Description

An ovel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to locally group coupled small cell base stations ( SBSs) into clusters based on loca-tion and traffic load. Within each formed cluster, S BSs coordinate their t ransmission parameters to minimize a cost f unction, which captures the tradeoffs between energy efficiency and flow level per-formance, while satisfying their users’ quality-of-service require-ments. Due t o t he lack of intercluster communications, clusters compete with one another to improve the overall network’s energy efficiency. This intercluster competition is formulated as a n on-cooperative 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 t he proposed algorithm converges t o 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 upto 41% of reduced fractional transfer time compared t o conventional approaches.