2,026 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
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
Information-Centric Wireless Networks with Mobile Edge Computing
In order to better accommodate the dramatically increasing demand for data
caching and computing services, storage and computation capabilities should be
endowed to some of the intermediate nodes within the network. In this paper, we
design a novel virtualized heterogeneous networks framework aiming at enabling
content caching and computing. With the virtualization of the whole system, the
communication, computing and caching resources can be shared among all users
associated with different virtual service providers. We formulate the virtual
resource allocation strategy as a joint optimization problem, where the gains
of not only virtualization but also caching and computing are taken into
consideration in the proposed architecture. In addition, a distributed
algorithm based on alternating direction method of multipliers is adopted to
solve the formulated problem, in order to reduce the computational complexity
and signaling overhead. Finally, extensive simulations are presented to show
the effectiveness of the proposed scheme under different system parameters
Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures
The field of wireless networks has been rapidly developed during the past
decade due to the increasing popularity of the mobile devices. The great demand
for mobility and connectivity makes wireless networking a field whose
continuous technological development is very important as new challenges and
issues are arising. Many scientists and researchers are currently engaged in
developing new approaches and optimization methods in several topics of
wireless networking. This survey paper study works from the following topics:
Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks,
Cloud Computing and Resource Management, Interactive Marketing and
Optimization
Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks (C-RANs)
Over the last few years, Cloud Radio Access Network (C-RAN) has arisen as a
transformative architecture for 5G cellular networks that brings the
flexibility and agility of cloud computing to wireless communications. At the
same time, content caching in wireless networks has become an essential
solution to lower the content-access latency and backhaul traffic loading,
which translate into user Quality of Experience (QoE) improvement and network
cost reduction. In this article, a novel Cooperative Hierarchical Caching (CHC)
framework in C-RAN is introduced where contents are jointly cached at the
BaseBand Unit (BBU) and at the Radio Remote Heads (RRHs). Unlike in traditional
approaches, the cache at the BBU, cloud cache, presents a new layer in the
cache hierarchy, bridging the latency/capacity gap between the traditional
edge-based and core-based caching schemes. Trace-driven simulations reveal that
CHC yields up to 80% improvement in cache hit ratio, 21% decrease in average
content-access latency, and 20% reduction in backhaul traffic load compared to
the edge-only caching scheme with the same total cache capacity. Before closing
the article, several challenges and promising opportunities for deploying
content caching in C-RAN are highlighted towards a content-centric mobile
wireless network.Comment: to appear on IEEE Network, July 201
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
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey
Edge networking is a complex and dynamic computing paradigm that aims to push
cloud resources closer to the end user improving responsiveness and reducing
backhaul traffic. User mobility, preferences, and content popularity are the
dominant dynamic features of edge networks. Temporal and social features of
content, such as the number of views and likes are leveraged to estimate the
popularity of content from a global perspective. However, such estimates should
not be mapped to an edge network with particular social and geographic
characteristics. In next generation edge networks, i.e., 5G and beyond 5G,
machine learning techniques can be applied to predict content popularity based
on user preferences, cluster users based on similar content interests, and
optimize cache placement and replacement strategies provided a set of
constraints and predictions about the state of the network. These applications
of machine learning can help identify relevant content for an edge network.
This article investigates the application of machine learning techniques for
in-network caching in edge networks. We survey recent state-of-the-art
literature and formulate a comprehensive taxonomy based on (a) machine learning
technique (method, objective, and features), (b) caching strategy (policy,
location, and replacement), and (c) edge network (type and delivery strategy).
A comparative analysis of the state-of-the-art literature is presented with
respect to the parameters identified in the taxonomy. Moreover, we debate
research challenges and future directions for optimal caching decisions and the
application of machine learning in edge networks.Comment: This is the updated article submitted for publicatio
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
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
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