## 2 Related Work  
Optimal placement of UAVs has already been studied in the literature, whether considering single or multi-UAV scenarios. In [2], a single UAV was proposed for search and rescue application such as earthquake, flood, or bomb blast. The goal is to deploy a UAV to a position where it can bridge communication between two static nodes on the ground. It is assumed that the UAV hovers over the area in spiral or ladder search mode, sending hello/beacon messages at a regular interval. Upon receiving such a message, the GNs respond by sending their GPS positions back to the UAV. The UAV stores this information and continues hovering in the immediate surroundings to find a position based on the Received Signal Strength (RSS) and distance between the UAV and nodes on the ground. Simulation results showed that the algorithm provides maximum throughput and a low Bit Error Rate (BER) once the UAV is fixed at an optimal position. The drawback of this system is that it is only validated for two GNs. Therefore, as the number of GNs grows, the solution should consider energy constraints during the search process and bandwidth constraints when providing network access to GNs. The authors in [13] have developed a framework named UAVNet. It is capable of autonomously deploying a wireless mesh network to interconnect two end systems using small quadrocopter-based UAVs with 802.11 s nodes on board. Each UAV would act as the access point and provide network access for regular IEEE 802.11 g wireless devices. There are two positioning modes to place the UAVs between the end systems. The first one is the location-based positioning mode. The latter uses the submitted GPS locations of the end systems and directs the UAV to the exact geographical position between these two GPS coordinates. The second one is the signal strength positioning mode. It extends the location positioning mode and includes also the received signal strength of the two end systems to calculate a more accurate position for the UAV. This takes the quality of the wireless link and other environmental factors into account. Usually, the process of network densification in cellular networks uses fixed small cells (e.g., picocells and femtocells) to increase the network capacity based on the expected formation of hotspots. In places where temporary hotspots are formed, fixed small cells would remain under-utilized once the hotspots have moved to a different location or disappeared. The authors in [14] proposed small cells mounted on UAVs to offload User Equipment (UE) from the microcell infrastructure. The optimum placement points of the UAVs are determined using the K-means clustering algorithm. In their work, the performance metric was measured based on the RSS experienced by the UE. The simulation results have shown that as UAVs are able to position themselves in real time around the actual UE position rather than expected UE hotspots, they outperform equivalent small cell deployment. In [15], [PERSON] et al. presented a mathematical model that aims to optimize the flying altitude of UAV-based Base Stations (BSs) to maximize the coverage area on the ground. The Air-to-Ground (A2G) path loss model is based on their previous work [16]. The A2G path loss in [16] considered some parameters (\(\alpha\), \(\beta\)o, and \(\gamma o\)) that describe to a fair extent the general geometrical statistics of a certain urban area in which the Radio Frequency (RF) signal propagates. The authors refer to the additive loss incurred on top of the Free Space Path Loss (FSPL) as the excessive path loss (\(\eta\)), which has a Gaussian distribution. In the model, \(\eta\) is used to identify three different groups of GNs, namely G1, which is the group favoring the Line-of-Sight (LoS) condition; group G2 corresponds to GNs with No Line-of-Sight (NLoS), but still receiving coverage via strong reflection and diffraction; and lastly, group G3, which suffer from deep fading conditions.