  In this paper, a novel cluster-based approach for maximizing the energy
efficiency of wireless small cell networks is proposed. A dynamic mechanism is
proposed to group locally-coupled small cell base stations (SBSs) into clusters
based on location and traffic load. Within each formed cluster, SBSs coordinate
their transmission parameters to minimize a cost function which captures the
tradeoffs between energy efficiency and flow level performance, while
satisfying their users' quality-of-service requirements. Due to the lack of
inter-cluster communications, clusters compete with one another in order to
improve the overall network's energy efficiency. This inter-cluster competition
is formulated as a noncooperative game between clusters that seek to minimize
their respective cost functions. To solve this game, a distributed learning
algorithm is proposed using which clusters autonomously choose their optimal
transmission strategies based on local information. It is shown that the
proposed algorithm converges to a stationary mixed-strategy distribution which
constitutes an epsilon-coarse correlated equilibrium for the studied game.
Simulation results show that the proposed approach yields significant
performance gains reaching up to 36% of reduced energy expenditures and up to
41% of reduced fractional transfer time compared to conventional approaches.
