ArticlePDF Available

Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges

Authors:

Abstract and Figures

The fifth-generation (5G) networks are expected to be able to satisfy users' different quality-of-service (QoS) requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network slicing based 5G systems. In this article, we introduce a logical architecture for network slicing based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network slicing based 5G networks are discussed, including network reconstruction, network slicing management and cooperation with other 5G technologies.
Content may be subject to copyright.
IEEE Communications Magazine • August 2017
138 0163-6804/17/$25.00 © 2017 IEEE
AbstrAct
5G networks are expected to be able to satisfy
users’ different QoS requirements. Network slic-
ing is a promising technology for 5G networks to
provide services tailored for users’ specific QoS
demands. Driven by the increased massive wire-
less data traffic from different application scenar-
ios, efficient resource allocation schemes should
be exploited to improve the flexibility of network
resource allocation and capacity of 5G networks
based on network slicing. Due to the diversity of
5G application scenarios, new mobility manage-
ment schemes are greatly needed to guarantee
seamless handover in network-slicing-based 5G
systems. In this article, we introduce a logical
architecture for network-slicing-based 5G systems,
and present a scheme for managing mobility
between different access networks, as well as a
joint power and subchannel allocation scheme
in spectrum-sharing two-tier systems based on
network slicing, where both the co-tier interfer-
ence and cross-tier interference are taken into
account. Simulation results demonstrate that the
proposed resource allocation scheme can flexibly
allocate network resources between different slic-
es in 5G systems. Finally, several open issues and
challenges in network-slicing-based 5G networks
are discussed, including network reconstruction,
network slicing management, and cooperation
with other 5G technologies.
IntroductIon
With the rapid development and innovations of
mobile networking technologies, an entirely new
era of mobile communications, the fifth genera-
tion (5G) of mobile communication systems, is
coming. There is a consensus that 5G systems
can be rolled out around 2020. 5G systems are
expected to provide society with full connection,
which can break through the limitations of time
and space to create all-dimensional user-centered
or service-centric interconnections between peo-
ple and things [1].
5G networks aim to meet various user quality
of service (QoS) requirements in different applica-
tion scenarios (e.g., in terms of data transmission
rate and latency) [2]. In scenarios where seamless
wide-area coverage is needed, 5G systems should
provide users with seamless high-data-rate services
anytime and anywhere, even at cell edges or with
high-speed (up to 500 km/h) mobility. In metropol-
itan areas where the density and volume of wireless
traffic demand are both very high, 5G networks
should provide dense hotspot coverage with high
capacity. In scenarios where reliable connections
of a large number of widespread low-power nodes
(e.g., wireless sensors) are needed, 5G networks
should be able to connect millions of devices
under the constraints of low power consumption
and low cost per device. Extremely low latency
and high reliability of 5G networks are required to
meet the performance requirements of real-time,
reliable, and secure communications in some ver-
tical industries such as interconnected vehicles and
industrial production control.
Faced with the abundant, distinct, customized
service requirements, and in the new application
scenarios mentioned above, the network archi-
tecture and networking technologies need to be
revisited for 5G systems [3]. This has become
the focus of research and development activities
of operators, equipment vendors, and research
institutes all over the world. In order to provide
customized reliable services using limited network
resources while reducing capital expenditure and
operating expense of 5G networks, network slic-
ing has recently been proposed by the wireless
industry as a main enabler of network service con-
vergence and on-demand customized services
[4–6]. By slicing a physical network into sever-
al logical networks, network slicing can support
on-demand tailored services for distinct appli-
cation scenarios while using the same physical
network. Supported by network slicing, network
resources can be dynamically and efficiently allo-
cated to logical network slices according to the
corresponding QoS demands.
Network slicing has also attracted a lot of
research interest in academia. In [7] a user-cen-
tric service slicing strategy considering different
QoS requirements was proposed based on soft-
ware defined networking (SDN), and a genetic
algorithm was devised to optimize the virtualized
radio resource management based on resource
pooling. In [8], a network slicing mechanism was
introduced for network edge nodes to offer low-la-
Haijun Zhang, Na Liu, Xiaoli Chu, Keping Long, Abdol-Hamid Aghvami, and Victor C. M. Leung
network slIcIng In 5g systems
The authors introduce
a logical architecture for
network-slicing-based
5G systems, and present
a scheme for managing
mobility between differ-
ent access networks, as
well as a joint power and
subchannel allocation
scheme in spectrum-shar-
ing two-tier systems
based on network slicing,
where both the co-tier
interference and cross-tier
interference are taken into
account.
