# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 2 UAV Serving Ground Users  
An underlying network architecture for UAVs is presented in [15]. It describes and defines two categories of links: The control and non-payload (CNPL) and the data links. The first one should provide a full-duplex, low latency, high reliability and secure connections; usually with low data rates, for the usage of the UAV. Whereas the latter one is highly dependent on the role of the UAV. In this architecture, neither access nor backhaul links are described. When the application is using UAVs to aid wireless networks, they are called UABS. In this case, these links are divided into three different types: Access, relay and backhaul. The problem of using UAVs to support ground communications in disaster scenarios has been covered by diverse authors; however, their relation and constraints with the backhaul are not widely explored. [PERSON] et al. present in [16] one of the most extensive reviews of UAV in wireless networks. It covers various potential application including disaster scenarios, 5G BSs, mmWave communications, relaying and Internet of Things (IoT) applications. [17] proposes a mathematical framework for 3D cellular network based on UABS. Instead, [PERSON] et al. collected in [18] a detailed survey into the network-side challenges including physical, data link and network layers from the perspective of UAV networks. [PERSON] et al. in [10] present a taxonomy classification of location optimization solutions, which includes a study of 124 papers related to UABS. Its taxonomy comprises parameters of how the location of the UABS is done statically or dynamically, number of UABS, among others. The backhaul constraints are considered only in static scenarios with exact or PSH solutions. In [19], [PERSON] et al. describe the potential and challenges of using UAV as users of the cellular networks and includes using it to support backhaul network in case of a disaster. Different approaches are discussed from a 2D joint trajectory and scheduling optimization method for only one drone up to multiple relaying users equipment (UEs) to extend the coverage area. However, the backhaul is considered only between the core network and a disconnected BS by the usage of multiple UAVs that do not serve users [7]. Reference [20] presents a method to deploy and optimize UABS locations based in a distributed optimization model, to maximize coverage quality, serving time and minimize interference influence. [PERSON] et al. present a 2D trajectory design using a tradeoff model between delay, power and throughput, concluding that the average throughput increased as the speed of the UAV increases at expenses of the delay [21]. Reference [22] introduces a 3D location mixed-integer non-linear programming algorithm for only one UABS being served by a terrestrial macro-cell network as a backhaul using 2 GHz frequency. [PERSON] et al. [14] present a study of multiple UABSs 3D placements for serving users maximizing the coverage area; however, it does not include backhaul limitations. In [9], [PERSON] et al. propose a deployment tool to investigate network performance during a disaster, but it does not account for the backhaul. Similar, [8] presents a public safety heterogeneous network with two tiers, the microcell and the UAV network, and their backhaul links are considered to be infinite. [PERSON] et al. in [13] present the first known trial of a UAV serving users using WiFi resource management. They evaluate the impact of motion, antenna directivity and resource allocation in the experiment. Lime Microsystems demonstrate a 3G call for a drone-mounted lightweight base station with a weight less than 2 kg [23]. In [12], the results of a UAV relaying a user using an existing long term evolution (LTE) network are presented. The increase in user throughput when the flying relay is in operation is shown.