56 research outputs found
Efficiency Maximization for UAV-Enabled Mobile Relaying Systems with Laser Charging
This work studies the joint problem of power and trajectory optimization in
an unmanned aerial vehicle (UAV)-enabled mobile relaying system. In the
considered system, in order to provide convenient and sustainable energy supply
to the UAV relay, we consider the deployment of a power beacon (PB) which can
wirelessly charge the UAV and it is realized by a properly designed laser
charging system. To this end, we propose an efficiency (the weighted sum of the
energy efficiency during information transmission and wireless power
transmission efficiency) maximization problem by optimizing the source/UAV/PB
transmit powers along with the UAV's trajectory. This optimization problem is
also subject to practical mobility constraints, as well as the
information-causality constraint and energy-causality constraint at the UAV.
Different from the commonly used alternating optimization (AO) algorithm, two
joint design algorithms, namely: the concave-convex procedure (CCCP) and
penalty dual decomposition (PDD)-based algorithms, are presented to address the
resulting non-convex problem, which features complex objective function with
multiple-ratio terms and coupling constraints. These two very different
algorithms are both able to achieve a stationary solution of the original
efficiency maximization problem. Simulation results validate the effectiveness
of the proposed algorithms.Comment: 33 pages, 8 figures, accepted for publication in IEEE Transactions on
Wireless Communication
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Adaptive Coding and Modulation Aided Mobile Relaying for Millimeter-Wave Flying Ad-Hoc Networks
The emerging drone swarms are capable of carrying out sophisticated tasks in
support of demanding Internet-of-Things (IoT) applications by synergistically
working together. However, the target area may be out of the coverage of the
ground station and it may be impractical to deploy a large number of drones in
the target area due to cost, electromagnetic interference and flight-safety
regulations. By exploiting the innate \emph{agility} and \emph{mobility} of
unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone
swarm network architecture, which is capable of extending the coverage of the
ground station and enhancing the effective end-to-end throughput. Explicitly, a
swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing
and collecting data with the aid of their mounted cameras and/or sensors, and a
powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the
DCDS and a ground station (GS). Given a time period, in order to maximize the
data delivered whilst minimizing the delay imposed, we harness an
-multiple objective genetic algorithm (-MOGA) assisted
Pareto-optimization scheme. Our simulation results demonstrate that the
proposed mobile relaying is capable of delivering more data. As specific
examples investigated in our simulations, our mobile relaying-assisted drone
swarm network is capable of delivering more data than the benchmark
solutions, when a stationary relay is available, and it is capable of
delivering more data than the benchmark solutions when no stationary
relay is available
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Radio Resource Management for Unmanned Aerial Vehicle Assisted Wireless Communications and Networking
In recent years, employing unmanned aerial vehicles (UAVs) as aerial communication platforms or users is envisioned as a promising solution to enhance the performance of the existing wireless communication systems. However, applying UAVs for information technology applications also introduces many new challenges.
This thesis focuses on the UAV-assisted wireless communication and networking, and aims to address the challenges through exploiting and designing efficient radio resource management methods. Specifically, four research topics are studied in this thesis. Firstly, to address the constraint of network heterogeneity and leverage the benefits of diversity of UAVs, a hierarchical air-ground heterogeneous network architecture enabled by software defined networking is proposed, which integrates both high and low altitude platforms into conventional terrestrial networks to provide additional capacity enhancement and expand the coverage of current network systems. Secondly, to address the constraint of link disconnection and guarantee the reliable communications among UAVs as aerial user equipment to perform sensing tasks, a robust resource allocation scheme is designed while taking into account the dynamic features and different requirements for different UAV transmission connections. Thirdly, to address the constraint of privacy and security threat and motivate the spectrum sharing between cellular and UAV operators, a blockchain-based secure spectrum trading framework is constructed where mobile network operators and UAV operators can share spectrum in a distributed and trusted environment based on blockchain technology to protect users' privacy and data security. Fourthly, to address the constraint of low endurance of UAV and prolong its flight time as an aerial base station for delivering communication coverage in a disaster area, an energy efficiency maximization problem jointly optimizing user association, UAV's transmission power and trajectory is studied in which laser charging is exploited to supply sustainable energy to enable the UAV to operate in the sky for a long time
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