# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 1 Introduction
In [23], the authors proposed a new analysis model to study the key factors affecting UAV power consumption. In [24], the authors proposed a joint power allocation and deployment scheme in UAV-based IoT networks, aiming to optimize the deployment locations and power allocation of UAVs by maximizing network coverage and minimizing network interference. In [25], the authors proposed an adaptive UAV deployment scheme with the objective of optimizing UAV positions to cover as many navigation grids as possible while reducing communication energy consumption. In [26], the authors analyzed the effects of wind speed, wind direction, and turbulence on the endurance of UAVs. In [27], the authors studied the use of hybrid precoding to reduce hardware complexity and energy consumption in UAVs. Applying the meta-heuristic optimization algorithm to solve the UAV deployment optimization problem can simplify the solution process, improve the deployment efficiency, and optimize the deployment effectiveness. The most common meta-heuristic methods include real-coded genetic algorithm (RCGA-rdn) [28], water strider algorithm (WSA) [29], thermal exchange optimization algorithm (TEO) [30], and so on. Moreover, there exist several intriguing research directions. In [31], the authors presented a UAV emergency communication system architecture based on 5G and its subsequent technologies. This system exploits UAVs as communication relay nodes to provide communication services within disaster-stricken areas. In [32], the authors proposed a relay deployment and network optimization framework based on non-orthogonal multiple access. The framework aims to maximize network coverage and service quality by optimizing the deployment locations and power allocation of relay nodes. In [33], the authors considered a facility location problem with drones. In [34], the authors proposed a new UAV or UAV platform that can connect a network of sensors and actuators on demand. In [35], the authors proposed a base station interference management method for UAVs based on affinity propagation and machine learning. In [36], the authors proposed a lightweight, privacy-preserving protocol for UAV internet environments. In [37], the authors developed a multi-UAV cooperative search model (MCSM) with communication cost and formation benefit as an optimization function to ensure the effectiveness of multi-UAV search. The above research focuses on solving the problems of UAV energy consumption and UAV communication effect but does not consider the efficiency of UAV deployment. In view of the urgency and timeliness of disaster emergency communication, we design the rapid deployment method for UAV base stations which is suitable for small areas and large areas, respectively. Then, according to different disaster scenarios, the launch power and deployment height of the UAVs are dynamically adjusted to provide emergency communications for different disaster scenarios. The main contributions of this paper are as follows:  
* First, we decouple the UAV deployment problem for disaster emergency communication into two sub-problems: horizontal deployment and height regulation. We extract the horizontal deployment problem into the solution of UAV coverage rate and connectivity rate and calculate the optimal horizontal deployment coordinates of the UAV base stations, which effectively improves the deployment speed of UAV base stations after disasters. Then, the transmitting power and deployment height of the UAV base station are adjusted according to the channel model of urban, suburban, and rural environments and the distribution characteristics of the users waiting for rescue on the ground; in this way, energy-saving communication for the UAV base station is realized effectively. * Secondly, we proposed the small-area UAV deployment improved-Broyden-Fletcher-Goldfarb-Shanno algorithm (SAIBFGS) to solve the UAV two-dimensional deployment problem for small-scale disaster scenarios, which reduces the complexity of the algorithm by improving the iterative step size and search direction.