Efficient and Fair Network Selection for Integrated Cellular and Drone-Cell Networks

Abstract

This paper is concerned with the network selection for heterogeneous networks. Although many network selection approaches have been developed for heterogeneous networks, few of them explore the network selection problem for integrated cellular and drone-cell networks. Furthermore, most of the existing designs may be unsuitable for the integrated cellular and drone-cell networks owing to the high dynamic drone-cell-user association and the relatively limited drone-cell network capacity. In this paper, we investigate a network selection problem for the integrated cellular and drone-cell networks with a goal of maximizing a proportional fairness function of time average utilities across users under coarse correlated equilibrium constraints and minimum time average utility constraints. To mitigate this challenging problem, we first convert it into a non-linear integer programming (NLIP) problem based on our derived theoretical results. Next, we propose a repeated-stochastic-game-based efficient and fair network selection (RSG-EF) algorithm to alleviate the NLIP problem by leveraging a linear approximation mechanism. Simulation results show that the RSG-EF algorithm can achieve the highest total utility and a high level of fairness across users compared with three benchmark algorithms.