3,744 research outputs found
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
An Edge Computing Empowered Radio Access Network With UAV-Mounted FSO Fronthaul and Backhaul: Key Challenges and Approaches
One promising approach to address the supply-demand mismatch between the
terrestrial infrastructure and the temporary and/or unexpected traffic demands
is to leverage the unmanned aerial vehicle (UAV) technologies. Motivated by the
recent advancement of UAV technologies and retromodulator based free space
optical communication, we propose a novel edge-computing empowered radio access
network architecture where the fronthaul and backhaul links are mounted on the
UAVs for rapid event response and flexible deployment. The implementation of
hardware and networking technologies for the proposed architecture are
investigated. Due to the limited payload and endurance as well as the high
mobility of UAVs, research challenges related to the communication resource
management and recent research progress are reported.Comment: This work is accepted by IEEE Wireless Communications Magazin
SkyLiTE: End-to-End Design of Low-Altitude UAV Networks for Providing LTE Connectivity
Un-manned aerial vehicle (UAVs) have the potential to change the landscape of
wide-area wireless connectivity by bringing them to areas where connectivity
was sparing or non-existent (e.g. rural areas) or has been compromised due to
disasters. While Google's Project Loon and Facebook's Project Aquila are
examples of high-altitude, long-endurance UAV-based connectivity efforts in
this direction, the telecom operators (e.g. AT&T and Verizon) have been
exploring low-altitude UAV-based LTE solutions for on-demand deployments.
Understandably, these projects are in their early stages and face formidable
challenges in their realization and deployment. The goal of this document is to
expose the reader to both the challenges as well as the potential offered by
these unconventional connectivity solutions. We aim to explore the end-to-end
design of such UAV-based connectivity networks particularly in the context of
low-altitude UAV networks providing LTE connectivity. Specifically, we aim to
highlight the challenges that span across multiple layers (access, core
network, and backhaul) in an inter-twined manner as well as the richness and
complexity of the design space itself. To help interested readers navigate this
complex design space towards a solution, we also articulate the overview of one
such end-to-end design, namely SkyLiTE-- a self-organizing network of
low-altitude UAVs that provide optimized LTE connectivity in a desired region
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
When Machine Learning Meets Big Data: A Wireless Communication Perspective
We have witnessed an exponential growth in commercial data services, which
has lead to the 'big data era'. Machine learning, as one of the most promising
artificial intelligence tools of analyzing the deluge of data, has been invoked
in many research areas both in academia and industry. The aim of this article
is twin-fold. Firstly, we briefly review big data analysis and machine
learning, along with their potential applications in next-generation wireless
networks. The second goal is to invoke big data analysis to predict the
requirements of mobile users and to exploit it for improving the performance of
"social network-aware wireless". More particularly, a unified big data aided
machine learning framework is proposed, which consists of feature extraction,
data modeling and prediction/online refinement. The main benefits of the
proposed framework are that by relying on big data which reflects both the
spectral and other challenging requirements of the users, we can refine the
motivation, problem formulations and methodology of powerful machine learning
algorithms in the context of wireless networks. In order to characterize the
efficiency of the proposed framework, a pair of intelligent practical
applications are provided as case studies: 1) To predict the positioning of
drone-mounted areal base stations (BSs) according to the specific tele-traffic
requirements by gleaning valuable data from social networks. 2) To predict the
content caching requirements of BSs according to the users' preferences by
mining data from social networks. Finally, open research opportunities are
identified for motivating future investigations.Comment: This article has been accepted by IEEE Vehicular Technology Magazin
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Bi-Directional Mission Offloading for Agile Space-Air-Ground Integrated Networks
Space-air-ground integrated networks (SAGIN) provide great strengths in
extending the capability of ground wireless networks. On the other hand, with
rich spectrum and computing resources, the ground networks can also assist
space-air networks to accomplish resource-intensive or power-hungry missions,
enhancing the capability and sustainability of the space-air networks.
Therefore, bi-directional mission offloading can make full use of the
advantages of SAGIN and benefits both space-air and ground networks. In this
article, we identify the key role of network reconfiguration in coordinating
heterogeneous resources in SAGIN, and study how network function virtualization
(NFV) and service function chaining (SFC) enable agile mission offloading. A
case study validates the performance gain brought by bi-directional mission
offloading. Future research issues are outlooked as the bi-directional mission
offloading framework opens a new trail in releasing the full potentials of
SAGIN.Comment: accepted by IEEE Wireless Communications Magazin
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
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
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
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