Energy-Efficient 3D UAV-BS Placement versus Mobile Users' Density and Circuit Power

Abstract

Properly 3D placement of unmanned aerial vehicle mounted base stations (UAV-BSs) can effectively prolong the life-time of the mobile ad hoc network, since UAVs are usually powered by batteries. This paper involves the on-board circuit consumption power and considers the optimal placement that minimizes the UAV-recall-frequency (UAV-RF), which is defined to characterize the life-time of this kind of network. Theoretical results show that the optimal vertical and horizontal dimensions of UAV can be decoupled. That is, the optimal hovering altitude is proportional to the coverage radius of UAVs, and the slope is only determined by environment. Dense scattering environment may greatly enlarge the needed hovering altitude. Also, the optimal coverage radius is achieved when the transmit power equals to on-board circuit power, and hence limiting on-board circuit power can effectively enlarge life-time of system. In addition, our proposed 3D placement method only require the statistics of mobile users density and environment parameters, and hence it's a typical on-line method and can be easily implemented. Also, it can be utilized in scenarios with varying users' density.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…Research has considered the placement of several drones into stationary positions over some area of interest, which is closely related to the so‐called disc coverage problem, but considers communication constraints . Coordinates of users may be known in advance .…”

A Survey of Channel Modeling for UAV Communications

“…The path loss model in [10] addressed the technical challenges in UAV communication, such as optimum deployment of the UAV in [11], [12], outage and bit-error rate (BER) analysis in [84], energy efficiency of UAV networks in [85]- [87], interference management in multi-UAV scenario in [88], latency in UAV-enabled cellular networks in [89] and UAV flight endurance time in [90]. Furthermore, this model complements the optimum deployment of UAVs to ensure maximum reliability in terms of the outage capacity and the BER using static and mobile aerial relays in [91].…”

Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization

“…Unfortunately, the additional constraint is non-convex which makes (15) extremely hard to solve. Although the integer variables in (13) can be addressed with advanced mixed integer programming techniques, using solvers such as MOSEK [17], the optimization problem (15) which is a MINLP problem with non-convex constraint can not be straightforwardly solved. Even if we apply semidefinite relaxation (SDR) techniques to convert the quadratic programs into the form of semidefinite matrix which makes the non-convex constraint of (15) convex, a problem with both positive semidefinite matrix and integer variables is still unsolvable with existing techniques [28].…”

“…When the aerial BSs are utilized for emergency communications such as search-and-rescue, the priority is finding the optimal locations of UAVs so that a maximum number of users can be covered. Meanwhile, since built-in batteries are used for supplying power in most cases, limited on-board energy is another factor that constrains the endurance of aerial BSs [4], [13]- [15]. It has been proven that prolonged operation time can be achieved by reducing the transmit power of aerial BSs when quality-of-service (QoS) requirements are met [16], [17].…”