  In this paper, the problem of cell association between small base stations
(SBSs) and users in dense wireless networks is studied using artificial
intelligence (AI) techniques. The problem is formulated as a mean-field game in
which the users' goal is to maximize their data rate by exploiting local data
and the data available at neighboring users via an imitation process. Such a
collaborative learning process prevents the users from exchanging their data
directly via the cellular network's limited backhaul links and, thus, allows
them to improve their cell association policy collaboratively with minimum
computing. To solve this problem, a neural Q-learning learning algorithm is
proposed that enables the users to predict their reward function using a neural
network whose input is the SBSs selected by neighboring users and the local
data of the considered user. Simulation results show that the proposed
imitation-based mechanism for cell association converges faster to the optimal
solution, compared with conventional cell association mechanisms without
imitation.
