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.

Latency-aware Resource Management at the Edge

“…This is also the case in the work by Li et al [103]. Mobile edge devices have, to the best of our knowledge not been considered for edge servers, but Plachy et al [104] showed that mobile (in that particular case flying) base stations are beneficial for increasing the data rate a user can get.…”

Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

“…Our proposed solution for UAV-assisted IAB networks is significantly different compared to the studies in [13], [14], [19], [43], [48]. In particular, we consider the mutual dependence between backhaul, direct and access transmissions, inter-cell interference and the mutual dependence between the spatial configurations of UAVs and the spatial dynamics of ground user distribution, which are significantly challenging in UAV-based cellular scenarios.…”

Energy Efficient Resource Allocation in UAV-based Heterogeneous Networks

“…After adjusting (11c) and solving the URLLC power allocation in (13), the remaining power at each BS for the eMBB users is ρ j − k ∈Ku P * jk . The user association and power allocation problem for eMBB users is given as follows:…”