1,437 research outputs found
Network-Connected UAV Communications: Potentials and Challenges
This article explores the use of network-connected unmanned aerial vehicle
(UAV) communications as a compelling solution to achieve high-rate information
transmission and support ultra-reliable UAV remote command and control. We
first discuss the use cases of UAVs and the resulting communication
requirements, accompanied with a flexible architecture for network-connected
UAV communications. Then, the signal transmission and interference
characteristics are theoretically analyzed, and subsequently we highlight the
design and optimization considerations, including antenna design,
non-orthogonal multiple access communications, as well as network selection and
association optimization. Finally, case studies are provided to show the
feasibility of network-connected UAV communications
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Mobile Cloud Computing with a UAV-Mounted Cloudlet: Optimal Bit Allocation for Communication and Computation
Mobile cloud computing relieves the tension between compute-intensive mobile
applications and battery-constrained mobile devices by enabling the offloading
of computing tasks from mobiles to a remote processors. This paper considers a
mobile cloud computing scenario in which the "cloudlet" processor that provides
offloading opportunities to mobile devices is mounted on unmanned aerial
vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system
with frequency division multiplexing between mobile and UAV, the joint
optimization of the number of input bits transmitted in the uplink by the
mobile to the UAV, the number of input bits processed by the cloudlet at the
UAV, and the number of output bits returned by the cloudlet to the mobile in
the downlink in each slot is carried out by means of dual decomposition under
maximum latency constraints with the aim of minimizing the mobile energy
consumption. Numerical results reveal the critical importance of an optimized
bit allocation in order to enable significant energy savings as compared to
local mobile execution for stringent latency constraints.Comment: 21 pages, 3 figures, 1 Table, accepted in IET Communication
Planning UAV Activities for Efficient User Coverage in Disaster Areas
Climate changes brought about by global warming as well as man-made
environmental changes are often the cause of sever natural disasters. ICT,
which is itself responsible for global warming due to its high carbon
footprint, can play a role in alleviating the consequences of such hazards by
providing reliable, resilient means of communication during a disaster crisis.
In this paper, we explore the provision of wireless coverage through UAVs
(Unmanned Aerial Vehicles) to complement, or replace, the traditional
communication infrastructure. The use of UAVs is indeed crucial in emergency
scenarios, as they allow for the quick and easy deployment of micro and pico
cellular base stations where needed. We characterize the movements of UAVs and
define an optimization problem to determine the best UAV coverage that
maximizes the user throughput, while maintaining fairness across the different
parts of the geographical area that has been affected by the disaster. To
evaluate our strategy, we simulate a flooding in San Francisco and the car
traffic resulting from people seeking safety on higher ground
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Aeronautical Ad Hoc Networking for the Internet-Above-The-Clouds
The engineering vision of relying on the ``smart sky" for supporting air
traffic and the ``Internet above the clouds" for in-flight entertainment has
become imperative for the future aircraft industry. Aeronautical ad hoc
Networking (AANET) constitutes a compelling concept for providing broadband
communications above clouds by extending the coverage of Air-to-Ground (A2G)
networks to oceanic and remote airspace via autonomous and self-configured
wireless networking amongst commercial passenger airplanes. The AANET concept
may be viewed as a new member of the family of Mobile ad hoc Networks (MANETs)
in action above the clouds. However, AANETs have more dynamic topologies,
larger and more variable geographical network size, stricter security
requirements and more hostile transmission conditions. These specific
characteristics lead to more grave challenges in aircraft mobility modeling,
aeronautical channel modeling and interference mitigation as well as in network
scheduling and routing. This paper provides an overview of AANET solutions by
characterizing the associated scenarios, requirements and challenges.
Explicitly, the research addressing the key techniques of AANETs, such as their
mobility models, network scheduling and routing, security and interference are
reviewed. Furthermore, we also identify the remaining challenges associated
with developing AANETs and present their prospective solutions as well as open
issues. The design framework of AANETs and the key technical issues are
investigated along with some recent research results. Furthermore, a range of
performance metrics optimized in designing AANETs and a number of
representative multi-objective optimization algorithms are outlined
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Optimal Power Allocation for Secure Directional Modulation Networks with a Full-duplex UAV User
This paper make an investigation of a secure unmanned aerial vehicle
(UAV)-aided communication network based on directional modulation(DM), in which
one ground base station (Alice), one legitimate full-duplex (FD) user (Bob) and
one illegal receiver (Eve) are involved. In this network, Alice acts as a
control center to transmit confidential message and artificial noise (AN). The
UAV user, moving along a linear flight trajectory, is intended to receive the
useful information from Alice. At the same time, it also sends AN signals to
further interference Eve's channel. Aiming at maximizing secrecy rate during
the UAV flight process, a joint optimization problem is formulated
corresponding to power allocation (PA) factors, beamforming vector, AN
projection matrices. For simplicity, maximum ratio transmission, null-space
projection and the leakage-based method are applied to form the transmit
beamforming vector, AN projection matrix at Alice, and AN projection vector at
Bob, respectively. Following this, the optimization problem reduces into a
bivariate optimization programme with two PA factors. We put forward an
alternating iterative algorithm to optimize the two PA factors. Simulation
results demonstrate that the proposed strategy for FD mode achieves a higher SR
than the half-duplex (HD) mode, and outperforms the FD mode with fixed PA
strategy
Machine Learning for Unmanned Aerial System (UAS) Networking
Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale.
With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring.
This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS
Wireless Communication using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization
In this paper, the effective use of flight-time constrained unmanned aerial
vehicles (UAVs) as flying base stations that can provide wireless service to
ground users is investigated. In particular, a novel framework for optimizing
the performance of such UAV-based wireless systems in terms of the average
number of bits (data service) transmitted to users as well as UAVs' hover
duration (i.e. flight time) is proposed. In the considered model, UAVs hover
over a given geographical area to serve ground users that are distributed
within the area based on an arbitrary spatial distribution function. In this
case, two practical scenarios are considered. In the first scenario, based on
the maximum possible hover times of UAVs, the average data service delivered to
the users under a fair resource allocation scheme is maximized by finding the
optimal cell partitions associated to the UAVs. Using the mathematical
framework of optimal transport theory, a gradient-based algorithm is proposed
for optimally partitioning the geographical area based on the users'
distribution, hover times, and locations of the UAVs. In the second scenario,
given the load requirements of ground users, the minimum average hover time
that the UAVs need for completely servicing their ground users is derived. To
this end, first, an optimal bandwidth allocation scheme for serving the users
is proposed. Then, given this optimal bandwidth allocation, the optimal cell
partitions associated with the UAVs are derived by exploiting the optimal
transport theory. Results show that our proposed cell partitioning approach
leads to a significantly higher fairness among the users compared to the
classical weighted Voronoi diagram. In addition, our results reveal an inherent
tradeoff between the hover time of UAVs and bandwidth efficiency while serving
the ground users
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