Dynamic Bayesian Game Based Power Allocation in Mobile Edge Computing with Users’ Behaviors

Abstract

In this paper, we investigate the power allocation problem based on users’ behaviors in mobile edge computing (MEC) networks. Due to the information limitation about the behavior types of mobile users, the power allocation is quite challenging. To deal with this problem, we propose a dynamic Bayesian game based power allocation algorithm to maximize the utility function of MEC network. In Bayesian game model, the MEC server imposes a price per unit power on mobile users. And the MEC server modified the price based on the Bayesian probability information about the behaviors of the users. The mobile users dynamically adapt the behavior types and the required power according to their own performance and cost. We investigate the existence of Bayesian Nash equilibrium and simulation studies are carried out to demonstrate the effectiveness of the proposed algorithm.