Haijun Zhang and Keping Long are with the Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of
Science and Technology Beijing; Victor C. M. Leung is with the University of British Columbia; Na Liu is with Beijing University of Chemical Technology; Xiaoli Chu is
with the University of Sheffield; Abdol-Hamid Aghvami is with King’s College London. Keping Long is the corresponding author.
Digital Object Identifier:
10.1109/MCOM.2017.1600940
Network Slicing Based 5G and
Future Mobile Networks: Mobility,
Resource Management, and Challenges
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017 139
tency services to users, where the centralized core
network (CN) entities and related applications are
shifted to the network edge to reduce delays and
burdens on the backhaul. The authors of [8] also
proposed mobility management schemes and an
optimal gateway selection algorithm to support
seamless handover. A resource allocation scheme
with consideration of interference management
was presented in [9], where heterogeneous QoS
requirements were guaranteed by optimizing
power and subchannel allocation jointly. In [10],
an agile and flexible SDN-based 5G network archi-
tecture was proposed to allocate physical network
resources to virtual slices within a local area and
to perform scheduling among slices. The SDN-
based network architecture features a unified con-
trol plane, where hierarchical controllers are used
to achieve differentiated services in user access
layers close to the base stations, radio access net-
work (RAN), and CN, respectively. The research on
mobility management in network slicing systems
has mainly been focused on an SDN-based control
and handover procedure [10–12]. In the existing
literature, mobility management and virtualized
resource allocation have not been sufficiently stud-
ied for network-slicing-based 5G networks.
In this article, we present a logical architecture
for network-slicing-based 5G systems, including
an introduction to the fundamental concepts of
network slicing. Based on the proposed network
architecture, we investigate mobility management
and virtualized radio resource allocation technolo-
gies in network-slicing-based 5G systems. Due to
the diversity and complexity of 5G scenarios, it is
vital to study proper mobility management for dif-
ferent mobility scenarios. Accordingly, we present
a handover management scheme for handovers
between different access networks. Virtualized
resource management is responsible for inter-slice
and intra-slice allocation of network resources in
a dynamic and efficient manner. We propose a
joint power and subchannel allocation scheme for
network-slicing-based spectrum-sharing two-tier
networks, where both the co-tier interference and
cross-tier interference are taken into account. Sim-
ulation results show that the proposed resource
allocation scheme can flexibly allocate network
resources between different slices, thereby real-
izing efficient sharing of network resources in 5G
systems. Finally, we highlight the future challenges
and open issues on network slicing in 5G systems.
The remainder of this article is arranged as fol-
lows. The network-slicing-based 5G network archi-
tecture is given, and network slicing management
is described in detail. Next, mobility management
in 5G networks based on network slicing is briefly
discussed. A joint power and subchannel allocation
scheme in spectrum-sharing two-tier systems based
on network slicing is formulated, and the simula-
tion results are given. Several open issues and chal-
lenges in network-slicing-based 5G networks are
discussed. Lastly, the article is summarized.
network-slIcIng-bAsed
5g system ArchItecture
The design of 5G network architectures should
be based on comprehensive consideration of
software control and hardware infrastructure and
the interworking between them. Network slicing,
which can fulfil diverse network requirements
based on the unified physical infrastructure and
sharing network resources, is considered as a
key paradigm to provide several independent-
ly operating instances for a specific network
function [5]. SDN has been widely accepted as
a promising technique to implement network
slicing on the basis of network functions virtual-
ization (NFV) [10]. NFV replaces the traditional
network elements, such as mobility management
entity (MME), policy and charging rules function
(PCRF), and packet/service gateway (P/S-GW),
in the CN and RAN with commercial off-the-shelf
servers, which also host the functions of dedicat-
ed physical infrastructures. Each such server can
be considered as a pool of virtual machines (VMs)
running on commercial off-the-shelf hardware and
software. The traditional RAN is divided into cen-
tralized processing units, such as baseband units
(BBUs) in cloud RAN (C-RAN)) and radio access
units. The centralized processing units are largely
virtualized, where resource pooling is introduced
to perform service slicing in accordance with dif-
ferent QoS requirements [13].
