1,364 research outputs found
Radio Map Based 3D Path Planning for Cellular-Connected UAV
In this paper, we study the three-dimensional (3D) path planning for a
cellular-connected unmanned aerial vehicle (UAV) to minimize its flying
distance from given initial to final locations, while ensuring a target link
quality in terms of the expected signal-to-interference-plus-noise ratio (SINR)
at the UAV receiver with each of its associated ground base stations (GBSs)
during the flight. To exploit the location-dependent and spatially varying
channel as well as interference over the 3D space, we propose a new radio map
based path planning framework for the UAV. Specifically, we consider the
channel gain map of each GBS that provides its large-scale channel gains with
uniformly sampled locations on a 3D grid, which are due to static and
large-size obstacles (e.g., buildings) and thus assumed to be time-invariant.
Based on the channel gain maps of GBSs as well as their loading factors, we
then construct an SINR map that depicts the expected SINR levels over the
sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive
the optimal UAV path by solving an equivalent shortest path problem (SPP) in
graph theory. We further propose a grid quantization approach where the grid
points in the SINR map are more coarsely sampled by exploiting the spatial
channel/interference correlation over neighboring grids. Then, we solve an
approximate SPP over the reduced-size SINR map (graph) with reduced complexity.
Numerical results show that the proposed solution can effectively minimize the
flying distance/time of the UAV subject to its communication quality
constraint, and a flexible trade-off between performance and complexity can be
achieved by adjusting the grid quantization ratio in the SINR map. Moreover,
the proposed solution significantly outperforms various benchmark schemes
without fully exploiting the channel/interference spatial distribution in the
network.Comment: to appear in IEEE Transactions on Wireless Communications. arXiv
admin note: text overlap with arXiv:1905.0504
Radio Map Based Path Planning for Cellular-Connected UAV
In this paper, we study the path planning for a cellular-connected unmanned
aerial vehicle (UAV) to minimize its flying distance from given initial to
final locations, while ensuring a target link quality in terms of the
large-scale channel gain with each of its associated ground base stations
(GBSs) during the flight. To this end, we propose the use of radio map that
provides the information on the large-scale channel gains between each GBS and
uniformly sampled locations on a three-dimensional (3D) grid over the region of
interest, which are assumed to be time-invariant due to the generally static
and large-size obstacles therein (e.g., buildings). Based on the given radio
maps of the GBSs, we first obtain the optimal UAV path by solving an equivalent
shortest path problem (SPP) in graph theory. To reduce the computation
complexity of the optimal solution, we further propose a grid quantization
method whereby the grid points in each GBS's radio map are more coarsely
sampled by exploiting the spatial channel correlation over neighboring grids.
Then, we solve the approximate SPP over the reduced-size radio map (graph) more
efficiently. Numerical results show that the proposed solutions can effectively
minimize the flying distance of the UAV subject to its communication quality
constraint. Moreover, a flexible trade-off between performance and complexity
can be achieved by adjusting the quantization ratio for the radio map.Comment: to appear in Proc. IEEE Global Communications Conference (Globecom),
201
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
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
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
Millimeter-Wave for Unmanned Aerial Vehicles Networks: Enabling Multi-Beam Multi-Stream Communications
With the fifth-generation (5G) mobile networks being actively standardized
and deployed, many new vehicular communications technologies are developed to
support and enrich various application scenarios. Unmanned aerial vehicle (UAV)
enabled communications emerges as one of many promising solutions of
constructing the next-generation highly reconfigurable and mobile networks. In
this article, we first investigate and envision the challenges of future UAV
applications from the net-work, system, and hardware design perspectives, and
then pre-sent a UAV aerial base station (ABS) prototype which works at
millimeter-wave (mmWave) bands and enable multi-beam mul-ti-stream
communications. In terms of the field trial tests of the first UAV-ABS of its
kind in the world, multi-giga-bit-per-second data rate of uplink and downlink
is verified with good stability and reliability against mildly challenging
weather conditions.Comment: 8 pages, 10 figures. P.S. MmWave for UAV networks and multi-stream
multi-beam communications, it works ! (from field tests
Edge Computing Assisted Autonomous Flight for UAV: Synergies between Vision and Communications
Autonomous flight for UAVs relies on visual information for avoiding
obstacles and ensuring a safe collision-free flight. In addition to visual
clues, safe UAVs often need connectivity with the ground station. In this
paper, we study the synergies between vision and communications for edge
computing-enabled UAV flight. By proposing a framework of Edge Computing
Assisted Autonomous Flight (ECAAF), we illustrate that vision and
communications can interact with and assist each other with the aid of edge
computing and offloading, and further speed up the UAV mission completion.
ECAAF consists of three functionalities that are discussed in detail: edge
computing for 3D map acquisition, radio map construction from the 3D map, and
online trajectory planning. During ECAAF, the interactions of communication
capacity, video offloading, 3D map quality, and channel state of the trajectory
form a positive feedback loop. Simulation results verify that the proposed
method can improve mission performance by enhancing connectivity. Finally, we
conclude with some future research directions
Beyond 5G with UAVs: Foundations of a 3D Wireless Cellular Network
In this paper, a novel concept of three-dimensional (3D) cellular networks,
that integrate drone base stations (drone-BS) and cellular-connected drone
users (drone-UEs), is introduced. For this new 3D cellular architecture, a
novel framework for network planning for drone-BSs as well as latency-minimal
cell association for drone-UEs is proposed. For network planning, a tractable
method for drone-BSs' deployment based on the notion of truncated octahedron
shapes is proposed that ensures full coverage for a given space with minimum
number of drone-BSs. In addition, to characterize frequency planning in such 3D
wireless networks, an analytical expression for the feasible integer frequency
reuse factors is derived. Subsequently, an optimal 3D cell association scheme
is developed for which the drone-UEs' latency, considering transmission,
computation, and backhaul delays, is minimized. To this end, first, the spatial
distribution of the drone-UEs is estimated using a kernel density estimation
method, and the parameters of the estimator are obtained using a
cross-validation method. Then, according to the spatial distribution of
drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell
association for drone-UEs is derived by exploiting tools from optimal transport
theory. Simulation results show that the proposed approach reduces the latency
of drone-UEs compared to the classical cell association approach that uses a
signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the
proposed approach yields a reduction of up to 46% in the average latency
compared to the SINR-based association. The results also show that the proposed
latency-optimal cell association improves the spectral efficiency of a 3D
wireless cellular network of drones.Comment: Accepted in IEEE Transactions on Wireless Communication
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.Comment: This manuscript has been accepted for publication in IEEE Wireless
Communication
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to
unlock the full potential of UAVs in the future. However, how to achieve
ubiquitous three-dimensional (3D) communication coverage for the UAVs in the
sky is a new challenge. In this paper, we tackle this challenge by a new
coverage-aware navigation approach, which exploits the UAV's controllable
mobility to design its navigation/trajectory to avoid the cellular BSs'
coverage holes while accomplishing their missions. We formulate an UAV
trajectory optimization problem to minimize the weighted sum of its mission
completion time and expected communication outage duration, and propose a new
solution approach based on the technique of deep reinforcement learning (DRL).
To further improve the performance, we propose a new framework called
simultaneous navigation and radio mapping (SNARM), where the UAV's signal
measurement is used not only for training the deep Q network (DQN) directly,
but also to create a radio map that is able to predict the outage probabilities
at all locations in the area of interest. This thus enables the generation of
simulated UAV trajectories and predicting their expected returns, which are
then used to further train the DQN via Dyna technique, thus greatly improving
the learning efficiency
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