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Communication Dans Un Congrès Année : 2024

Load Balancing in Large WiFi Networks Using DQL-MultiMDP with Constrained Clustering

Résumé

Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (Multi-MDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.

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Dates et versions

hal-04679924 , version 1 (28-08-2024)
hal-04679924 , version 2 (03-09-2024)

Identifiants

  • HAL Id : hal-04679924 , version 2

Citer

Mohamed Bellouch, Lynda Zitoune, Iyad Lahsen-Cherif, Véronique Vèque. Load Balancing in Large WiFi Networks Using DQL-MultiMDP with Constrained Clustering. MASCOTS 2024, Oct 2024, Krakow (Cracovie), Poland. ⟨hal-04679924v2⟩
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