# Energy-Efficient Deployment Simulator of UAV-Mounted Base Stations Under Dynamic Weather Conditions

## TL;DR

This paper introduces a simulator and an energy-efficient deployment method for UAV-mounted base stations that adapts to dynamic weather conditions.

## Contribution

The novel contribution is a hybrid ISA-PSO algorithm and a simulator that integrates geolocation and weather data for UAV-MBS deployment.

## Key findings

- The proposed deployment scheme achieves faster convergence and higher stability than conventional methods.
- The simulator integrates terrain and real-time weather data to produce practical deployment results.
- The hybrid algorithm effectively determines optimal 3D positions and transmission power for UAV-MBSs.

## Abstract

In unmanned aerial vehicle (UAV)-mounted base station (MBS) networks, user equipment (UE) experiences dynamic channel variations because of the mobility of the UAV and the changing weather conditions. In order to overcome the degradation in the quality of service (QoS) of the UE due to channel variations, it is important to appropriately determine the three-dimensional (3D) position and transmission power of the base station (BS) mounted on the UAV. Moreover, it is also important to account for both geographical and meteorological factors when deploying UAV-MBSs because they service ground UE in various regions and atmospheric environments. In this paper, we propose an energy-efficient UAV-MBS deployment scheme in multi-UAV-MBS networks using a hybrid improved simulated annealing–particle swarm optimization (ISA-PSO) algorithm to find the 3D position and transmission power of each UAV-MBS. Moreover, we developed a simulator for deploying UAV-MBSs, which took the dynamic weather conditions into consideration. The proposed scheme for deploying UAV-MBSs demonstrated superior performance, where it achieved faster convergence and higher stability compared with conventional approaches, making it well suited for practical deployment. The developed simulator integrates terrain data based on geolocation and real-time weather information to produce more practical results.