4,092 research outputs found
Coverage Probability of 3D Mobile UAV Networks
In this paper, we consider a network of multiple unmanned aerial vehicles
(UAVs) where a given number of UAVs are placed at three-dimensional (3D)
locations in a finite circular disk shaped region to serve a reference ground
user equipment (UE) located at its center. Herein, a serving UAV is assumed to
be located at fixed altitude which communicates with the reference UE. All the
other UAVs in the network are designated as interfering UAVs to the UE and are
assumed to have 3D mobility. To characterize the 3D UAV movement process, we
hereby propose an effective 3D mobility model based on the mixed random
waypoint mobility (RWPM) and uniform mobility (UM) models in the vertical and
spatial directions. Further, considering the proposed 3D mobility model, we
first characterize the interference received at reference UE, and then evaluate
its coverage probability under Nakagami-m fading. We quantify the achievable
performance gains for the ground UE under various system and channel
conditions. Moreover, we corroborate our analytical results through
simulations.Comment: 5 pages, 4 figure
Tutorial on UAV: A Blue Sky View on Wireless Communication
The growing use of Unmanned Aerial Vehicles (UAVs) for various applications
requires ubiquitous and reliable connectivity for safe control and data
exchange between these devices and ground terminals. Depending on the
application, UAV-mounted wireless equipment can either be an aerial user
equipment (AUE) that co-exists with the terrestrial users, or it can be a part
of wireless infrastructure providing a range of services to the ground users.
For instance, AUE can be used for real-time search and rescue and Aerial Base
Station (ABS) can enhance coverage, capacity and energy efficiency of wireless
networks. In both cases, UAV-based solutions are scalable, mobile, fast to
deploy. However, several technical challenges have to be addressed. In this
work, we present a tutorial on wireless communication with UAVs, taking into
account a wide range of potential applications. The main goal of this work is
to provide a complete overview of the main scenarios (AUE and ABS), channel and
performance models, compare them, and discuss open research points. This work
gives a comprehensive overview of the research done until now and depicts a
comprehensive picture to foster new ideas and solutions while avoiding
duplication of past work. We start by discussing the open challenges of
wireless communication with UAVs. To give answers to the posed questions, we
focus on the UAV communication basics, mainly providing the necessary channel
modeling background and giving guidelines on how various channel models should
be used. Next, theoretical, simulation- and measurement-based approaches, to
address the key challenges for AUE usage, are presented. Moreover, in this
work, we aim to provide a comprehensive overview on how UAV-mounted equipment
can be used as a part of a communication network. Based on the theoretical
analysis, we show how various network parameters can be optimized.Comment: 42 pages, 32 Figure
Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design
A novel framework is proposed for quality of experience (QoE)-driven
deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs).
The problem of joint non-convex three-dimensional (3D) deployment and dynamic
movement of the UAVs is formulated for maximizing the sum mean opinion score
(MOS) of ground users, which is proved to be NP-hard. In the aim of solving
this pertinent problem, a three-step approach is proposed for attaining 3D
deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm
based K-means (GAK-means) algorithm is utilized for obtaining the cell
partition of the users. Secondly, Q-learning based deployment algorithm is
proposed, in which each UAV acts as an agent, making their own decision for
attaining 3D position by learning from trial and mistake. In contrast to
conventional genetic algorithm based learning algorithms, the proposed
algorithm is capable of training the direction selection strategy offline.
Thirdly, Q-learning based movement algorithm is proposed in the scenario that
the users are roaming. The proposed algorithm is capable of converging to an
optimal state. Numerical results reveal that the proposed algorithms show a
fast convergence rate after a small number of iterations. Additionally, the
proposed Q-learning based deployment algorithm outperforms K-means algorithms
and Iterative-GAKmean (IGK) algorithms with a low complexity
Cognitive UAV Communication via Joint Maneuver and Power Control
This paper investigates a new scenario of spectrum sharing between unmanned
aerial vehicle (UAV) and terrestrial wireless communication, in which a
cognitive/secondary UAV transmitter communicates with a ground secondary
receiver (SR), in the presence of a number of primary terrestrial communication
links that operate over the same frequency band. We exploit the UAV's mobility
in three-dimensional (3D) space to improve its cognitive communication
performance while controlling the co-channel interference at the primary
receivers (PRs), such that the received interference power at each PR is below
a prescribed threshold termed as interference temperature (IT). First, we
consider the quasi-stationary UAV scenario, where the UAV is placed at a static
location during each communication period of interest. In this case, we jointly
optimize the UAV's 3D placement and power control to maximize the SR's
achievable rate, subject to the UAV's altitude and transmit power constraints,
as well as a set of IT constraints at the PRs to protect their communications.