The logical architecture of a 5G system
based on network slicing is given in Fig. 1. In the
radio access plane of the 5G system, a hetero-
geneous network accommodates multiple radio
access technologies (RATs) and supports efficient
cooperation between them. Small cells and WiFi
access points are densely deployed to meet the
increasing data traffic demand in 5G systems [14].
Furthermore, device-to-device (D2D) communi-
cations are used to increase system capacity and
improve energy and spectrum efficiency while
reducing communication delays and relieving the
backhaul burden of macrocells [10]. D2D com-
munications will play a critical role in network-slic-
ing-based 5G systems, especially for improving
quality of local services, emergency communica-
tions, and the Internet of Things (IoT).
As shown in Fig. 1, the traditional centralized
architecture of the CN has evolved into a core
cloud, which separates the control plane from
the user plane so as to reduce control signaling
and delays of data transmissions. The core cloud
provides some important functions of the control
plane, including mobility management, virtualized
resource management, interference management,
and so on. The servers and other functions of the
RAN are located in the edge cloud, which is a
centralized pool of virtualized functionalities. The
edge cloud mainly performs data forwarding and
control plane functions such as baseband process-
ing. The user plane functions in the P/S-GW are
also shifted to the edge cloud, to provide low-la-
tency services and to reduce the burden on the
backhaul. Mobile edge computing platforms are
also deployed in the edge cloud, in conjunction
with data forwarding and content storage servers,
which can collaboratively execute the storage,
computing, and transmission of massive data in
a real-time and efficient way. The corresponding
VMs will be distributed in the core cloud and
edge cloud to execute virtualized network func-
tions. By utilizing SDN, 5G networks can connect
the VMs distributed in the core cloud and edge
cloud, creating the mapping between them. Fur-
thermore, the SDN controllers can control net-
work slicing in a centralized fashion.
There is a consensus
that 5G systems can
be rolled out around
2020. 5G systems are
expected to provide
society with full connec-
tion, which can break
through the limitations
of time and space to
create all-dimensional,
user-centered, or ser-
vice-centric interconnec-
tions between people
and things.
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017
140
After the virtualization and software redefini-
tion of system architecture as described above,
network slicing can be implemented. An example
of network slicing operating on a set of gener-
ic physical infrastructures is illustrated in Fig. 2.
An end-to-end network slice is a specific collec-
tion of network functions and resource allocation
modules isolated from other network slices [5].
For example, the enhanced mobile broadband
(eMBB) slice requires a large bandwidth to sup-
port high-data-rate services, such as high-defini-
tion video streaming and augmented reality. A
caching function, data unit, and cloud unit are
also needed to assist control functions in imple-
menting eMMB slicing services. Reliability, low
latency, and security will be critical for the ultra-re-
liable and low-latency communication (uRLLC)
slice to provide services that are extremely sensi-
tive to latency, such as autonomous driving and
Internet of Vehicles (vehicle-to-everything, V2X).
For uRLLC slice, all dedicated functions should be
instantiated at the edge cloud. For the IoT slice,
which serves a large number of static or dynamic
machine type devices (e.g., sensors and moni-
tors), the vertical applications will be placed on
the upper layer to support the external services
demanded by different commercial tenants.
In network slicing management, the control
parts interact with each other through control-
lers or some kind of interfaces. The virtualized
network function manager is responsible for the
mapping of physical network functions to VMs.
Coordinated with virtualized network function
management (VNFM), the SDN controller oper-
ates and controls the entire virtual network by
connecting the data layer and vertical applications
through the interface protocols. Virtualized infra-
structure management (VIM), as the center of
the virtualized infrastructure, allocates virtualized
resources to VMs by monitoring their resource
utilization status. The network management and
orchestration unit is the core part of slicing man-
agement, because it is responsible for creating,
activating, and deleting network slices according
to customized service requirements.
The network-slicing-based 5G network archi-
tecture will radically change the traditional net-
work planning and deployment patterns. Network
slicing is driven by and tailored for the network
applications and user requirements. By avoiding
mapping each application to a single pipeline in
the physical network, 5G networks can provide
end-to-end tailored services according to custom-
ized application requirements.
mobIlIty mAnAgement In network-
slIcIng-bAsed 5g systems
Mobility management in mobile communications
has evolved from handling simple and single-RAT
handover cases to managing complex, multi-RAT
mobility scenarios. Based on SDN, the control
By utilizing SDN, 5G
networks can connect
the VMs distributed
in the core cloud and
edge cloud, creating the
mapping between them.
