269 research outputs found
Backhaul For Low-Altitude UAVs in Urban Environments
Unmanned Aerial Vehicles (UAVs) acting as access points in cellular networks
require wireless backhauls to the core network. In this paper we employ
stochastic geometry to carry out an analysis of the UAV backhaul performance
that can be achieved with a network of dedicated ground stations. We provide
analytical expressions for the probability of successfully establishing a
backhaul and the expected data rate over the backhaul link, given either an LTE
or a millimeter-wave backhaul. We demonstrate that increasing the density of
the ground station network gives diminishing returns on the performance of the
UAV backhaul, and that for an LTE backhaul the ground stations can benefit from
being colocated with an existing base station network
Performance Evaluation of UAV-enabled Cellular Networks with Battery-limited Drones
Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) to
offload Macro-BSs in hotspots. However, due to the limited battery on-board,
UAVs can typically stay in operation for less than 1.5 hours. Afterward, the
UAV has to fly back to a dedicated charging station that recharges/replaces the
UAV's battery. In this paper, we study the performance of a UAV-enabled
cellular network while capturing the influence of the spatial distribution of
the charging stations. In particular, we use tools from stochastic geometry to
derive the coverage probability of a UAV-enabled cellular network as a function
of the battery size, the density of the charging stations, and the time
required for recharging/replacing the battery
Coverage analysis of Tethered UAV-assisted Large Scale Cellular Networks
One of the major challenges slowing down the use of unmanned aerial vehicles
(UAVs) as aerial base stations (ABSs) is the limited on-board power supply
which reduces the UAV's flight time. Using a tether to provide UAVs with power
can be considered a reasonable compromise that will enhance the flight time
while limiting the UAV's mobility. In this work, we propose a system where ABSs
are deployed at the centers of user hotspots to offload the traffic and assist
terrestrial base stations (TBSs). Firstly, given the location of the ground
station in the user hotspot (user cluster) and the users spatial distribution,
we compute the optimal inclination angle and length of the tether. Using these
results, we compute the densities of the tethered UAVs deployed at different
altitudes, which enables tractable analysis of the interference in the
considered setup. Next, using tools from stochastic geometry and an approach of
dividing user clusters into finite frames, we analyze the coverage probability
as a function of the maximum tether length, the density of accessible rooftops
for UAV ground station deployment, and the density of clusters. We verify our
findings using Monte-Carlo simulations and draw multiple useful insights. For
instance, we show that it is actually better to deploy UAVs at a fraction of
the clusters, not all of them as it is usually assumed in literature
On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks
Using drones for cellular coverage enhancement is a recent technology that
has shown a great potential in various practical scenarios. However, one of the
main challenges that limits the performance of drone-enabled wireless networks
is the limited flight time. In particular, due to the limited on-board battery
size, the drone needs to frequently interrupt its operation and fly back to a
charging station to recharge/replace its battery. In addition, the charging
station might be responsible to recharge multiple drones. Given that the
charging station has limited capacity, it can only serve a finite number of
drones simultaneously. Hence, in order to accurately capture the influence of
the battery limitation on the performance, it is required to analyze the
dynamics of the time spent by the drones at the charging stations. In this
paper, we use tools from queuing theory and stochastic geometry to study the
influence of each of the charging stations limited capacity and spatial density
on the performance of a drone-enabled wireless network
Drone Mobile Networks: Performance Analysis Under 3D Tractable Mobility Models
Reliable wireless communication networks are a significant but challenging mission for
post-disaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial
vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile
networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits
of drone mobile networks taking into account practical considerations. Most existing works on mobile drone
networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is
significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile
networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number
of different mobility models including a random Brownian motion approach. The proposed framework allows
to circumvent the extremely complex reality model and obtain upper and lower performance bounds for
drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading
characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multi-antenna
operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for
various network settings. In addition, the results reveal some design guidelines and important trends for
the practical deployment of drone networks
Sustainable Wireless Services with UAV Swarms Tailored to Renewable Energy Sources
Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid
scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs
have no access to the power grid and instead rely on renewable energy.
Considering a main battery fed from two renewable sources, wind and solar, we
scale such a system based on the financial budget, environmental
characteristics, and seasonal variations. Interestingly, the source of energy
is correlated with the energy expenditure of the UAVs, since strong winds cause
UAV hovering to become increasingly energy-hungry. The aim is to maximize the
cost efficiency of coverage at a particular location, which is a combinatorial
optimization problem for dimensioning of the multivariate energy generation
system under non-convex criteria. We have devised a customized algorithm by
lowering the processing complexity and reducing the solution space through
sampling. Evaluation is done with condensed real-world data on wind, solar
energy, and traffic load per unit area, driven by vendor-provided prices. The
implementation was tested in four locations, with varying wind or solar
intensity. The best results were achieved in locations with mild wind presence
and strong solar irradiation, while locations with strong winds and low solar
intensity require higher Capital Expenditure (CAPEX) allocation.Comment: To be published in Transactions on Smart Gri
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