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

TL;DR

This paper models user access mode selection in fog radio access networks using evolutionary game theory, demonstrating that the proposed algorithm outperforms traditional max rate methods in terms of payoff.

Contribution

It introduces an evolutionary game framework for access mode selection in F-RANs and derives analytical payoff expressions considering network heterogeneity.

Findings

The evolutionary game approach achieves higher payoffs than max rate algorithms.

Simulation results validate the effectiveness of the proposed access mode selection method.

The model accounts for node locations, cache sizes, and delay costs.

Abstract

The fog radio access network (F-RAN) is a promising paradigm for the fifth generation wireless communication systems to provide high spectral efficiency and energy efficiency. Characterizing users to select an appropriate communication mode among fog access point (F-AP), and device-to-device (D2D) in F-RANs is critical for performance optimization. Using evolutionary game theory, we investigate the dynamics of user access mode selection in F-RANs. Specifically, the competition among groups of potential users space is formulated as a dynamic evolutionary game, and the evolutionary equilibrium is the solution to this game. Stochastic geometry tool is used to derive the proposals' payoff expressions for both F-AP and D2D users by taking into account the different nodes locations, cache sizes as well as the delay cost. The analytical results obtained from the game model are evaluated via simulations, which show that the evolutionary game based access mode selection algorithm can reach a much higher payoff than the max rate based algorithm.