Furthermore, the SDN
controllers can control
network slicing in a cen-
tralized fashion.
Figure 1. Network-slicing-based 5G system architecture.
WiFi
D2D
Mesh
MEC
Data center
Content server
Management server
Edge cloud
Core cloud
Network slicing
Backhaul
Fronthaul
Access unit Access unit
Access unit
uRLLC slice
IoT slice
NFV&SDN
Control plane
User plane
eMBB slice
Control plane
User plane
Control unit
D
Dt
t
U
U
U
i
it
Cache
Cloud unit
Cloud unit
Control unit
Data unit
Application V2X
V2X
Cloud unit
Centralization
Distribution
Fronthaul
Fronthaul
Backhaul
Backhaul
Backhaul
Fronthaul
Macro BS
Massive MIMO
Control unit
Data unit Data unit
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017 141
plane and the user plane are split and decoupled
at the gateway in the CN, and the integrated con-
trol functions can reduce control signaling even
for a large number of distributed network nodes.
However, network-slicing-based 5G systems will
still face mobility management challenges caused
by the potentially ultra high density of 5G net-
works combined with high mobility and high
density of end devices. Consequently, new mobil-
ity management schemes need to be developed
for network-slicing-based 5G systems to support
seamless user experience with quality, continuity,
and scalability [12].
Different network slices have different char-
acteristics and requirements in terms of mobili-
ty, latency, and reliability. For instance, in railway
communications, many handovers could be trig-
gered by a high-speed train during a short time
[11]; while in IoT applications, reliable and/or
low-latency communications should be guaran-
teed for many devices with low or no mobility.
In the following, we study the on-demand and
scalable mobility management mechanism under
network slicing for customized service scenarios.
There are two main procedures in mobility
management: location registration and handover
management.
locAtIon regIstrAtIon
Mobile devices register their locations when
they first connect to the network, and then
report their location information to the net-
work periodically. In 5G networks, the home
subscriber servers will be distributed into the
edge cloud, making them closer to end devices
to shorten registration delays and reduce back-
haul burdens. 5G networks will aggregate mul-
tiple heterogeneous RATs. To achieve unified
multi-RAT access and seamless mobility in 5G
networks, multi-RAT coordination is needed for
different RATs to share location information of
their mobile devices.
hAndover mAnAgement
In conventional cellular networks, handovers are
mainly event-triggered. The base station controls
the user terminals to execute the measurement
and report the measured network status infor-
mation to the serving base station. However, in
our proposed network-slicing-based 5G systems,
mobility related events need to be redefined. For
instance, handovers may occur in different slic-
ing scenarios. Flexible handover mechanisms and
adaptive handover thresholds should be exploited
to support mobility management in service-tai-
lored scenarios.
In the proposed mobility management scheme
for network-slicing-based 5G systems, the SDN
is introduced into the RAN, generating the
software-defined wireless network (SDWN). In
SDWN, the single or hierarchical control plane
is deployed close to the edge cloud to support
centralized control plane handover decisions.
One SDN controller can handle handovers in
a single network slice. In a hierarchical control
plane within SDN, it is necessary for controllers to
cooperate [10]. A handover signaling procedure
in network-slicing-based 5G systems is given in
Fig. 3. The user supported by one of the slices is
communicating with other terminals through the
core cloud when the handover is triggered. After
handover is executed successfully, the data will
be transmitted through the target edge cloud and
target access unit to the user from the core cloud.
Due to virtualization, physical network elements
are replaced by corresponding logical servers
in the core cloud and edge cloud. Moreover, in
order to simplify multi-RAT cooperation, only IP
protocols are used to support signaling interac-
tions in the control plane. Existing interfaces are
made open so that a unified interface protocol
can operate flexibly. The SDN controllers located
in the core cloud, the edge cloud, and the access
plane cooperatively carry out handover manage-
ment in complex application scenarios.
Figure 2. Network slicing management.
uRLLC slice
eMBB slice
IoT slice
NFV&SDN
Virtualized
resources
Physical
resources
Compute
Storage
Network
Compute
Storage
Network
Compute
Storage
Network
Compute
Storage
Network
Access
network Edge cloud Core
cloud
Network slices
Application
Control plane
User plane
Dedicated
physical
infrastructures
SDN
controller
VNFM
Management and
orchestration
VIM
Network slicing management
VIM: Virtualized infrastructure management
VNFM: VNF management
In conventional cellular
networks, handovers are
mainly event-triggered.
