An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks

Abstract

The fog radio access network (F-RAN) is a promising paradigm to provide high spectral efficiency and energy efficiency. Characterizing users to select an appropriate communication mode in F-RANs is critical for performance optimization. With evolutionary game theory, a dynamic mode selection is proposed for F-RANs, in which the competition among the groups of potential users' space is formulated as a dynamic evolutionary game, and the game is solved by an evolutionary equilibrium. Stochastic geometry tool is used to derive the proposals' payoff expressions for both fog access point and device-to-device users by considering node location, cache sizes, as well as the delay cost. The analytical results for the proposed game model and the corresponding solution are evaluated, which show that the evolutionary game-based access mode selection algorithm has a better payoff than the max rate-based algorithm.