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In this paper, we develop a hierarchical Bayesian game framework forautomated dynamic offset selection. Users compete to maximize their throughputby picking the best locally serving radio access network (RAN) with respect totheir own measurement, their demand and a partial statistical channel stateinformation (CSI) of other users. In particular, we investigate the propertiesof a Stackelberg game, in which the base station is a player on its own. Wederive analytically the utilities related to the channel quality perceived byusers to obtain the equilibria. We study the Price of Anarchy (PoA) of suchsystem, where the PoA is the ratio of the social welfare attained when anetwork planner chooses policies to maximize social welfare versus the socialwelfare attained in Nash/Stackeleberg equilibrium when users choose theirpolicies strategically. We show by means of a Stackelberg formulation, how theoperator, by sending appropriate information about the state of the channel,can configure a dynamic offset that optimizes its global utility while usersmaximize their individual utilities. The proposed hierarchical decisionapproach for wireless networks can reach a good trade-off between the globalnetwork performance at the equilibrium and the requested amount of signaling.Typically, it is shown that when the network goal is orthogonal to user's goal,this can lead the users to a misleading association problem.

Automated Dynamic Offset Applied to Cell Association