Optimization Strategies for UAV-BS Positioning in Disaster Scenarios

The rapid restoration of communication services is crucial in natural disaster scenarios. Unmanned Aerial Vehicle Base Stations (UAV-BSs) have emerged as a promising solution for providing connectivity where ground infrastructure is compromised. A key aspect of this approach is the three-dimensional (3D) positioning of UAV-BSs, which involves optimizing not only the geographic location but also the deployment altitude. This additional degree of freedom makes it possible to mitigate propagation challenges such as fading and interference more effectively. Previous studies have addressed this problem by considering a single deployment metric such as coverage, energy efficiency, or throughput. However, real-world situations demand the satisfaction of multiple, often conflicting, objectives, which significantly increases the computational complexity of finding an optimal solution. To address this gap, this work tackles the multi-objective optimization problem of maximizing user coverage and network throughput. To this end, we propose two optimization algorithms: nsgaUAV, based on the NSGA-II genetic algorithm, and a simpler heuristic, greedyUAV. The simulation results demonstrate that by strategically adjusting the 3D positions of the UAV-BSs, the proposed approaches achieve substantial performance gains. Specifically, they deliver a network throughput of up to 240 Mbps, ensure a user service rate of 98%, and effectively determine the optimal number of UAV-BSs required for efficient coverage of the disaster area.