| Autor: |
Soto P; Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium.; Department of Telecommunications Engineering, Universidad de Antioquia, Medellín 050010, Colombia., Camelo M; Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium., Mets K; Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium., Wilhelmi F; Centre Tecnològic de Telecomunicacions de Catalunya, 08860 Castelldefels, Spain., Góez D; Department of Telecommunications Engineering, Universidad de Antioquia, Medellín 050010, Colombia., Fletscher LA; Department of Telecommunications Engineering, Universidad de Antioquia, Medellín 050010, Colombia., Gaviria N; Department of Telecommunications Engineering, Universidad de Antioquia, Medellín 050010, Colombia., Hellinckx P; Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium., Botero JF; Department of Telecommunications Engineering, Universidad de Antioquia, Medellín 050010, Colombia., Latré S; Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium. |
| Abstrakt: |
IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment's topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features. |
