# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 1 Introduction  
In recent decades, the frequency of all kinds of natural disasters has been increasing year by year, especially earthquakes, floods, and debris flows, which often bring incalculable losses to the affected areas [1]. In these disasters, the ground communication facilities in the accident area are often destroyed, which can make it difficult for information on the disaster situation in the accident area to be transmitted in time and makes the post-disaster relief work difficult to carry out effectively. Unmanned aerial vehicles (UAVs), which are characterized by their low cost, high flexibility, and high line-of-sight link probability [2], can be utilized to establish low altitude platforms (LAP) for emergency communication purposes [3], satisfying post-disaster communication needs [4; 5; 6; 7; 8]. However, given the urgency and timeliness of disasters, a UAV-assisted emergency communications network must be deployed in a timely manner [9], and the key challenges are the following. First, UAVs have limited computing power [10], and the dynamictracking of disaster areas to rescue ground users will consume a lot of computing resources, increasing the deployment time of UAVs. Second, the UAV's energy storage is limited [11]; the dynamic adjustment of the height of the UAV will further increase energy consumption, reducing the UAV emergency communications service time. A reasonable channel model can improve the deployment effect of UAVs, minimizing the path loss, expanding the coverage area, and reducing the deployment time of the UAVs. In [12], the authors assumed that UAVs are fixed in a two-dimensional plane without seeking a balance between deployment range and link loss. In [13], the authors derived the air-to-air channel model among UAVs while considering height factors. In [14], the authors incorporated the characteristics of the air-to-ground propagation channel, the impact of co-channel interference from other UAV base stations, and energy constraints of the UAV base stations. In [15], the authors proposed a framework for evaluating flight height and area coverage using a fixed number of drones and area dimensions. In [16], the authors presented a distributed deployment algorithm specifically designed for line-of-sight (LoS) scenarios which allowed UAV base stations to determine their motion based solely on local information, making it suitable for large area UAVs deployments. By integrating considerations of connectivity and coverage, the communication connectivity and area coverage challenges in the deployment of emergency communication UAVs can be effectively addressed, thereby enhancing the efficiency and accuracy of the emergency response. In [17], the authors proposed a three-dimensional UAV deployment scheme based on the improved genetic algorithm (IGA), which ensured the connectivity of the UAV network in both static and dynamic user scenarios. In [18], the authors investigated the maximum coverage deployment problem while maintaining connectivity conditions in the absence of user location information. In [19], the authors presented a combined approach using the steepest descent method and genetic algorithm to optimize the deployment scheme of wireless sensor nodes, aiming to achieve network connectivity while meeting coverage constraints. In [20], the authors proposed a graphic coalition formation game that combines UAV time-varying topology with a coalition formation game. To reduce the complexity of the UAV deployment problem, some scholars have pursued some interesting research. In [21], the authors took into consideration the complexity of 3D UAV deployment by decoupling the UAV deployment problem into vertical and horizontal dimensions. In [22], the authors demonstrated the existence of an optimal vertical height that achieves a maximum coverage range for UAV energy efficiency. Due to the limited endurance of UAVs, researchers have investigated the power allocation problem for UAVs.