11 research outputs found
Energy-Efficient 3D Deployment of Aerial Access Points in a UAV Communication System
In this letter, we propose an energy-efficient 3-dimensional placement of
multiple aerial access points (AAPs), in the desired area, acting as flying
base stations for uplink communication from a set of ground user equipment
(UE). The globally optimal energy-efficient vertical position of AAPs is
derived analytically by considering the inter-cell interference and AAP energy
consumption. The horizontal position of AAPs which maximize the packing density
of the AAP coverage area are determined using a novel regular polygon-based AAP
placement algorithm. We also determine the maximum number of non-interfering
AAPs that can be placed in the desired area. The effect of the AAP energy
consumption on the optimal placement and the analytic findings are verified via
numerical simulations.Comment: This version of the work has been accepted for publication in the
IEEE COMMUNICATIONS LETTERS. 6 pages; 5 figure
Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks
[EN] Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.The research is funded by the Department of Computer Science, Iqra University, Islamabad Campus, PakistanMajeed, S.; Sohail, A.; Qureshi, KN.; Kumar, A.; Iqbal, S.; Lloret, J. (2020). Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks. EURASIP Journal on Wireless Communications and Networking. 2020(1):1-14. https://doi.org/10.1186/s13638-020-01877-01142020
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide
rapid communication services to ground users and are thus promising for future
communication systems. In this paper, we consider a scenario where no
functional terrestrial BSs are available and the aim is deploying multiple
aerial BSs to cover a maximum number of users within a certain target area. To
this end, we first propose a naive successive deployment method, which converts
the non-convex constraints in the involved optimization into a combination of
linear constraints through geometrical relaxation. Then we investigate a
deployment method based on K-means clustering. The method divides the target
area into K convex subareas, where within each subarea, a mixed integer
non-linear problem (MINLP) is solved. An iterative power efficient technique is
further proposed to improve coverage probability with reduced power. Finally,
we propose a robust technique for compensating the loss of coverage probability
in the existence of inaccurate user location information (ULI). Our simulation
results show that, the proposed techniques achieve an up to 30% higher coverage
probability when users are not distributed uniformly. In addition, the proposed
simultaneous deployment techniques, especially the one using iterative
algorithm improve power-efficiency by up to 15% compared to the benchmark
circle packing theory