Dynamic Clustering and ON/ OFF Strategies for Wireless Small Cell Networks

Abstract— A novel cluster-based approach for maximizing t he 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 location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost f unction, which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users’ quality-of-service requirements. Due to the 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 t his game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown t hat t he 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 upto 41% of reduced fractional transfer time compared to conventional approaches. < final year projects >