Learning methodologies for wireless big data networks: A Markovian game-theoretic perspective

Wireless big data significantly challenges the current network management and control architecture, mathematical modeling techniques, and distributed algorithm design, in particular, in the promising cognitive, distributed, and ultra-dense networks. Motivated by the idea of divide-and-conquer, in this article, we first present a multiple cognitive agent-based divide-and-conquer network management and control architecture. Furthermore, a Markovian game-theoretic modeling framework is proposed to model the state big data-based decision-making problem. Then, we investigate various learning methodologies with respect to different kinds of the state information, in particular, we concentrate on the construction of state space, the state transition computation, and the convergence of parallel Q-learning technique. This work provides a suitable network management architecture, an effective modeling tool, and various learning techniques for wireless big data networks.