A Distributed Access Point Selection Algorithm Based on No-Regret Learning for Wireless Access Networks

The proliferation of wireless access technologies offers users the possibility of choosing among multiple available wireless access networks to connect to. This paper focuses on such network selection problem in the context of IEEE 802.11 WLANs where several access points provide connection service to users. We formulate this problem as a non-cooperative game where each user tries to maximize its utility function, defined as the throughput reward minus the fee charged by the access point. We then conduct a systematic analysis on the formulated game and develop an access point selection algorithm based on no-regret learning to orient the system converges to an equilibrium state (correlated equilibrium). The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as wireless access networks. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm in achieving high system efficiency.