# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs) for wireless communications have experienced rapid growth in a broad range of application domains [1]. On-board communication devices enable UAVs to enhance coverage, capacity, reliability or energy efficiency of wireless networks. However, note that the drone communication technology has a decisive impact on the data rate and range usable in the mission [2]. The predominant technology in UAV-related literature is cellular (i.e., UAVs are used as aerial base stations) [3], but it is not uncommon to find studies with other communication technologies for UAV-to-ground communication such as WiFi [4; 5; 6] or Bluetooth [7]. This work takes WiFi as enabling technology for UAV-to-ground communication. WiFi provides a data range of 11 Mbps-1 Gbps and a range of up to 250 m (depending on the revision).  
Deploying UAVs for wireless networks brings in new opportunities, such as creating a provisional communication infrastructure in Search and Rescue (SAR) missions [1; 8]. However, it also brings multiple challenges such as optimal 3D placement, performance analysis, path planning, backhaulconnectivity, or energy limitation, just to name a few [3; 9]. In this work, we deal with the challenge of optimal multi-UAV 3D placement in the context of creating a provisional WLAN (i.e., UAVs are used as aerial Access Points--AP) to provide ground users with VoIP over WiFi (Vo WiFi) service. A distinctive characteristic of this service is that 3D placement has to consider not only coverage (e.g., [10; 11]) but also VoIP speech quality [12] since the mac-sublayer of the communication protocol limits the number of simultaneous VoIP flows that APs can handle with quality of experience (for example, an AP performing IEEE 802.11 b can handle a maximum of 15 simultaneous calls [13], even though the number of stations associated to such AP could be multiple times greater). This limit is known as VoIP capacity [14; 15; 16].  
UAV's optimal 3D placement is a challenging task that has received significant attention from the research community [3; 9; 17; 18]. The problem has to take into account multiple factors such as the terrain, location of users, UAV-to-ground channel characteristics or radio interference between UAVs, and in our case, the Quality of Service (QoS). According to [PERSON] et at. [19], another factor to be carefully taken care of \"when deploying UAVs is their flight time and energy constraints\". Indeed, the authors provide an excellent literature review with emphasis on the energy efficiency perspective, dissecting the factors affecting energy consumption in UAVs such as UAV placement, UAV-user association, flight-time, mobility, trajectory, and UAV communication and mechanical energy. Although optimal 3D placement has been extensively addressed, the comparison between works is difficult as they have different objective functions and constraints. In general, we can classify them roughly as (a) those that minimize the number of UAVs deployed [20; 21; 22; 23] (i.e., minimize deployment cost); (b) those maximize energy efficiency or battery lifetime [24; 25; 26] (i.e., UAV endurance [27]). Energy-aware UAV optimal deployment problems typically considering the power consumption due to either radio communication [25; 26] or mechanical energy [24] using energy consumption models [28; 29]. The reader is encouraged to read the surveys [2; 19; 30] for a more detailed list of works and references that address the problem of 3D placement with emphasis in energy consumption.