20,042 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
Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption
A promising technique to provide mobile applications with high computation
resources is to offload the processing task to the cloud. Utilizing the
abundant processing capabilities of the clouds, mobile edge computing enables
mobile devices with limited batteries to run resource hungry applications and
to save power. However, it is not always true that edge computing consumes less
power compared to device computing. It may take more power for the mobile
device to transmit a file to the cloud than running the task itself. This paper
investigates the power minimization problem for the mobile devices by data
offloading in multi-cell multi-user OFDMA mobile edge computing networks. We
consider the maximum acceptable delay as QoS metric to be satisfied in our
network. We formulate the problem as a mixed integer nonlinear problem which is
converted into a convex form using D.C. approximation. To solve the converted
optimization problem, we have proposed centralized and distributed algorithms
for joint power allocation and channel assignment together with
decision-making. Simulation results illustrate that by utilizing the proposed
algorithms, considerable power savings can be achieved, e.g., about 60 % for
large bit stream size compared to local computing baseline
MEC-aware Cell Association for 5G Heterogeneous Networks
The need for efficient use of network resources is continuously increasing
with the grow of traffic demand, however, current mobile systems have been
planned and deployed so far with the mere aim of enhancing radio coverage and
capacity. Unfortunately, this approach is not sustainable anymore, as 5G
communication systems will have to cope with huge amounts of traffic,
heterogeneous in terms of latency among other Qualityof- Service (QoS)
requirements. Moreover, the advent of Multiaccess Edge Computing (MEC) brings
up the need to more efficiently plan and dimension network deployment by means
of jointly exploiting the available radio and processing resources. From this
standpoint, advanced cell association of users can play a key role for 5G
systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a
comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell
association rules, taking the scenario of task offloading in the Uplink (UL) as
an example. Numerical evaluations show that the proposed cell association rule
provides nearly 60% latency reduction, as compared to its standard,
radio-exclusive counterpart.Comment: 2018 IEEE Wireless Communications and Networking Conference Workshops
(WCNCW): The First Workshop on Control and management of Vertical slicing
including the Edge and Fog Systems (COMPASS
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
Density-aware Dynamic Mobile Networks: Opportunities and Challenges
We experience a major paradigm change in mobile networks. The infrastructure
of cellular networks becomes mobile as it is densified by using mobile and
nomadic small cells to increase coverage and capacity. Furthermore, the
innovative approaches such as green operation through sleep scheduling,
user-controlled small cells, and end-to-end slicing will make the network
highly dynamic. Mobile cells, while bringing many benefits, introduce many
unconventional challenges that we present in this paper. We have to introduce
novel techniques for adapting network functions, communication protocols and
their parameters to network density. Especially when cells on wheels or wings
are considered, static and man-made configurations will waste valuable
resources such as spectrum or energy if density is not considered as an
optimization parameter. In this paper, we present the existing density
estimators. We analyze the impact of density on coverage, interference,
mobility management, scalability, capacity, caching, routing protocols and
energy consumption. We evaluate nomadic cells in dynamic networks in a
comprehensive way and illustrate the potential objectives we can achieve by
adapting mobile networks to base station density. The main challenges we may
face by employing dynamic networks and how we can tackle these problems are
discussed in detail
Edge Computing Aware NOMA for 5G Networks
With the fast development of Internet of things (IoT), the fifth generation
(5G) wireless networks need to provide massive connectivity of IoT devices and
meet the demand for low latency. To satisfy these requirements, Non-Orthogonal
Multiple Access (NOMA) has been recognized as a promising solution for 5G
networks to significantly improve the network capacity. In parallel with the
development of NOMA techniques, Mobile Edge Computing (MEC) is becoming one of
the key emerging technologies to reduce the latency and improve the Quality of
Service (QoS) for 5G networks. In order to capture the potential gains of NOMA
in the context of MEC, this paper proposes an edge computing aware NOMA
technique which can enjoy the benefits of uplink NOMA in reducing MEC users'
uplink energy consumption. To this end, we formulate a NOMA based optimization
framework which minimizes the energy consumption of MEC users via optimizing
the user clustering, computing and communication resource allocation, and
transmit powers. In particular, similar to frequency Resource Blocks (RBs), we
divide the computing capacity available at the cloudlet to computing RBs.
Accordingly, we explore the joint allocation of the frequency and computing RBs
to the users that are assigned to different order indices within the NOMA
clusters. We also design an efficient heuristic algorithm for user clustering
and RBs allocation, and formulate a convex optimization problem for the power
control to be solved independently per NOMA cluster. The performance of the
proposed NOMA scheme is evaluated via simulations
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
Intelligent Processing in Vehicular Ad hoc Networks: a Survey
The intelligent Processing technique is more and more attractive to
researchers due to its ability to deal with key problems in Vehicular Ad hoc
networks. However, several problems in applying intelligent processing
technologies in VANETs remain open. The existing applications are
comprehensively reviewed and discussed, and classified into different
categories in this paper. Their strategies, advantages/disadvantages, and
performances are elaborated. By generalizing different tactics in various
applications related to different scenarios of VANETs and evaluating their
performances, several promising directions for future research have been
suggested.Comment: 11pages, 5 figure
Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment
An edge computing environment features multiple edge servers and multiple
service clients. In this environment, mobile service providers can offload
client-side computation tasks from service clients' devices onto edge servers
to reduce service latency and power consumption experienced by the clients. A
critical issue that has yet to be properly addressed is how to allocate edge
computing resources to achieve two optimization objectives: 1) minimize the
service cost measured by the service latency and the power consumption
experienced by service clients; and 2) maximize the service capacity measured
by the number of service clients that can offload their computation tasks in
the long term. This paper formulates this long-term problem as a stochastic
optimization problem and solves it with an online algorithm based on Lyapunov
optimization. This NP-hard problem is decomposed into three sub-problems, which
are then solved with a suite of techniques. The experimental results show that
our approach significantly outperforms two baseline approaches.Comment: This paper has been accepted by Early Submission Phase of ICWS201
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
- …