1,726 research outputs found
Hypergraph Theory: Applications in 5G Heterogeneous Ultra-Dense Networks
Heterogeneous ultra-dense network (HUDN) can significantly increase the
spectral efficiency of cellular networks and cater for the explosive growth of
data traffic in the fifth-generation (5G) communications. Due to the dense
deployment of small cells (SCs), interference among neighboring cells becomes
severe. As a result, the effective resource allocation and user association
algorithms are essential to minimize inter-cell interference and optimize
network performance. However, optimizing network resources in HUDN is extremely
complicated as resource allocation and user association are coupled. Therefore,
HUDN requires low-complexity but effective resource allocation schemes to
address these issues. Hypergraph theory has been recognized as a useful
mathematical tool to model the complex relations among multiple entities. In
this article, we show how the hypergraph models can be used to effectively
tackle resource allocation problems in HUDN. We also discuss several potential
research issues in this field
Towards Service-oriented 5G: Virtualizing the Networks for Everything-as-a-Service
It is widely acknowledged that the forthcoming 5G architecture will be highly
heterogeneous and deployed with a high degree of density. These changes over
the current 4G bring many challenges on how to achieve an efficient operation
from the network management perspective. In this article, we introduce a
revolutionary vision of the future 5G wireless networks, in which the network
is no longer limited by hardware or even software. Specifically, by the idea of
virtualizing the wireless networks, which has recently gained increasing
attention, we introduce the Everything-as-a-Service (XaaS) taxonomy to light
the way towards designing the service-oriented wireless networks. The concepts,
challenges along with the research opportunities for realizing XaaS in wireless
networks are overviewed and discussed.Comment: 18 pages, 5 figure
Towards 6G Networks: Use Cases and Technologies
Reliable data connectivity is vital for the ever increasingly intelligent,
automated and ubiquitous digital world. Mobile networks are the data highways
and, in a fully connected, intelligent digital world, will need to connect
everything, from people to vehicles, sensors, data, cloud resources and even
robotic agents. Fifth generation (5G) wireless networks (that are being
currently deployed) offer significant advances beyond LTE, but may be unable to
meet the full connectivity demands of the future digital society. Therefore,
this article discusses technologies that will evolve wireless networks towards
a sixth generation (6G), and that we consider as enablers for several potential
6G use cases. We provide a full-stack, system-level perspective on 6G scenarios
and requirements, and select 6G technologies that can satisfy them either by
improving the 5G design, or by introducing completely new communication
paradigms.Comment: The paper has been accepted for publication at the IEEE
Communications Magazine, 202
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
The Wireless Control Plane: An Overview and Directions for Future Research
Software-defined networking (SDN), which has been successfully deployed in
the management of complex data centers, has recently been incorporated into a
myriad of 5G networks to intelligently manage a wide range of heterogeneous
wireless devices, software systems, and wireless access technologies. Thus, the
SDN control plane needs to communicate wirelessly with the wireless data plane
either directly or indirectly. The uncertainties in the wireless SDN control
plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP,
and indirect WCP, I-WCP) have been incorporated into recent 5G networks;
however, a discussion of their design principles and their design limitations
is missing. This paper introduces an overview of the WCP design (I-WCP and
D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G
networks. Furthermore, to facilitate synthesizing a robust WCP, this paper
proposes a generic WCP framework using deep reinforcement learning (DRL)
principles and presents a roadmap for future research.Comment: This paper has been accepted to appear in Elsevier Journal of
Networks and Computer Applications. It has 34 pages, 8 figures, and two
table
Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks
The next-generation wireless networks are evolving into very complex systems
because of the very diversified service requirements, heterogeneity in
applications, devices, and networks. The mobile network operators (MNOs) need
to make the best use of the available resources, for example, power, spectrum,
as well as infrastructures. Traditional networking approaches, i.e., reactive,
centrally-managed, one-size-fits-all approaches and conventional data analysis
tools that have limited capability (space and time) are not competent anymore
and cannot satisfy and serve that future complex networks in terms of operation
and optimization in a cost-effective way. A novel paradigm of proactive,
self-aware, self- adaptive and predictive networking is much needed. The MNOs
have access to large amounts of data, especially from the network and the
subscribers. Systematic exploitation of the big data greatly helps in making
the network smart, intelligent and facilitates cost-effective operation and
optimization. In view of this, we consider a data-driven next-generation
wireless network model, where the MNOs employ advanced data analytics for their
networks. We discuss the data sources and strong drivers for the adoption of
the data analytics and the role of machine learning, artificial intelligence in
making the network intelligent in terms of being self-aware, self-adaptive,
proactive and prescriptive. A set of network design and optimization schemes
are presented with respect to data analytics. The paper is concluded with a
discussion of challenges and benefits of adopting big data analytics and
artificial intelligence in the next-generation communication system
Leveraging Synergy of 5G SDWN and Multi-Layer Resource Management for Network Optimization
Fifth-generation (5G) cellular wireless networks are envisioned to predispose
service-oriented, flexible, and spectrum/energy-efficient edge-to-core
infrastructure, aiming to offer diverse applications. Convergence of
software-defined networking (SDN), software-defined radio (SDR) compatible with
multiple radio access technologies (RATs), and virtualization on the concept of
5G software-defined wireless networking (5G-SDWN) is a promising approach to
provide such a dynamic network. The principal technique behind the 5G-SDWN
framework is the separation of the control and data planes, from the deep core
entities to edge wireless access points (APs). This separation allows the
abstraction of resources as transmission parameters of each user over the
5G-SDWN. In this user-centric and service-oriented environment, resource
management plays a critical role to achieve efficiency and reliability.
However, it is natural to wonder if 5G-SDWN can be leveraged to enable
converged multi-layer resource management over the portfolio of resources, and
reciprocally, if CML resource management can effectively provide performance
enhancement and reliability for 5G-SDWN. We believe that replying to these
questions and investigating this mutual synergy are not trivial, but
multidimensional and complex for 5G-SDWN, which consists of different
technologies and also inherits legacy generations of wireless networks. In this
paper, we propose a flexible protocol structure based on three mentioned
pillars for 5G-SDWN, which can handle all the required functionalities in a
more crosslayer manner. Based on this, we demonstrate how the general framework
of CML resource management can control the end user quality of experience. For
two scenarios of 5G-SDWN, we investigate the effects of joint user-association
and resource allocation via CML resource management to improve performance in a
virtualized network
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
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
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
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