1,268 research outputs found
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
Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer
Radio frequency (RF) energy harvesting and transfer techniques have recently
become alternative methods to power the next generation of wireless networks.
As this emerging technology enables proactive replenishment of wireless
devices, it is advantageous in supporting applications with quality-of-service
(QoS) requirement. This article focuses on the resource allocation issues in
wireless networks with RF energy harvesting capability, referred to as RF
energy harvesting networks (RF-EHNs). First, we present an overview of the
RF-EHNs, followed by a review of a variety of issues regarding resource
allocation. Then, we present a case study of designing in the receiver
operation policy, which is of paramount importance in the RF-EHNs. We focus on
QoS support and service differentiation, which have not been addressed by
previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many
civil application domains including real-time monitoring, providing wireless
coverage, remote sensing, search and rescue, delivery of goods, security and
surveillance, precision agriculture, and civil infrastructure inspection. Smart
UAVs are the next big revolution in UAV technology promising to provide new
opportunities in different applications, especially in civil infrastructure in
terms of reduced risks and lower cost. Civil infrastructure is expected to
dominate the more that $45 Billion market value of UAV usage. In this survey,
we present UAV civil applications and their challenges. We also discuss current
research trends and provide future insights for potential UAV uses.
Furthermore, we present the key challenges for UAV civil applications,
including: charging challenges, collision avoidance and swarming challenges,
and networking and security related challenges. Based on our review of the
recent literature, we discuss open research challenges and draw high-level
insights on how these challenges might be approached.Comment: arXiv admin note: text overlap with arXiv:1602.03602,
arXiv:1704.04813 by other author
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
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
White Paper on Critical and Massive Machine Type Communication Towards 6G
The society as a whole, and many vertical sectors in particular, is becoming
increasingly digitalized. Machine Type Communication (MTC), encompassing its
massive and critical aspects, and ubiquitous wireless connectivity are among
the main enablers of such digitization at large. The recently introduced 5G New
Radio is natively designed to support both aspects of MTC to promote the
digital transformation of the society. However, it is evident that some of the
more demanding requirements cannot be fully supported by 5G networks.
Alongside, further development of the society towards 2030 will give rise to
new and more stringent requirements on wireless connectivity in general, and
MTC in particular. Driven by the societal trends towards 2030, the next
generation (6G) will be an agile and efficient convergent network serving a set
of diverse service classes and a wide range of key performance indicators
(KPI). This white paper explores the main drivers and requirements of an
MTC-optimized 6G network, and discusses the following six key research
questions:
- Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will
there emerge new key metrics?
- How to deliver different E2E service mandates with different KPI
requirements considering joint-optimization at the physical up to the
application layer?
- What are the key enablers towards designing ultra-low power receivers and
highly efficient sleep modes?
- How to tackle a disruptive rather than incremental joint design of a
massively scalable waveform and medium access policy for global MTC
connectivity?
- How to support new service classes characterizing mission-critical and
dependable MTC in 6G?
- What are the potential enablers of long term, lightweight and flexible
privacy and security schemes considering MTC device requirements?Comment: White paper by http://www.6GFlagship.co
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection
Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT)
capability provide a practical means to deploy a large number of wireless
powered sensing devices into areas with no access to persistent power supplies.
The UAV can charge the sensing devices remotely and harvest their data. A key
challenge is online MPT and data collection in the presence of on-board control
of a UAV (e.g., patrolling velocity) for preventing battery drainage and data
queue overflow of the sensing devices, while up-to-date knowledge on battery
level and data queue of the devices is not available at the UAV. In this paper,
an on-board deep Q-network is developed to minimize the overall data packet
loss of the sensing devices, by optimally deciding the device to be charged and
interrogated for data collection, and the instantaneous patrolling velocity of
the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the
states of battery level and data queue length of sensing devices, channel
conditions, and waypoints given the trajectory of the UAV; and solve it
optimally with Q-learning. Furthermore, we propose the on-board deep Q-network
that can enlarge the state space of the MDP, and a deep reinforcement learning
based scheduling algorithm that asymptotically derives the optimal solution
online, even when the UAV has only outdated knowledge on the MDP states.
Numerical results demonstrate that the proposed deep reinforcement learning
algorithm reduces the packet loss by at least 69.2%, as compared to existing
non-learning greedy algorithms.Comment: 32 pages, 8 figure
On the Performance of Renewable Energy-Powered UAV-Assisted Wireless Communications
We develop novel statistical models of the harvested energy from renewable
energy sources (such as solar and wind energy) considering
harvest-store-consume (HSC) architecture. We consider three renewable energy
harvesting scenarios, i.e. (i) harvesting from the solar power, (ii) harvesting
from the wind power, and (iii) hybrid solar and wind power. In this context, we
first derive the closed-form expressions for the probability density function
(PDF) and cumulative density function (CDF) of the harvested power from the
solar and wind energy sources. Based on the derived expressions, we calculate
the probability of energy outage at UAVs and signal-to-noise ratio (SNR) outage
at ground cellular users. We derive novel closed-form expressions for the
moment generating function (MGF) of the harvested solar power and wind power.
Then, we apply Gil-Pelaez inversion to evaluate the energy outage at the UAV
and signal-to-noise-ratio (SNR) outage at the ground users. We formulate the
SNR outage minimization problem and obtain closed-form solutions for the
transmit power and flight time of the UAV. In addition, we derive novel
closed-form expressions for the moments of the solar power and wind power and
demonstrate their applications in computing novel performance metrics
considering the stochastic nature of the amount of harvested energy as well as
energy arrival time. These performance metrics include the probability of
charging the UAV battery within the flight time, average UAV battery charging
time, probability of energy outage at UAVs, and the probability of eventual
energy outage (i.e. the probability of energy outage in a finite duration of
time) at UAVs
Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure
Current network access infrastructures are characterized by heterogeneity,
low latency, high throughput, and high computational capability, enabling
massive concurrent connections and various services. Unfortunately, this design
does not pay significant attention to mobile services in underserved areas. In
this context, the use of aerial radio access networks (ARANs) is a promising
strategy to complement existing terrestrial communication systems. Involving
airborne components such as unmanned aerial vehicles, drones, and satellites,
ARANs can quickly establish a flexible access infrastructure on demand. ARANs
are expected to support the development of seamless mobile communication
systems toward a comprehensive sixth-generation (6G) global access
infrastructure. This paper provides an overview of recent studies regarding
ARANs in the literature. First, we investigate related work to identify areas
for further exploration in terms of recent knowledge advancements and analyses.
Second, we define the scope and methodology of this study. Then, we describe
ARAN architecture and its fundamental features for the development of 6G
networks. In particular, we analyze the system model from several perspectives,
including transmission propagation, energy consumption, communication latency,
and network mobility. Furthermore, we introduce technologies that enable the
success of ARAN implementations in terms of energy replenishment, operational
management, and data delivery. Subsequently, we discuss application scenarios
envisioned for these technologies. Finally, we highlight ongoing research
efforts and trends toward 6G ARANs.Comment: Accepted by the IEEE Communications Surveys and Tutorial
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