Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM - 2018 PROJECT TITLE :Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM - 2018ABSTRACT:In this Project, tradeoff and convergence problems for incentive mechanisms are addressed by combining optimization and game theory. Specifically, a multiple-leader multiple-follower (MLMF) game-primarily based alternating direction method of multipliers (ADMM) is developed that incentivizes the agents to perform a cluster of controllers' tasks so as to satisfy their corresponding objectives. Each analytical and simulation results verify that the proposed methodology reaches a hierarchical social optimum and converges linearly. More importantly, the convergence rate is independent of the network size, that indicates that the MLMF game-primarily based ADMM will be employed in very large networks, like highly virtualized Communication networks with two-layer hierarchies, for giant information processing. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching - 2018 Global Energy Efficiency Optimization for Wireless-Powered Massive MIMO Aided Multiway AF Relay Networks - 2018