHeywood, 1–9. IEEE. https://doi.org/10.23919/cnsm50824.2020.9269064.
Chicago author-date (all authors)
Soto, Paola, Miguel Camelo, Jaron Fontaine, Merkebu Tekaw Girmay, Adnan Shahid, Vasilis Maglogiannis, Eli De Poorter, Ingrid Moerman, Juan F. Botero, and Steven Latré. 2020. “Augmented Wi-Fi : An AI-Based Wi-Fi Management Framework for Wi-Fi/LTE Coexistence.” In 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), ed by. C. Cetinkaya, M.S. Kim, S. Clayman, M. Sayit, M. Ulema, and N. ZincirHeywood, 1–9. IEEE. doi:10.23919/cnsm50824.2020.9269064.
Vancouver
1.
Soto P, Camelo M, Fontaine J, Girmay MT, Shahid A, Maglogiannis V, et al. Augmented Wi-Fi : an AI-based Wi-Fi management framework for Wi-Fi/LTE coexistence. In: Cetinkaya C, Kim MS, Clayman S, Sayit M, Ulema M, ZincirHeywood N, editors. 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM). IEEE; 2020. p. 1–9.
IEEE
[1]
P. Soto et al., “Augmented Wi-Fi : an AI-based Wi-Fi management framework for Wi-Fi/LTE coexistence,” in 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), online, 2020, pp. 1–9.
@inproceedings{8684903,
  abstract     = {{Recently, the operation of LTE in unlicensed bands has been proposed to cope with the ever-increasing mobile traffic demand. However, the deployment of LTE in such bands implies sharing spectrum with mature technologies such as Wi-Fi. Several studies have discussed this coexistence problem by suggesting that LTE implements different adaptation mechanisms that allow transmission possibilities to Wi-Fi. While such adaptation mechanisms exist, they still negatively impactWi-Fi performance, mainly due to the lack of collaboration/coordination mechanisms that inform about the co-located networks' activities. In this paper, we propose a distributed spectrum management framework that enhances the performance of Wi-Fi, as a particular case, by detecting harmful co-located wireless networks and changes the Wi-Fi's operating central frequency to avoid them. The framework is based on a Convolutional Neural Network (CNN) that can identify different wireless technologies and provides spectrum usage statistics. Experiments were carried out in a real-life testbed, and the results show that Wi-Fi maintains its performance when using our framework. This translates in an increase of at least 40% on the overall throughput compared to a non-managed operation of Wi-Fi.}},
  author       = {{Soto, Paola and Camelo, Miguel and Fontaine, Jaron and Girmay, Merkebu Tekaw and Shahid, Adnan and Maglogiannis, Vasilis and De Poorter, Eli and Moerman, Ingrid and Botero, Juan F. and Latré, Steven}},
  booktitle    = {{2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM)}},
  editor       = {{Cetinkaya, C. and Kim, M.S. and Clayman, S. and Sayit, M. and Ulema, M. and ZincirHeywood, N.}},
  isbn         = {{9783903176317}},
  issn         = {{2165-9605}},
  keywords     = {{Spectrum sharing,Machine learning,Cognitive radios,Coexistence,Experimental demonstration,LTE,SPECTRUM}},
  language     = {{eng}},
  location     = {{online}},
  pages        = {{1--9}},
  publisher    = {{IEEE}},
  title        = {{Augmented Wi-Fi : an AI-based Wi-Fi management framework for Wi-Fi/LTE coexistence}},
  url          = {{http://dx.doi.org/10.23919/cnsm50824.2020.9269064}},
  year         = {{2020}},
}

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