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Communication Dans Un Congrès Année : 2020

Machine Learning models for the prediction of Wi-Fi links performance using a CityLab testbed

Résumé

The Wi-Fi links performance depends in a highly complex way on the actual topology, channel qualities, spectral configurations, etc. Existing Wi-Fi radio link performance models usually adopt explicit and bottom-up approaches in order to predict throughput figures bawd on Markov chains and SINR levels. In this work we have validated a new approach for predicting the performance of Wi-Fi networks. Based on data measurements from the outdoor Wi-Fi CityLab testbed in Antwerp we have tested four different supervised learning algorithms. We observed that abstract "black box" models built using supervised machine learning techniques — without any deep knowledge of the complex interference dynamics of IEEE 802.11 networks — can estimate the link throughput with very good accuracy, reaching a value of R2-score of 90% for the case of the Gradient Boosting Regressor.
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Dates et versions

hal-02495164 , version 1 (01-03-2020)

Identifiants

  • HAL Id : hal-02495164 , version 1

Citer

Paulo Marques. Machine Learning models for the prediction of Wi-Fi links performance using a CityLab testbed. 8th International Workshop on ADVANCEs in ICT Infrastructures and Services (ADVANCE 2020), Candy E. Sansores, Universidad del Caribe, Mexico, Nazim Agoulmine, IBISC Lab, University of Evry - Paris-Saclay University, Jan 2020, Cancún, Mexico. pp.1--8. ⟨hal-02495164⟩
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