# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 3 System Model  
### Reference Scenario and Traffic Generated  
We considered a rectangular area where \(N_{\text{BS}}\) sites, with four directional TBSs per site, were uniformly distributed (see Figure 1). The service provider designated a UAV to support the network where and when it is needed. The UAV used, as a starting point for its flight, one of the TBS sites, then, its dynamic trajectory was computed as described in the following.  
In the considered scenario, a number of vehicles move according to traffic traces reproduced using the micro-traffic simulator VISSIM, which reproduces in detail the movements of vehicles taking into account the real road network, physical laws and the road rules [39].  
Among all \(N_{v}\) vehicles, we assumed that a portion \(P_{v}\) of them was equipped with C-V2X devices and required the considered service. In the rest of the paper we denote these vehicles as active, with the parameter \(P_{v}\) being varied in our simulations to observe the impact of a different load on the network performance.  
Further, we set the application requirements to be coherent with the 3 GPP documentation [8] on the extended sensing use case, which defines a range of data rates between 10 Mbps to 1 Gbps. We decided to focus on the downlink only, by setting requirements of either 25 Mb/s or 50 Mb/s. This choice has been made because the downlink becomes a bottleneck for the network when the same sensing information collected from one vehicle has to be forwarded in broadcast to all the interested neighbors.  
We assumed that the UAV knew: (1) the position of all vehicles that are under cellular coverage in each time instant and their application requirements; and (2) the pool of RRs available to it and the set of RRs used by the TBSs. This can be possible by centralizing these network operations in a network entity that manages both TBSs and UAV through Software Defined Networking (SDN) and  
Figure 1: Reference scenario: dots represent vehicles in a given simulation snapshot.  
network function virtualization (NFV) techniques [27]. Then, the UAV was responsible for: (1) defining its mission and trajectory in real-time; and (2) RR assignment at its side.  
As known, UAVs have a limited battery life. Nowadays, a drone flight lasts for 25-30 min [40], but recent developments in technology demonstrate that an endurance of few hours is already possible [41]. In any case, since this is the typical issue of UAVs for the majority of possible applications, a number of recent studies has tackled this problem, such as [42; 43]. The former presents a new UAV with an endurance that lasts more than two hours, while the latter studies how to wirelessly recharge drones to make their landing even unnecessary. In this work, we assumed that, when the UAV runs out of battery, it was able to fly back to one TBS site to recharge and be replaced by another one to maintain a seamless operation. Moreover, we considered a small area (few squared kilometers) and we focused only on the operational phase. Actually, it can be noted that in our scenario in the worst case, the UAV has to fly for 721 m; with a speed of 20 m/s it takes only 36 s out of a 30 min flight, thus the recharge phase has a negligible impact on the network performance.