176 research outputs found
Power Efficient Visible Light Communication (VLC) with Unmanned Aerial Vehicles (UAVs)
A novel approach that combines visible light communication (VLC) with
unmanned aerial vehicles (UAVs) to simultaneously provide flexible
communication and illumination is proposed. To minimize the power consumption,
the locations of UAVs and the cell associations are optimized under
illumination and communication constraints. An efficient sub-optimal solution
that divides the original problem into two sub-problems is proposed. The first
sub-problem is modeled as a classical smallest enclosing disk problem to obtain
the optimal locations of UAVs, given the cell association. Then, assuming fixed
UAV locations, the second sub-problem is modeled as a min-size clustering
problem to obtain the optimized cell association. In addition, the obtained UAV
locations and cell associations are iteratively optimized multiple times to
reduce the power consumption. Numerical results show that the proposed approach
can reduce the total transmit power consumption by at least 53.8% compared to
two baseline algorithms with fixed UAV locations.Comment: 4 pages, 4 figures. Accepted for publication in IEEE Communications
Letter
Deep Learning for Optimal Deployment of UAVs with Visible Light Communications
In this paper, the problem of dynamical deployment of unmanned aerial
vehicles (UAVs) equipped with visible light communication (VLC) capabilities
for optimizing the energy efficiency of UAV-enabled networks is studied. In the
studied model, the UAVs can simultaneously provide communications and
illumination to service ground users. Since ambient illumination increases the
interference over VLC links while reducing the illumination threshold of the
UAVs, it is necessary to consider the illumination distribution of the target
area for UAV deployment optimization. This problem is formulated as an
optimization problem which jointly optimizes UAV deployment, user association,
and power efficiency while meeting the illumination and communication
requirements of users. To solve this problem, an algorithm that combines the
machine learning framework of gated recurrent units (GRUs) with convolutional
neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the
long-term historical illumination distribution and predict the future
illumination distribution. Given the prediction of illumination distribution,
the original nonconvex optimization problem can be divided into two
sub-problems and is then solved using a low-complexity, iterative algorithm.
Then, the proposed algorithm enables UAVs to determine the their deployment and
user association to minimize the total transmit power. Simulation results using
real data from the Earth observations group (EOG) at NOAA/NCEI show that the
proposed approach can achieve up to 68.9% reduction in total transmit power
compared to a conventional optimal UAV deployment that does not consider the
illumination distribution and user association.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communications. arXiv admin note: text overlap with arXiv:1909.0755
Sum-Rate Optimization for Visible-Light-Band UAV Networks based on Particle Swarm Optimization
The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively investigated, visible-light-band networking is still in its infancy because of the intermittent link availability caused by blockage and miss-alignment among transceivers. Fortunately, drones can be deployed dynamically at network runtime to establish line-of-sight (LOS) links to users in blockage-rich environments. In this article, we first formulate a sum-rate optimization problem for visible-light-band UAV networks by jointly control the real-time position and orientations of drones. We then propose a solution algorithm based on particle swarm optimization (PSO). The simulation results show that the proposed algorithm can converge in 10 to 20 iteration time and can result in up to 24% performance gain compared to that in heuristic-central-point drone deployment
Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks
In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light
communication (VLC) has been considered which has two tiers: UAV-to-centroid
and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can
simultaneously provide communications and illumination for the centroids of the
ground users over VLC links. In the D2D tier, the centroids retransmit received
data from UAV over D2D links to the cluster members. For network, the
optimization problem of joint user association and deployment location of UAVs
is formulated to maximize the received data, satisfy illumination constraint,
and the user cluster size. An iterative algorithm is first proposed to
transform the optimization problem into a series of two interdependent sub
problems. Following the smallest enclosing disk theorem, a random incremental
construction method is designed to find the optimal UAV locations. Then,
inspired by unsupervised learning method, a clustering algorithm to find a
suboptimal user association is proposed. Our simulation results show that the
proposed scheme on average guarantees the users brightness 0.77 lux more than
their threshold requirements. Moreover, the received bitrate plus number of D2D
connected users under our proposed method is 50.69% more than the scenario in
which we have RF Link instead of VLC link and do not optimize UAV location.Comment: 7 pages, 5 figures, conferenc
Optimal Resource Allocation for Multi-UAV Assisted Visible Light Communication
In this paper, the optimization of deploying unmanned aerial vehicles (UAVs)
over a reconfigurable intelligent surfaces (RISs)-assisted visible light
communication (VLC) system is studied. In the considered model, UAVs are
required to simultaneously provide wireless services as well as illumination
for ground users. To meet the traffic and illumination demands of the ground
users while minimizing the energy consumption of the UAVs, one must optimize
UAV deployment, phase shift of RISs, user association and RIS association. This
problem is formulated as an optimization problem whose goal is to minimize the
transmit power of UAVs via adjusting UAV deployment, phase shift of RISs, user
association and RIS association. To solve this problem, the original
optimization problem is divided into four subproblems and an alternating
algorithm is proposed. Specifically, phases alignment method and semidefinite
program (SDP) algorithm are proposed to optimize the phase shift of RISs. Then,
the UAV deployment optimization is solved by the successive convex
approximation (SCA) algorithm. Since the problems of user association and RIS
association are integer programming, the fraction relaxation method is adopted
before using dual method to find the optimal solution. For simplicity, a greedy
algorithm is proposed as an alternative to optimize RIS association. The
proposed two schemes demonstrate the superior performance of 34:85% and 32:11%
energy consumption reduction over the case without RIS, respectively, through
extensive numerical study
A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges
Maritime activities represent a major domain of economic growth with several
emerging maritime Internet of Things use cases, such as smart ports, autonomous
navigation, and ocean monitoring systems. The major enabler for this exciting
ecosystem is the provision of broadband, low-delay, and reliable wireless
coverage to the ever-increasing number of vessels, buoys, platforms, sensors,
and actuators. Towards this end, the integration of unmanned aerial vehicles
(UAVs) in maritime communications introduces an aerial dimension to wireless
connectivity going above and beyond current deployments, which are mainly
relying on shore-based base stations with limited coverage and satellite links
with high latency. Considering the potential of UAV-aided wireless
communications, this survey presents the state-of-the-art in UAV-aided maritime
communications, which, in general, are based on both conventional optimization
and machine-learning-aided approaches. More specifically, relevant UAV-based
network architectures are discussed together with the role of their building
blocks. Then, physical-layer, resource management, and cloud/edge computing and
caching UAV-aided solutions in maritime environments are discussed and grouped
based on their performance targets. Moreover, as UAVs are characterized by
flexible deployment with high re-positioning capabilities, studies on UAV
trajectory optimization for maritime applications are thoroughly discussed. In
addition, aiming at shedding light on the current status of real-world
deployments, experimental studies on UAV-aided maritime communications are
presented and implementation details are given. Finally, several important open
issues in the area of UAV-aided maritime communications are given, related to
the integration of sixth generation (6G) advancements
6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap
The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communicatio
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