# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 1 Introduction
The authors proposed a downlink coverage performance characterization framework to optimize a cellular network serving both aerial user equipment (AUE) and terrestrial user equipment. This framework considers the impact of fundamental design parameters such as base station height, antenna pattern and UAV altitude for different types of environments. They found that an optimal UAV altitude with respect to coverage probability does exist. Follow-up research in [8] analyzed the performance of a cellular network using closed form expressions and found network design trade-offs to optimize the cellular network for both ground users and UAVs. Their methods however are purely analytical and do not allow estimating performance of an existing network in a real scenario. Several approaches to overcome the interference problem are suggested in the literature. The work in [9] developed an interference-aware path planning scheme for aerial UEs that yields to a minimum communication latency for UAVs as well as their interference on ground users. In [10], it was proposed to transfer information over a satellite link as an addition to the cellular connection. Some other techniques attract attention of the research community: Massive Multiple Input Multiple Output (MaMIMO) and analog beamforming could be major technologies when trying to include UAVs in future cellular networks. Moreover, in upcoming 5G networks, millimeter wave (mmWave) cellular networks will play a big role in providing higher data rates and even more expanded coverage [11]. It is well known that mmWave links are much more vulnerable to blockages; however, it has been shown in all mentioned papers that UAVs have higher probability to have no obstacles between aerial and ground terminals. The other known problem of propagation at high frequencies is high attenuation of the signal, which may be overcome by using advanced antenna techniques giving large array gains. The combination of the beamforming and mmWave for UAV applications was analyzed in [12]. The small antenna aperture size of mmWave systems allows the deployment of large antenna arrays. These large arrays support the usage of analog and digital beamforming and MaMIMO techniques. These techniques allow more targeted use of the spectrum, effectively serving multiple users without creating interference to their neighbors, resulting in mmWave cellular systems being noise limited rather than interference limited unlike LTE cellular systems [13; 14]. Potentially, the combination of these technologies will be able provide a solution to the congestion of the spectrum for UAVs. Published research considers rural environments for measurements. The simulation based works rely on modeling of various Manhattan-like urban areas or on stochastic channel and LOS probability models. We believe that more realistic urban models must be included in the analysis since the propagation conditions are highly dependent on the environment. Moreover, using UAVs in a realistic European middle-sized city has not been studied. Initial studies of mmWave communication with UAVs have not answered several important questions:  
* From what altitude onwards can we assume LOS communication in an urban environment? * Is mmWave communication superior to LTE for providing coverage to UAVs, as it enables networks with less interference? * Can we have a good aerial coverage at mmWave frequencies using the same base station density (as claimed in [15]), or is it necessary to deploy many more cells? * As mmWave promises cells with more directive antennas, how directional should the antenna array be for optimizing aerial coverage? In this article, we investigate these key challenges in mmWave UAV-enabled cellular networks and discuss possible solutions. To answer the questions above, we perform simulations in a 3D environment of real world locations. In the paper, this environment is statistically characterized and we discuss the possible mmWave semi-deterministic channel models.