The base station con-
trols the user terminals
to execute the mea-
surement and report
the measured network
status information to
serving base station.
However, in our pro-
posed network slicing
based 5G systems,
mobility related events
need to be redefined.
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017
142
vIrtuAlIzed resource AllocAtIon wIth
Interference mAnAgement
Network slicing facilitates dynamic and efficient allo-
cation of network resources to meet diverse QoS
requirements [5]. In SDN and NFV enabled network
slicing systems, network resources are virtualized
and managed in the centralized resource pools [7].
Due to limited network resources and increasing-
ly diversified network services, it is challenging to
efficiently provision network resources to network
slices with different QoS requirements. Moreover,
the heterogeneous nature of 5G networks (e.g., dif-
ferent RATs, different cell sizes) also adds complexi-
ties to resource allocation [9]. Especially for densely
deployed spectrum-sharing small cells, efficient and
flexible resource allocation schemes with interfer-
ence awareness are needed [15].
In this section, we present a resource alloca-
tion scheme tailored for different QoS require-
ments of the uRLLC slice, IoT slice, and eMBB
slice, which are the three fundamental categories
Figure 3. Handover procedure based on 5G network slicing systems.
UE Target
access unit Core coud
Source
edge cloud
Target
edge cloud
Source
access unit
Measurement control
Area restriction information provided
Packet data Packet data
UL allocation
Measurement reports Measurement reports
HO decision
HO request
Admission control
HO request ACK
HO request ACK
Handover command
Handover command
Detach from source
cell and synchronizing to
target cell
Deliver buffered and transit
packets to target cell
Data forwarding
Data forwarding
Buffer packets
from source cell
Synchronization
DL allocation
UL allocation + TA for UE
SN state transfer
Handover confirm Handover confirm
Packet data Packet data Packet data
Path switch request User plane update
request
Switch DL path
End marker
Packet data Packet data
End marker
End marker
End marker
User plane update
response
Path switch response
Ack
UE context release
Release
resources
Handover
preparation
Handover
execution
Handover
completion
L3 signaling L1/L2 signalingUser data
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017 143
of network slicing in 5G systems. For example, in
uRLLC slicing scenarios, communication devic-
es are more sensitive to time delay and require
lower transmission rate than those in other slic-
es. There could be mutual interference between
small cells and macrocells, which provide services
(e.g., video streaming) for the eMBB slice and IoT
slice, respectively.
modelIng And formulAtIon
As shown in Fig. 1, the collocated small cells and
macrocell compose a two-tier system in the radio
access plane. Small cells receive two kinds of
interference: cross-tier interference from the mac-
rocell and co-tier interference from neighboring
small cells. In this scenario, we model the uplink
resource allocation problem as the maximization
of uplink capacity on each subchannel for small
cells considering the following constraints:
1. The maximum transmit power of each small
cell user
2. The minimum data rate requirement of each
uRLLC user
3. The threshold of total interference power
received by the macrocell from small cell
users
4. A subchannel that can be allocated to at
most one user in each small cell during one
transmission interval
solutIon bAsed on the
lAgrAngIAn duAl decomposItIon method
The above formulation results in a non-convex
discrete objective function. By relaxing the bina-
ry subchannel allocation indicators into continu-
ous real variables, we transform it into a convex
continuous function, which can be solved using
the Lagrangian dual decomposition method. To
simplify the solution, we decompose the objec-
tive function into a master problem and K N
sub-problems (for K small cells and N subchan-
nels). The Karush-Kuhn-Tucker (KKT) conditions
are used to get the optimal power allocation, and
the sub-gradient method is exploited to update
the Lagrangian multipliers to obtain the optimal
subchannel allocation.
sImulAtIon results
We present simulation results to demonstrate
the performance of a network-slicing-based 5G
network (in conjunction with the proposed sub-
channel and power allocation scheme), where a
suburban environment is considered with small
cells randomly distributed in the macrocell cov-
erage area. The macrocell coverage radius is 500
m and that of a small cell is 10 m. Other system
parameters are set as follows: the carrier fre-
quency is 2 GHz, the 10 MHz channel is divided
into 50 subchannels, the minimum inter-small-
cell distance is 20 m, the maximal transmission
power (of small cell and macrocell users) is 23
dBm, the threshold of interference per subchan-
nel (received by the macrocell) is –101.2 dBm,
and the power spectral density of additive white
Gaussian noise (AWGN) is –174 dBm/Hz. There
are 50 users (requesting IoT services) distribut-
ed randomly in the macrocell, and 2 or 4 users
(requesting uRLLC or eMBB services) camping on
each small cell. The channel model includes path
loss (indoor and outdoor) and frequency-selective
fading. Round-robin scheduling is used in each
cell, and uniform power allocation is adopted for
macrocell users.