Next, we consider the mobile UAV scenario, in which the UAV is dispatched to
fly from an initial location to a final location within a given task period. We
propose an efficient algorithm to maximize the SR's average achievable rate
over this period by jointly optimizing the UAV's 3D trajectory and power
control, subject to the additional constraints on UAV's maximum flying speed
and initial/final locations. Finally, numerical results are provided to
evaluate the performance of the proposed designs for different scenarios, as
compared to various benchmark schemes. It is shown that in the quasi-stationary
scenario the UAV should be placed at its minimum altitude while in the mobile
scenario the UAV should adjust its altitude along with horizontal trajectory,
so as to maximize the SR's achievable rate in both scenarios.Comment: 16 pages,11 figures, accepted by IEEE Transactions on Communication
MmWave UAV Networks with Multi-cell Association: Performance Limit and Optimization
This paper aims to exploit the fundamental limits on the downlink coverage
and spatial throughput performances of a cellular network comprised of a tier
of unmanned aerial vehicle (UAV) base stations (BSs) using the millimeter wave
(mmWave) band and a tier of ground BSs using the ultra high frequency (UHF)
band. To reduce handover signaling overhead, the ground BSs take charge of
control signaling delivery whereas the UAVs are in charge of payload data
transmission so that users need to be simultaneously associated with a ground
BS and a UAV in this network with a control-data plane-split architecture. We
first propose a three-dimensional (3D) location distribution model of the UAVs
using stochastic geometry which is able to generally characterize the positions
of the UAVs in the sky. Using this 3D distribution model of UAVs, two
performance metrics, i.e., multi-cell coverage probability and volume spectral
efficiency, are proposed. Their explicit low-complexity expressions are derived
and their upper limits are found when each of the UAVs and ground BSs is
equipped with a massive antenna array. We further show that the multi-cell
coverage probability and the volume spectral efficiency can be maximized by
optimally deploying and positioning the UAVs in the sky and thereby their
fundamental maximal limits are found. These important analytical findings are
validated by numerical simulations.Comment: 17pages, 1 table, 8 figures, journal submissio
Spatial Configuration of Agile Wireless Networks with Drone-BSs and User-in-the-loop
Agile networking can reduce over-engineering, costs, and energy waste.
Towards that end, it is vital to exploit all degrees of freedom of wireless
networks efficiently, so that service quality is not sacrificed. In order to
reap the benefits of flexible networking, we propose a spatial network
configuration scheme (SNC), which can result in efficient networking; both from
the perspective of network capacity, and profitability. First, SNC utilizes the
drone-base-stations (drone-BSs) to configure access points. Drone-BSs are
shifting paradigms of heterogeneous wireless networks by providing radically
flexible deployment opportunities. On the other hand, their limited endurance
and potential high cost increase the importance of utilizing drone-BSs
efficiently. Therefore, secondly, user mobility is exploited via
user-in-the-loop (UIL), which aims at influencing users' mobility by offering
incentives. The proposed uncoordinated SNC is a computationally efficient
method, yet, it may be insufficient to exploit the synergy between drone-BSs
and UIL. Hence, we propose joint SNC, which increases the performance gain
along with the computational cost. Finally, semi-joint SNC combines benefits of
joint SNC, with computational efficiency. Numerical results show that
semi-joint SNC is two orders of magnitude times faster than joint SNC, and more
than 15 percent profit can be obtained compared to conventional systems.Comment: To appear in IEEE Transactions on Wireless Communication
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Millimeter wave (mmWave) communications can potentially meet the high
data-rate requirements of unmanned aerial vehicle (UAV) networks. However, as
the prerequisite of mmWave communications, the narrow directional beam tracking
is very challenging because of the three-dimensional (3D) mobility and attitude
variation of UAVs. Aiming to address the beam tracking difficulties, we propose
to integrate the conformal array (CA) with the surface of each UAV, which
enables the full spatial coverage and the agile beam tracking in highly dynamic
UAV mmWave networks. More specifically, the key contributions of our work are
three-fold. 1) A new mmWave beam tracking framework is established for the
CA-enabled UAV mmWave network. 2) A specialized hierarchical codebook is
constructed to drive the directional radiating element (DRE)-covered
cylindrical conformal array (CCA), which contains both the angular beam pattern
and the subarray pattern to fully utilize the potential of the CA. 3) A
codebook-based multiuser beam tracking scheme is proposed, where the Gaussian
process machine learning enabled UAV position/attitude predication is developed
to improve the beam tracking efficiency in conjunction with the tracking-error
aware adaptive beamwidth control. Simulation results validate the effectiveness
of the proposed codebook-based beam tracking scheme in the CA-enabled UAV
mmWave network, and demonstrate the advantages of CA over the conventional
planner array in terms of spectrum efficiency and outage probability in the
highly dynamic scenarios
On-Demand Deployment of Multiple Aerial Base Stations for Traffic Offloading and Network Recovery
Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of
applications in wireless networks leading to attractive business opportunities.
