18,454 research outputs found
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
Fog Computing based Radio Access Networks: Issues and Challenges
A fog computing based radio access network (F-RAN) is presented in this
article as a promising paradigm for the fifth generation (5G) wireless
communication system to provide high spectral and energy efficiency. The core
idea is to take full advantages of local radio signal processing, cooperative
radio resource management, and distributed storing capabilities in edge
devices, which can decrease the heavy burden on fronthaul and avoid large-scale
radio signal processing in the centralized baseband unit pool. This article
comprehensively presents the system architecture and key techniques of F-RANs.
In particular, key techniques and their corresponding solutions, including
transmission mode selection and interference suppression, are discussed. Open
issues in terms of edge caching, software-defined networking, and network
function virtualization, are also identified.Comment: 21 pages, 7 figures, accepted by IEEE Networks Magazin
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
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds
Network slicing allows network operators to build multiple isolated virtual
networks on a shared physical network to accommodate a wide variety of services
and applications. With network slicing, service providers can provide a
cost-efficient solution towards meeting diverse performance requirements of
deployed applications and services. Despite slicing benefits, End-to-End
orchestration and management of network slices is a challenging and complicated
task. In this chapter, we intend to survey all the relevant aspects of network
slicing, with the focus on networking technologies such as Software-defined
networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and
Cloud Computing platforms. To build the required background, this chapter
begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their
interplay. Then we cover the 5G vision for network slicing and extend it to the
Fog and Cloud computing through surveying the state-of-the-art slicing
approaches in these platforms. We conclude the chapter by discussing future
directions, analyzing gaps and trends towards the network slicing realization.Comment: 31 pages, 4 figures, Fog and Edge Computing: Principles and
Paradigms, Wiley Press, New York, USA, 201
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
6G: The Next Frontier
The current development of 5G networks represents a breakthrough in the
design of communication networks, for its ability to provide a single platform
enabling a variety of different services, from enhanced mobile broadband
communications, automated driving, Internet-of-Things, with its huge number of
connected devices, etc. Nevertheless, looking at the current development of
technologies and new services, it is already possible to envision the need to
move beyond 5G with a new architecture incorporating new services and
technologies. The goal of this paper is to motivate the need to move to a sixth
generation (6G) of mobile communication networks, starting from a gap analysis
of 5G, and predicting a new synthesis of near future services, like hologram
interfaces, ambient sensing intelligence, a pervasive introduction of
artificial intelligence and the incorporation of technologies, like TeraHertz
(THz) or Visible Light Communications (VLC), 3-dimensional coverage.Comment: This paper was submitted to IEEE Vehicular Technologies Magazine on
the 7th of January 201
Small Cell Deployments: Recent Advances and Research Challenges
This paper summarizes the outcomes of the 5th International Workshop on
Femtocells held at King's College London, UK, on the 13th and 14th of February,
2012.The workshop hosted cutting-edge presentations about the latest advances
and research challenges in small cell roll-outs and heterogeneous cellular
networks. This paper provides some cutting edge information on the developments
of Self-Organizing Networks (SON) for small cell deployments, as well as
related standardization supports on issues such as carrier aggregation (CA),
Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell
Interference Coordination (eICIC), etc. Furthermore, some recent efforts on
issues such as energy-saving as well as Machine Learning (ML) techniques on
resource allocation and multi-cell cooperation are described. Finally, current
developments on simulation tools and small cell deployment scenarios are
presented. These topics collectively represent the current trends in small cell
deployments.Comment: 19 pages, 22 figure
Artificial Intelligence-Defined 5G Radio Access Networks
Massive multiple-input multiple-output antenna systems, millimeter wave
communications, and ultra-dense networks have been widely perceived as the
three key enablers that facilitate the development and deployment of 5G
systems. This article discusses the intelligent agent in 5G base station which
combines sensing, learning, understanding and optimizing to facilitate these
enablers. We present a flexible, rapidly deployable, and cross-layer artificial
intelligence (AI)-based framework to enable the imminent and future demands on
5G and beyond infrastructure. We present example AI-enabled 5G use cases that
accommodate important 5G-specific capabilities and discuss the value of AI for
enabling beyond 5G network evolution
Load Balancing Optimization in LTE/LTE-A Cellular Networks: A Review
During the past few decades wireless technology has seen a tremendous growth.
The recent introduction of high-end mobile devices has further increased
subscriber's demand for high bandwidth. Current cellular systems require manual
configuration and management of networks, which is now costly, time consuming
and error prone due to exponentially increasing rate of mobile users and nodes.
This leads to introduction of self organizing capabilities for network
management with minimum human involvement. It is expected to permit higher end
user Quality of Service (QoS) along with less operational and maintenance cost
for telecom service providers. Self organized cellular networks incorporate a
collection of functions for automatic configuration, optimization and
maintenance of cellular networks. As mobile end users continue to use network
resources while moving from a cell boundary to other, traffic load within a
cell does not remain constant. Thus Load balancing as a part of self organized
network solution, has become one of the most active and emerging fields of
research in Cellular Network. It involves transfer of load from overloaded
cells to the neighbouring cells with free resources for more balanced load
distribution in order to maintain appropriate end-user experience and network
performance. In this paper, review of various load balancing techniques
currently used in mobile networks is presented, with special emphasis on
techniques that are suitable for self optimization feature in future cellular
networks.Comment: Preprin
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