This paper extends a framework for load balancing in large Wi-Fi networks we previously proposed. The framework partitions the network into clusters and assigns a sub-controller to each cluster. The sub-controllers employ a Deep Q-Learning-based algorithm to balance the load on the access points in the cluster. The sub-controllers collaborate by exchanging their training updates through a database. However, in highly dynamic Wi-Fi networks, the frequency of these exchanges may cause control plane overhead. In this paper, the training updates are uploaded only when their “quality” meets a condition dependent on a threshold. High threshold values reduce overhead but compromise local learning performance, and vice versa. Therefore, finding the optimal threshold values is formulated as a multi-objective optimization problem. Two metrics are designed to quantify the overhead and instability. These metrics are stochastic, scenario-dependent, and time-consuming to estimate. Randomly generated scenarios of large and dynamic 802.11ax networks are simulated to collect realizations of the metrics. The objectives are obtained by smoothing the realizations with Gaussian kernel regression. The problem is then solved with a genetic algorithm to estimate the Pareto-optimal threshold values.

Wireless local area networks (WLAN) and recent Wi-Fi standards, are used since a few decades for a large variety of network applications. Since a few years, the $5^{\text {th }}$ generation wireless cellular networks (5 G) are being deployed and used, most of the time for similar applications. Both technologies and their evolution, are expected to be deployed within the wireless ecosystem by 2030 and for decades beyond. The Wi-FIP project invokes these two wireless networking solutions and their evolution, to further enhance the wireless data networks regarding sustainable and scalable quality of hybrid network services. This paper presents the visions and objectives of the Wi-FIP project, targeting to enhance synergies and integration of Wi-Fi in 5G/beyond 5G networks, through the convergent B2B/B2C usage model concept, sustainable multi-connectivity, hierarchical Software Defined Networking (SDN) and orchestration. The paper analyzes the functionalities to develop for sustainable, secure and hybrid WLAN/cellular networking.

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 (MultiMDP) 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.

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.