18,634 research outputs found
Six Key Enablers for Machine Type Communication in 6G
While 5G is being rolled out in different parts of the globe, few research
groups around the world such as the Finnish 6G Flagship program have
already started posing the question: \textit{What will 6G be?} The 6G vision is
a data-driven society, enabled by near instant unlimited wireless connectivity.
Driven by impetus to provide vertical-specific wireless network solutions,
machine type communication encompassing both its mission critical and massive
connectivity aspects is foreseen to be an important cornerstone of 6G
development. This article presents an over-arching vision for machine type
communication in 6G. In this regard, some relevant performance indicators are
first anticipated, followed by a presentation of six key enabling technologies.Comment: 14 pages, five figures, submitted to IEEE Communications Magazine for
possible publicatio
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
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
Network Slicing in Fog Radio Access Networks: Issues and Challenges
Network slicing has been advocated by both academia and industry as a
cost-efficient way to enable operators to provide networks on an as-a-service
basis and meet the wide range of use cases that the fifth generation wireless
network will serve. The existing works on network slicing are mainly targeted
at the partition of the core network, and the prospect of network slicing in
radio access networks should be jointly exploited. To solve this challenge, an
enhanced network slicing in fog radio access networks (F-RANs), termed as
access slicing, is proposed. This article comprehensively presents a novel
architecture and related key techniques for access slicing in F-RANs. The
proposed hierarchical architecture of access slicing consists of centralized
orchestration layer and slice instance layer, which makes the access slicing
adaptively implement in an convenient way. Meanwhile, key techniques and their
corresponding solutions, including the radio and cache resource management, as
well as the social-aware slicing, are presented. Open issues in terms of
standardization developments and field trials are identified
Security for Cyber-Physical Systems: Leveraging Cellular Networks and Fog Computing
The reach and scale of Cyber Physical Systems (CPS) are expanding to many
aspects of our everyday lives. Health, safety, transportation and education are
a few areas where CPS are increasingly prevalent. There is a pressing need to
secure CPS, both at the edge and at the network core. We present a hybrid
framework for securing CPS that leverages the computational resources and
coordination of Fog networks, and builds on cellular connectivity for low-power
and resource constrained CPS devices. The routine support for cellular
authentication, encryption, and integrity protection is enhanced with the
addition of a cellular cloud controller to take over the management of the
radio and core security contexts dedicated to CPS devices. Specialized cellular
cloudlets liaison with core network components to implement localized and
network-wide defense for denial-or-service, smart jamming, or unauthorized CPS
tracking attacks. A comparison between our framework and recent cellular/fog
solutions is provided, together with a feasibility analysis for operational
framework deployment. We conclude with future research directions that we
believe are pivotal to the proliferation of secure and scalable CPS.Comment: IEEE CNS 201
Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies
To mitigate the severe inter-tier interference and enhance limited
cooperative gains resulting from the constrained and non-ideal transmissions
between adjacent base stations in heterogeneous networks (HetNets),
heterogeneous cloud radio access networks (H-CRANs) are proposed as
cost-efficient potential solutions through incorporating the cloud computing
into HetNets. In this article, state-of-the-art research achievements and
challenges on H-CRANs are surveyed. In particular, we discuss issues of system
architectures, spectral and energy efficiency performances, and promising key
techniques. A great emphasis is given towards promising key techniques in
H-CRANs to improve both spectral and energy efficiencies, including cloud
computing based coordinated multi-point transmission and reception, large-scale
cooperative multiple antenna, cloud computing based cooperative radio resource
management, and cloud computing based self-organizing network in the cloud
converging scenarios. The major challenges and open issues in terms of
theoretical performance with stochastic geometry, fronthaul constrained
resource allocation, and standard development that may block the promotion of
H-CRANs are discussed as well.Comment: 20 pages, 6 figures, to be published in IEEE Wireless Communication
IoT Stream Processing and Analytics in The Fog
The emerging Fog paradigm has been attracting increasing interests from both
academia and industry, due to the low-latency, resilient, and cost-effective
services it can provide. Many Fog applications such as video mining and event
monitoring, rely on data stream processing and analytics, which are very
popular in the Cloud, but have not been comprehensively investigated in the
context of Fog architecture. In this article, we present the general models and
architecture of Fog data streaming, by analyzing the common properties of
several typical applications. We also analyze the design space of Fog streaming
with the consideration of four essential dimensions (system, data, human, and
optimization), where both new design challenges and the issues arise from
leveraging existing techniques are investigated, such as Cloud stream
processing, computer networks, and mobile computing
Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks
Ultra-dense network (UDN) is a promising technology to further evolve
wireless networks and meet the diverse performance requirements of 5G networks.
With abundant access points, each with communication, computation and storage
resources, UDN brings unprecedented benefits, including significant improvement
in network spectral efficiency and energy efficiency, greatly reduced latency
to enable novel mobile applications, and the capability of providing massive
access for Internet of Things (IoT) devices. However, such great promises come
with formidable research challenges. To design and operate such complex
networks with various types of resources, efficient and innovative
methodologies will be needed. This motivates the recent introduction of highly
structured and generalizable models for network optimization. In this article,
we present some recently proposed large-scale sparse and low-rank frameworks
for optimizing UDNs, supported by various motivating applications. A special
attention is paid on algorithmic approaches to deal with nonconvex objective
functions and constraints, as well as computational scalability.Comment: This paper has been accepted by IEEE Communication Magazine, Special
Issue on Heterogeneous Ultra Dense Network
Cloud Computing - Architecture and Applications
In the era of Internet of Things and with the explosive worldwide growth of
electronic data volume, and associated need of processing, analysis, and
storage of such humongous volume of data, it has now become mandatory to
exploit the power of massively parallel architecture for fast computation.
Cloud computing provides a cheap source of such computing framework for large
volume of data for real-time applications. It is, therefore, not surprising to
see that cloud computing has become a buzzword in the computing fraternity over
the last decade. This book presents some critical applications in cloud
frameworks along with some innovation design of algorithms and architecture for
deployment in cloud environment. It is a valuable source of knowledge for
researchers, engineers, practitioners, and graduate and doctoral students
working in the field of cloud computing. It will also be useful for faculty
members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138
pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link:
https://www.intechopen.com/books/cloud-computing-architecture-and-application
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