# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 2 Literature Overview  
Initial studies on UAV-aided networks focused on the key link-level considerations, and specifically on the characterization of path loss and its impact on the so-called air-to-ground (ATG) channel [7, 8]. These activities on UABs were followed by the aim of finding an acceptable trade-off between coverage, capacity and connectivity, as in [9]. To be more specific, in [8] the effect of the user-UAV incident angle w.r.t. the ground plane as a function of drone height is studied. It is defined as elevation angle, and its aperture may determine a link being or not being free from obstructions and in LoS conditions. In the remainder of this article, we refer to the authors' ATG model. In recent years, the interest in UAV-aided networking gradually increased, introducing studies ranging from UAV deployment issues to grant adequate coverage [10, 11] to more complex problems of UAV trajectory design for fair and satisfactory quality of service to ground users [12, 13, 14]. Differently from our objective, the majority of papers addresses a general user or device, forgetting the implications and limitations of the specific protocol procedures. For example, dynamic trajectories for the 3D space are studied in [12] with the purpose to connect IoT nodes at their activation time. The authors jointly optimize the transmission power of ground nodes, the overall energy spent in movement and the choice of the next stop of each UAV. Some activities deal with the definition of an optimal trajectory for UABs. In [13, 14] the trajectory is optimized with the aim of maximizing the minimum user rate. Optimization algorithms have also been studied for UAV placement to achieve optimal energy efficiency [15, 16] by minimizing power consumption and transmission delays, which are interesting requirements for IoT applications. However, they do not consider specific protocol constraints or overhead as it is in 3 GPP standards. Similarly to this study, some other works related to UAVs are targeting the IoT field. As for recent examples, references [17, 18] employ UAVs to gather IoT data from remote areas in which devices experience low connectivity or capacity issues. In [17], the authors address the trajectory and resource allocation optimization for time-constrained IoT nodes, while the authors in [18] optimize the UAB 3D placement and resource allocation minimizing the total transmission power of IoT devices and balancing the multi-UABs' tasks. Reference [19] focuses on the modelling and optimization of the carrier sensing-based medium access control (MAC) layer protocol of UAV-aided IoT networks; this paper analyses the achievable throughput as the performance metric and CSMA/CA as the MAC protocol. Moreover, reference [20] studies different mechanism for the energy and delay aware task assignment of UAVs with onboard IoT nodes. More specific to the technology investigated hereby, reference [21] studies trajectories for energy minimization in a NB-IoT context, reference [22] investigates connectivity strategies for a specific NB-IoT application, and [23] introduces a coverage analysis for UAV-aided NB-IoT networks. The NB-IoT protocol is considered in these articles, too, since it belongs to the machine-type emerging technologies [6] and targets low data rates, delay tolerance, massive connections, and extremely wide coverage [24]. However, these works usually lack either a fine-grainedprotocol study or focus on the optimization of one metric above the others. On the contrary, we aim at providing a more general overview of potentials and challenges of UAV-aided NB-IoT networks, with a deeper focus on protocol details rather than the optimization of a single performance metric. Moreover, optimization frameworks struggle to handle large input instances (e.g., a massive number of nodes in the scenario) because of the excessive computation times, in terms of days or weeks. For example, references [13; 14] consider less than 10 users in the service area.