Association optimization based on access fairness for Wi-Fi networks

Densification of Wi-Fis networks has led to the possibility for a wireless station to choose between several access points (APs), improving coverage, wireless link quality and mobility. But densification of APs may generate interference, contention and decrease the global throughput as these APs have to share a limited number of channels. The recent trend in which Wi-Fi networks are managed in a centralized way offers the opportunity to alleviate this problem through a global optimization of the resource usage. In particular, optimizing the association step between APs and stations can increase the overall throughput and fairness between stations. In this paper, we propose an original solution to this optimization problem based on two contributions. First, we propose a mathematical model to evaluate and forecast the throughput obtainable for each station for a given association. The best association is then defined as the one that maximizes a logarithmic utility function using the stations' throughputs predicted by the model. The use of a logarithmic utility function allows to achieve a good trade-off between overall throughput and fairness. A heuristic based on a local search algorithm is used to propose approximate solutions to this optimization problem. It relies on a suitable neighborhood structure between associations. This approach has the benefit to be tuned according to the CPU and time constraints of the WLAN controller. A comparison between different heuristic versions and the optimum solution shows that the proposed heuristic offers solutions very close to the optimum with a significant gain of time. We also evaluated our solution with the simulator NS-3 on a large set of scenarios and configurations. It empirically demonstrates that our proposal improves the overall throughput and the fairness of the network.