  In this paper, we develop a hierarchical Bayesian game framework for
automated dynamic offset selection. Users compete to maximize their throughput
by picking the best locally serving radio access network (RAN) with respect to
their own measurement, their demand and a partial statistical channel state
information (CSI) of other users. In particular, we investigate the properties
of a Stackelberg game, in which the base station is a player on its own. We
derive analytically the utilities related to the channel quality perceived by
users to obtain the equilibria. We study the Price of Anarchy (PoA) of such
system, where the PoA is the ratio of the social welfare attained when a
network planner chooses policies to maximize social welfare versus the social
welfare attained in Nash/Stackeleberg equilibrium when users choose their
policies strategically. We show by means of a Stackelberg formulation, how the
operator, by sending appropriate information about the state of the channel,
can configure a dynamic offset that optimizes its global utility while users
maximize their individual utilities. The proposed hierarchical decision
approach for wireless networks can reach a good trade-off between the global
network 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.