Figure 4 shows the total capacity of the eMBB
slice vs. the number of small cells per macrocell.
We can see that the eMBB slice capacity rises
nearly linearly with the density of small cells and
increases slightly with the number of users per
small cell. However, the eMBB slice capacity
decreases significantly due to the inter-cell inter-
ference between small cells, especially at high
small cell densities.
Figure 5 shows that the total capacity of the
uRLLC slice also increases with the number of
small cells, but the capacity of the uRLLC slice is
20 times less than that of the eMBB slice. This is
because the eMBB slice uses large bandwidths to
Figure 4. Total capacity of an eMBB slice vs. the number of small cells.
10 15 20 30 35 4025
Number of small cells
0
100
200
300
400
500
600
700
800
900
Total capacity of eMBB slice (b/s/Hz)
Inter-cell interferenc, 2 users
No inter-cell interferenc, 2 users
Inter-cell interferenc, 4 users
No inter-cell interferenc, 4 users
Figure 5. Total capacity of a uRLLC slice vs. the number of small cells.
0
5
10
15
20
25
30
Total capacity of uRLLC slice (b/s/Hz)
Inter-cell interferenc, 2 users
No inter-cell interferenc, 2 users
Inter-cell interferenc, 4 users
No inter-cell interferenc, 4 users
10 15 20 30 35 4025
Number of small cells
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017
144
transmit massive data, while the uRLLC slice only
transmits low-volume control messages or data
under low-latency constraints.
Figure 6 shows the total capacity of the IoT slice
supported by the macrocell, which suffers from
cross-tier interference from small cells supporting
the eMBB and uRLLC slices. The total capacity of
the IoT slice decreases with the number of small
cells due to the increasing cross-tier interference.
The capacity of the IoT slice will further decrease
due to co-tier interference between macrocells
and between small cells. This is because with
channel quality affected by increased inter-small-
cell interference, small cell users will adaptive-
ly increase their transmit power, leading to an
increase of cross-tier interference from small cells.
The simulation results have shown that in both
latency-sensitive and latency-tolerant network slic-
ing scenarios, the proposed resource allocation
scheme can allocate network resources properly
and efficiently, and can improve system capacity
of dense heterogeneous networks. Due to space
limitations, we will discuss other metrics (e.g.,
latency) in future works.
chAllenges And open Issues
Network slicing is a promising paradigm in future
5G mobile networks, but realizing it is not with-
out challenges [13]. In the following, we discuss
major challenges and open issues on network
slicing in terms of network reconstruction, slic-
ing management, and cooperation with other 5G
technologies.
network reconstructIon
Since 5G networks provide wireless connection
for everything, both the RAN and CN need recon-
struction to support end-to-end network slicing.
Especially in dense heterogeneous networks, not
only should the cooperation of macrocells and
small cells be designed to meet the customized
slicing demands, but also the cooperation of mul-
tiple RATs should be considered to provide seam-
less mobility and high transmission throughput.
network slIcIng mAnAgement
Network slicing supports customized configu-
ration of resources, management models, and
system parameters for various use cases in an iso-
lated or abstract way [5]. Although service provid-
ers and mobile operators have started developing
industrial solutions for network slicing, the man-
agement of network slicing is still a hard nut to
crack. There are many dimensions and technol-
ogies included in network slicing: to create, acti-
vate, maintain, and deactivate network slicing at
the service level; to adjust load balance, charging
policies, security, and QoS at the network level; to
abstract and isolate virtualized network resourc-
es; and inter-slice and intra-slice resource sharing.
Moreover, the complexity and difficulty of net-
work slicing management may increase with the
continued boom of applications and services.
cooperAtIon wIth other 5g technologIes
In future 5G systems, network slicing needs to
coexist and cooperate with traditional technol-
ogies, such as broadband transmission, mobile
cloud engineering (MCE), SDN, and NFV, evolved
from LTE/LTE-A systems. The virtualized cloud of
access networks and CN have the advantages of
physical resource pooling, distribution of software
architectures, and centralization of management.
