Min Gyeonghyeon, So Jaewoo
Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel). 2025 Jun 11;25(12):3648. doi: 10.3390/s25123648.
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.
在无人机搭载基站（MBS）网络中，由于无人机的移动性和不断变化的天气条件，用户设备（UE）会经历动态信道变化。为了克服由于信道变化导致的UE服务质量（QoS）下降，适当地确定安装在无人机上的基站（BS）的三维（3D）位置和发射功率非常重要。此外，在部署无人机MBS时考虑地理和气象因素也很重要，因为它们为不同区域和大气环境中的地面UE提供服务。在本文中，我们提出了一种在多无人机MBS网络中节能的无人机MBS部署方案，使用混合改进模拟退火-粒子群优化（ISA-PSO）算法来找到每个无人机MBS的3D位置和发射功率。此外，我们开发了一个用于部署无人机MBS的模拟器，该模拟器考虑了动态天气条件。所提出的无人机MBS部署方案表现出卓越的性能，与传统方法相比，它实现了更快的收敛和更高的稳定性，使其非常适合实际部署。所开发的模拟器集成了基于地理位置的地形数据和实时天气信息，以产生更实际的结果。
