# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Results
#### 4.2.1 Short Range Communications
The UAV realizes on-demand coverage and maintains a stable network topology under the influence of three relative distances by sensing the uncovered area of the ground, the neighbouring UAVs, and the location of the coverage boundary or obstacles. Finally, ref. [57] also considers a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. They address the problem of maximizing the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power, and bandwidth allocation under each user's statistical QoS requirement constraint. Nevertheless, none of the previous studies have dealt with multiuser bandwidth assignment jointly with multiple UAVs' mobility and energy budget in air-ground collaborative networks, which are critically important for the cooperation between terrestrial base stations and UAVs. Driven by this concern, ref. [51] focuses on the resource allocation optimization of UAV-enabled air-ground collaborative emergency networks, aiming at the maximization of signal rate of all users. In [40], the novelty of the authors' work is to optimally place available UAVs in 3D space to meet the objectives prominent during emergency situations. The objectives considered here are coverage, QoS, energy consumption, and two newly characterized objectives, i.e., equal load distribution over UAVs and fault tolerance for improving network connectivity and lifetime. In a similar line, ref. [50] proposes a joint 3D positioning and power allocation algorithm based on Reinforcement Learning (RL), more specifically Q-learning. Assuming an area in which a catastrophe has occurred, destroying the previous wireless communication infrastructure, the aim is to deploy a flexible and efficient local area Emergency Communication Network based on Drone Small Cells (DSCs). The proposed solution determines the more appropriate position and transmit power of each DSC to minimize overall user outage, therefore improving the network performance. Ref. [53] also presents a drones-based resilient communication infrastructure based on DSCs. The system not only addresses the ad-hoc on-demand formation of cells to re-establish communications but also optimizes the communication MAC layer based on priority and delay minimizations, with a clear focus on emergency communication needs. From a completely distinct perspective, ref. [34] proposes to integrate a Software Defined Network into disaster emergency UAVs networks to realize the flexible deployment and management of high dynamic disaster area networks. In this case, the main challenge is to increase network lifetime and reduce node switching time and power consumption. The work intends to start from reducing the signalling overhead between controllers to further save system resources and improve the lifetime of the emergency disaster rescue UAV network. In their method, a multi-controller cluster drone architecture is used to create a backbone network with variable topology, which can provide long-term stable network coverage service in disaster emergency communication network construction.