Deployment Optimization of UAV-Aided Networks Through a Dynamic Tunable Model

In the existing work of unmanned aerial vehicle base station (UAV-BS) deployment, where serving radius (SR) is fixed regardless of resource waste and overlapping interference, dynamic tunability of SR has not been considered. In this letter, a dynamic tunable model where SR can be properly adjusted is proposed, based on which a semi-progressive offloading deployment scheme devoted to UAV number and overlapping interference optimization is raised. Specifically, UAV is deployed around macro base stations (MBSs), where a certain proportion ( $\gamma $ ) of the ground terminals (GTs) are covered first with the principle of minimum overlap and maximum coverage, then through tunability of SR, the rest GTs are covered without increasing UAV number. Moreover, the base station expands its SR to cover the closest uncovered GT through fine adjustment of power (MBS) or antenna down-tilt (UAV), after which surrounding UAVs are also adjusted to reduce overlapping interference. The results show our proposed scheme reduces 17.26% UAV quantity compared with the cluster method and decreases 71.75% interference compared with the core-set method.