## 2 Related Work
The probability of a certain propagation group (excluding G3) to occur at a certain elevation angle is computed. Differently from [16], in [15], \(\eta\) refers to the mean value of the excessive path loss rather than its random behavior. The authors were able to present a closed-form equation based on the elevation angle and the urban statistical parameters (\(\alpha\), \(\beta\)o, \(\gamma o\)). In [17], [PERSON] et al. proposed a 3D UAV placement scheme using the Particle Swarm Optimization (PSO) algorithm. Their system model envisioned the use of UAVs as flying base stations (referred to as drone-BSs) and limited their analysis to the downlink communications. The main goal of the proposed algorithm is to find the minimum number of drone-BSs and their 3D placement to service all GNs with some target QoS requirement. The A2G channel model is similar to the one presented by [PERSON] et al. in [16], where the probability of LoS and NLoS connectivity between the drone-BSs and GNs was studied as explained previously. [PERSON] et al. [18] studied the optimum altitude to place a UAV when it is used to relay data from GN to a remote ground station for further processing. The new proposed model derives from the seminal A2G channel models [15; 16]. Differently from the former models, the proposed model accounts for the hop from the UAV to the remote station. The study was limited to a single UAV, which in turn may limit the coverage area. [PERSON] et al. [19] proposed a method to deploy multiple UAVs equipped with directional antennas. The aim was to maximize the coverage performance while ensuring that the coverage areas of UAVs do not overlap. The path loss model considered here is based on the model proposed in [16]. Although the A2G models and coverage schemes presented in [15; 16; 17; 19] may be a good approximation in an urban environment, they do not address the coordination mechanism or connectivity among UAVs, i.e., the Air-to-Air (A2A) connectivity, which is important for network reliability. In another work, [PERSON] et al. [20] presented a study in which the main contribution was to analyze the coverage and rate performance of UAV-based wireless communication in the presence of underlying device-to device (D2D) communication links. In [21], the authors presented a model for an optimal placement of UAVs to cover a set of targets, i.e., GNs. They considered two cost metrics, namely the number of UAVs and energy consumption, seeking to minimize both metrics. The authors assumed that each UAV had a minimum and maximum observation altitude. They also assured that the UAV's energy consumption was related to this altitude, since the higher the altitude, the larger the observed area, but also the higher the energy consumption. The optimization problem was mathematically solved by defining an integer linear and a mixed non-linear optimization model. The authors in [22] used the same assumption as in [21] to model an optimized UAV placement and formulate it as a multi-objective linear problem. The main difference is that, in [22], the connectivity among UAVs was considered as an additional constraint. In [22], the following objectives were to be minimized: the number of UAVs and the maximum flying altitude. Our work is closer to [22], though with some differences. Firstly, we consider using omni-directional antennas instead of directional. Secondly, one of our objectives is to minimize the difference between the assigned and required data rate, whereas one of their objectives is to maximize the flying altitude. Table 1 presents the main characteristics of the related work.