In the case of abrupt disruption to existing cellular network operation or
infrastructure, e.g., due to an unexpected surge in user demand or a natural
disaster, UAVs can be deployed to provide instant recovery via temporary
wireless coverage in designated areas. A major challenge is to determine
efficiently how many UAVs are needed and where to position them in a relatively
large 3D search space. To this end, we formulate the problem of 3D deployment
of a fleet of UAVs as a mixed integer linear program, and present a greedy
approach that mimics the optimal behavior assuming a grid composed of a finite
set of possible UAV locations. In addition, we propose and evaluate a novel low
complexity algorithm for multiple UAV deployment in a continuous 3D space,
based on an unsupervised learning technique that relies on the notion of
electrostatics with repulsion and attraction forces. We present performance
results for the proposed algorithm as a function of various system parameters
and demonstrate its effectiveness compared to the close-to-optimal greedy
approach and its superiority compared to recent related work from the
literature
Survey of Important Issues in UAV Communication Networks
Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and
civil domains. These are particularly useful in applications where human lives
would otherwise be endangered. Multi-UAV systems can collaboratively complete
missions more efficiently and economically as compared to single UAV systems.
However, there are many issues to be resolved before effective use of UAVs can
be made to provide stable and reliable context-specific networks. Much of the
work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular
Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV
networks. UAV networks may vary from slow dynamic to dynamic; have intermittent
links and fluid topology. While it is believed that ad hoc mesh network would
be most suitable for UAV networks yet the architecture of multi-UAV networks
has been an understudied area. Software Defined Networking (SDN) could
facilitate flexible deployment and management of new services and help reduce
cost, increase security and availability in networks. Routing demands of UAV
networks go beyond the needs of MANETS and VANETS. Protocols are required that
would adapt to high mobility, dynamic topology, intermittent links, power
constraints and changing link quality. UAVs may fail and the network may get
partitioned making delay and disruption tolerance an important design
consideration. Limited life of the node and dynamicity of the network leads to
the requirement of seamless handovers where researchers are looking at the work
done in the areas of MANETs and VANETs, but the jury is still out. As energy
supply on UAVs is limited, protocols in various layers should contribute
towards greening of the network. This article surveys the work done towards all
of these outstanding issues, relating to this new class of networks, so as to
spur further research in these areas.Comment: arXiv admin note: substantial text overlap with arXiv:1304.3904 by
other author
Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a
wide range of innovative applications that can fundamentally change the way
cyber-physical systems (CPSs) are designed. CPSs are a modern generation of
systems with synergic cooperation between computational and physical potentials
that can interact with humans through several new mechanisms. The main
advantages of using UAVs in CPS application is their exceptional features,
including their mobility, dynamism, effortless deployment, adaptive altitude,
agility, adjustability, and effective appraisal of real-world functions anytime
and anywhere. Furthermore, from the technology perspective, UAVs are predicted
to be a vital element of the development of advanced CPSs. Therefore, in this
survey, we aim to pinpoint the most fundamental and important design challenges
of multi-UAV systems for CPS applications. We highlight key and versatile
aspects that span the coverage and tracking of targets and infrastructure
objects, energy-efficient navigation, and image analysis using machine learning
for fine-grained CPS applications. Key prototypes and testbeds are also
investigated to show how these practical technologies can facilitate CPS
applications. We present and propose state-of-the-art algorithms to address
design challenges with both quantitative and qualitative methods and map these
challenges with important CPS applications to draw insightful conclusions on
the challenges of each application. Finally, we summarize potential new
directions and ideas that could shape future research in these areas
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