2,012 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
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
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
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
Recent Advances in Fog Radio Access Networks: Performance Analysis and Radio Resource Allocation
As a promising paradigm for the fifth generation wireless communication (5G)
system, the fog radio access network (F-RAN) has been proposed as an advanced
socially-aware mobile networking architecture to provide high spectral
efficiency (SE) while maintaining high energy efficiency (EE) and low latency.
Recent advents are advocated to the performance analysis and radio resource
allocation, both of which are fundamental issues to make F-RANs successfully
rollout. This article comprehensively summarizes the recent advances of the
performance analysis and radio resource allocation in F-RANs. Particularly, the
advanced edge cache and adaptive model selection schemes are presented to
improve SE and EE under maintaining a low latency level. The radio resource
allocation strategies to optimize SE and EE in F-RANs are respectively
proposed. A few open issues in terms of the F-RAN based 5G architecture and the
social-awareness technique are identified as well
Enabling Edge Cooperation in Tactile Internet via 3C Resource Sharing
Tactile Internet often requires (i) the ultra-reliable and ultra-responsive
network connection and (ii) the proactive and intelligent actuation at edge
devices. A promising approach to address these two requirements is to enable
mobile edge devices to share their communication, computation, and caching (3C)
resources via device-to-device (D2D) connections. In this paper, we propose a
general 3C resource sharing framework, which includes many existing 1C/2C
sharing models in the literature as special cases. Comparing with 1C/2C models,
the proposed 3C framework can further improve the resource utilization
efficiency by offering more flexibilities in the device cooperation and
resource scheduling. As a typical example, we focus on the energy utilization
under the proposed 3C framework. Specifically, we formulate an energy
consumption minimization problem under the 3C framework, which is an integer
non-convex optimization problem. To solve the problem, we first transform it
into an equivalent integer linear programming problem that is much easier to
solve. Then, we propose a heuristic algorithm based on linear programming,
which can further reduce the computation time and produce a result that is
empirically close to the optimal solution. Moreover, we evaluate the energy
reduction due to the 3C sharing both analytically and numerically. Numerical
results show that, comparing with the existing 1C/2C approaches, the proposed
3C sharing framework can reduce the total energy consumption by 83.8% when the
D2D energy is negligible. The energy reduction is still 27.5% when the D2D
transmission energy per unit time is twice as large as the cellular
transmission energy per unit time
Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
Caching at the wireless edge is a promising way of boosting spectral
efficiency and reducing energy consumption of wireless systems. These
improvements are rooted in the fact that popular contents are reused,
asynchronously, by many users. In this article, we first introduce methods to
predict the popularity distributions and user preferences, and the impact of
erroneous information. We then discuss the two aspects of caching systems,
namely content placement and delivery. We expound the key differences between
wired and wireless caching, and outline the differences in the system arising
from where the caching takes place, e.g., at base stations, or on the wireless
devices themselves. Special attention is paid to the essential limitations in
wireless caching, and possible tradeoffs between spectral efficiency, energy
efficiency and cache size.Comment: Published in IEEE Communications Magazin
Coded Caching in Fog-RAN: b-Matching Approach
Fog radio access network (Fog-RAN), which pushes the caching and computing
capabilities to the network edge, is capable of efficiently delivering contents
to users by using carefully designed caching placement and content replacement
algorithms. In this paper, the transmission scheme design and coding parameter
optimization will be considered for coded caching in Fog-RAN, where the
reliability of content delivery, i.e., content outage probability, is used as
the performance metric. The problem will be formulated as a complicated
multi-objective probabilistic combinatorial optimization. A novel maximum
b-matching approach will then be proposed to obtain the Pareto optimal solution
with fairness constraint. Based on the fast message passing approach, a
distributed algorithm with a low memory usage of O(M + N) is also proposed,
where M is the number of users and N is the number of Fog-APs. Although it is
usually very difficult to derive the closed-form formulas for the optimal
solution, the approximation formulas of the content outage probability will
also be obtained as a function of coding parameters. The asymptotic optimal
coding parameters can then be obtained by defining and deriving the outage
exponent region (OER) and diversity-multiplexing region (DMR). Simulation
results will illustrate the accuracy of the theoretical derivations, and verify
the outage performance of the proposed approach. Therefore, this paper not only
proposes a practical distributed Fog-AP selection algorithm for coded caching,
but also provides a systematic way to evaluate and optimize the performance of
Fog-RANs.Comment: to appear in IEEE TRANSACTIONS ON COMMUNICATION
Cooperative Edge Caching in User-Centric Clustered Mobile Networks
With files proactively stored at base stations (BSs), mobile edge caching
enables direct content delivery without remote file fetching, which can reduce
the end-to-end delay while relieving backhaul pressure. To effectively utilize
the limited cache size in practice, cooperative caching can be leveraged to
exploit caching diversity, by allowing users served by multiple base stations
under the emerging user-centric network architecture. This paper explores
delay-optimal cooperative edge caching in large-scale user-centric mobile
networks, where the content placement and cluster size are optimized based on
the stochastic information of network topology, traffic distribution, channel
quality, and file popularity. Specifically, a greedy content placement
algorithm is proposed based on the optimal bandwidth allocation, which can
achieve (1-1/e)-optimality with linear computational complexity. In addition,
the optimal user-centric cluster size is studied, and a condition constraining
the maximal cluster size is presented in explicit form, which reflects the
tradeoff between caching diversity and spectrum efficiency. Extensive
simulations are conducted for analysis validation and performance evaluation.
Numerical results demonstrate that the proposed greedy content placement
algorithm can reduce the average file transmission delay up to 50% compared
with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the
optimal clustering is also discussed considering the influences of different
system parameters.Comment: IEEE TM
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