DQL-MultiMDP: A Deep Q-Learning-Based Algorithm for Load Balancing in Dynamic and Dense WiFi Networks

In this paper, we present our primary version of DQL-MultiMDP, a flexible load balancing approach designed to ensure long-term user satisfaction in modern WiFi networks (WiFi 7 and beyond). The algorithm quasi-simultaneously solves multiple Markov Decision Processes (MDPs) by switching between them depending on the environmental configu-ration, specifically the number of Access Points (APs) and user Stations (STAs). Leveraging Deep Q-Learning (DQL), it utilizes sophisticated state representations with advanced metrics to determine the optimal AP-to-STA association through autonomous decision-making. Experimental results validate the effectiveness of the approach and lay the foundation for further improvements.