# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 2 Related Works  
Multiple survey papers address the optimal placement of UAVs for wireless communications. In this section, we scrutinize four recent surveys and complement them with other related works applicable. Our goal is to clarify how our work compares with state of the art. The authors in [2] review the literature for multiple drone applications such as drone base-stations, search and rescue, or surveillance and monitoring, which is challenging due to the disparity of goals and constraints. Regarding energy efficiency and 3D placement, in [26], the authors suggest an algorithm that minimizes the transmit power for the 3D placement of one drone and assess their approach by simulation. The 3D placement of one aerial base station was also proposed in [25]. In this case, the authors proposed a placement algorithm to maximize the number of covered users using the minimum transmit power. In both cases, only the power related to radio communication was taken into account. In [31] the authors propose a placement algorithm for a set of UAV-mounted 5G aerial base stations that maximizes the capacity boost provided by the UAVs in each considered time frame and extends the battery life of the served mobile users. Unfortunately, the authors did not model the energy consumption, stating that \"intuitively, less power is needed if the Gateway is closer to the users\". In a context similar to ours, in [12], we proposed a 3D placement algorithm that minimized the number of aerial APs deployed to provide VoWiFi to ground users. To the best of our knowledge, this is the only related work in which speech quality has been considered a constraint. However, the minimization subject was the number of UAVs launched, and only among solutions with a similar number of drones, the algorithm selected the layout that extended the battery life of served users, overlooking the power expenditure in UAVs. The survey paper [3] addresses the use of UAVs for wireless networks. The paper provides state of the art in the optimal deployment of UAVs as flying base stations. In their previous work [32], the authors addressed the optimal deployment and mobility of multiple UAVs for energy-efficient data collection, minimizing the total transmit power of IoT devices (i.e., ground users) during UAVs trajectories. Other works such as [18; 22; 23; 33; 34; 35; 36; 37; 38] address the 3D optimal placement problem considering coverage of ground users through different approaches, but disregard energy efficiency. Some of these works consider interference between UAVs [33]. Another excellent paper is [30], where the authors survey recent research addressing 1-3D placement optimization for aerial base stations. The authors highlight the diversity of objectives in the optimization problem, such as maximizing the system capacity, minimizing the required number of UAVs [34], or minimizing the total transmit power of the entire aerial system [39]. The algorithms used for the optimal placement included force brute search [37], Genetic Algorithms [40], K-means clustering [41] or transport theory [39]. This survey also provides a review of power-efficient operationof aerial base stations emphasizing that researchers typically work on either reducing communication energy (i.e., minimizing transmission power) of either the drone or ground users [17], or reducing mechanical energy of UAVs. The latter requires to count with an energy consumption model [21], which largely depends on hovering. As the authors put it, \"one solution to control the energy consumption of UAVs is to regulate their height. However, changing the height might reduce the performance of UAVs\". This trade-off is also applies to this work and it will be addressed in the next section. Finally, the survey provides a list of testbeds and real experiments in the literature.