Joint positioning of flying base stations and association of users: Evolutionary-based approach

Abstract

Time-varying requirements of users on communication push mobile operators to increase density of base stations. However, the dense deployment of conventional static base stations (SBSs) is not always economical, for example, when periods of peak load are short and infrequent. In such cases, several flying base stations (FlyBSs) mounted on unmanned aerial vehicles can be seen as a convenient substitution for the dense deployment of SBSs. This paper focuses on maximization of user satisfaction with provided data rates. To this end, we propose an algorithm that associates users with the most suitable SBS/FlyBS and finds optimal positions of all FlyBSs. Furthermore, we investigate the performance of two proposed approaches for the joint association and positioning based on the genetic algorithm (GA) and particle swarm optimization (PSO). It is shown that both solutions improve the satisfaction of users with provided data rates in comparison with a competitive approach. We also demonstrate trade-offs between the GA and the PSO. While the PSO is of lower complexity than the GA, the GA requires a slightly lower number of active FlyBSs to serve the users.