Online channel selection and user association in high-density WiFi networks

In this paper, we consider the emerging deployment of WiFi networks in sports and entertainment venues characterized by high-density, large capacity, and real-time service delivery. Due to extremely high user density, channel allocation and user association should be carefully managed so that cochannel inference can be mitigated. To this end, we propose a channel selection and user association (CSUA) solution based on the Adversarial Multi-armed Bandit (AMAB) framework, which captures not only the uncertainty of channel states, but also the selfishness of individual stations (STAs) and access points (APs). An exponentially weighted average strategy is adopted to design an online algorithm for this problem, which is guaranteed to converge to a set of correlated equilibria with vanishing regrets. Simulation results show the convergence of the proposed algorithm and its performance under different settings.