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Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage
To provide a reliable wireless uplink for users in a given ground area, one
can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another
application, one can use UAVs to collect data from sensors on the ground. For a
power-efficient and scalable deployment of such flying BSs, directional
antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We
consider a large-scale wireless path-loss model with a realistic
angle-dependent radiation pattern for the directional antennas. Based on such a
model, we determine the optimal 3-D deployment of N UAVs to minimize the
average transmit-power consumption of the users in a given target area. The
users are assumed to have identical transmitters with ideal omnidirectional
antennas and the UAVs have identical directional antennas with given half-power
beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For
uniformly distributed ground users, we show that the UAVs have to share a
common flight height in an optimal power-efficient deployment. We also derive
in closed-form the asymptotic optimal common flight height of UAVs in terms
of the area size, data-rate, bandwidth, HPBW, and path-loss exponent
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
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