# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 4 Application Prospects  
### Application to UAV-Aided Wireless Networks  
Future wireless networks will need to be much more flexible than in the past and they should be able to react smoothly and automatically to the fast time-space variations of any traffic demand. This can be achieved by moving part of the network infrastructure and tailor it according to actual traffic needs. Thanks to the ability to fly almost freely anywhere and whenever the highest needs arise, aerial platforms like Unmanned Aerial Vehicles (UAVs) become excellent candidates as nodes of future networks.  
Recent studies on UAVs and their use as Aerial Base Stations (ABSs) [34] show that they might be an efficient complement to traditional Terrestrial Base Stations (TBSs) especially in case of events such as concerts and sport events, traffic jams, and disaster-recovery applications [35].  
In order to properly deploy a heterogeneous network composed of both TBSs and ABSs, it is required to design and optimize the UAV trajectories so that the coverage is maximized, and the network should also be able to quickly reconfigure itself when  
Figure 7: Comparison between TA measurements in San José and Power-Delay Profiles generated by DED-RL (**a**) for Samsung S5 device and (**b**) for Nexus 5X device.  
the traffic demand abruptly increases in some areas. To accomplish this objective, a thorough ad accurate characterization of the air-to-ground radio channel is needed, as simple empirical channel models are not suitable, especially in dense urban areas where only a deterministic prediction is able to address the actual coverage in a realistic manner: this can be achieved in a straightforward way through the DED-RL software, which is able to generate hundreds or thousands of coverage maps of urban areas in a short time, thanks to its computational efficiency.  
An example of radio coverage map obtained with DED-RL is shown in Figure 8, assuming that the ABS is flying over the historical center of the Italian city of Bologna at 150 m of altitude, and illuminates the ground with a directive antenna, whose half-power beamwidth is 80\({}^{\circ}\). In this case, only the coverage at ground (i.e., along the streets) is represented, in order to make the urban structure visible, but DED-RL is able to deliver full-3D prediction also on the buildings surfaces, as explained before. It is worth noting that in dense urban areas like the one depicted here, the coverage map has not necessarily a circular or quasi-circular symmetry, like in rural or suburban areas. Therefore, in urban environments the obstruction of tall buildings and the combination of several propagation mechanisms, including reflection, diffraction, and scattering, is very important to predict the UAV-to-ground coverage in a realistic way, differently from rural/suburban areas where LOS is the largely dominant, if not the only, propagation mechanism [20].  
Radio coverage maps generated with DED-RL can then be used ad inputs for the network controller with the aim to manage radio resources and plan ABSs trajectories based on the simulated received power a priori for different UAV positions. Further details about this procedure, and a preliminary assessment of the performance, are presented in [24].