However, there is still no proper approach to inte-
grate network slicing with C-RAN, SDN, and NFV
to provide point-to-point connection between
physical radio equipment and radio equipment
controller. Cooperation between network slicing
and other 5G technologies is necessary to enable
more network slices in future 5G networks.
conclusIon
In this article, we have presented a logical archi-
tecture for network-slicing-based 5G systems, and
discussed the evolution of network architecture
based on SDN and NFV technologies, as well as
the implementation of network slicing. Based on
the network slicing architecture, we have revised
handover procedures in mobility management,
and discussed mobility management mechanisms
to offer flexible and agile customized services
in network-slicing-based 5G systems. Moreover,
considering various network slicing scenarios, we
have introduced a resource allocation mechanism
tailored for QoS requirements and interference
constraints of uRLLC, eMBB, and IoT service slic-
es. The promising performance of network-slic-
ing-based 5G networks has been demonstrated
through computer simulations.
Acknowledgment
This work was supported by the National Natural
Science Foundation of China (Grant 61471025),
the Young Elite Scientist Sponsorship Program
by CAST (2016QNRC001), and the Fundamental
Research Funds for the Central Universities.
references
[1] H. Zhang et al., “Fronthauling for 5G LTE-U Ultra Dense
Cloud Small Cell Networks,” IEEE Wireless Commun., vol.
23, no. 6, Dec. 2016, pp. 48--53.
[2] A. Osseiran et al., “Scenarios for 5G Mobile and Wireless
Communications: The Vision of the METIS Project,” IEEE
Commun. Mag., vol. 52, no. 5, May 2014, pp. 26--35.
[3] C. L. I et al.,”New Paradigm of 5G Wireless Internet,” IEEE
JSAC, vol. 34, no. 3, Mar. 2016, pp. 474–82.
Figure 6. Total capacity of the IoT slice vs. the number of small cells.
50
60
70
80
90
100
110
120
130
140
Total capacity of IoT slice (b/s/Hz)
Inter-cell interference, 2 users
No inter-cell interference, 2 users
10 15 20 30 35 4025
Number of small cells
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
IEEE Communications Magazine • August 2017 145
[4] P. Rost et al., “Mobile Network Architecture Evolution
toward 5G,” IEEE Commun. Mag., vol. 54, no. 5, May 2016,
pp. 84–91.
[5] M. Jiang, M. Condoluci, and T. Mahmoodi, “Network Slicing
Management & Prioritization in 5G Mobile Systems,” Euro.
Wireless 2016, Oulu, Finland, 2016, pp. 1–6.
[6] Ericsson, “Ericsson White Paper: 5G System,” Jan. 2015.
[7] X. Xu et al., “SDN Based Next Generation Mobile Network
with Service Slicing and Trials,” China Commun., vol. 11, no.
2, Feb. 2014, pp. 65–77.
[8] J. Heinonen et al., “Mobility Management Enhancements
for 5G Low Latency Services,” 2016 IEEE ICC Wksps., Kuala
Lumpur, Malaysia, 2016, pp. 68–73.
[9] H. Zhang et al., “Resource Allocation in Spectrum-Sharing
OFDMA Femtocells with Heterogeneous Services,” IEEE
Trans. Commun., vol. 62, no. 7, July 2014, pp. 2366–77.
[10] V. Yazici, U. C. Kozat, and M. O. Sunay, “A New Control
Plane for 5G Network Architecture with a Case Study on
Unified Handoff, Mobility, and Routing Management,” IEEE
Commun. Mag., vol. 52, no. 11, Nov. 2014, pp. 76–85.
[11] H. Song, X. Fang, and L. Yan, “Handover Scheme for 5G
C/U Plane Split Heterogeneous Network in High-Speed
Railway,” IEEE Trans. Vehic. Tech., vol. 63, no. 9, Nov. 2014,
pp. 4633–46.
[12] S. Kuklinski, Y. Li, and K. T. Dinh, “Handover Management
in SDN-Based Mobile Networks,” 2014 IEEE GLOBECOM
Wksps., Austin, TX, 2014, pp. 194–200.
[13] M. Peng et al., “Fronthaul-Constrained Cloud Radio Access
Networks: Insights and Challenges,” IEEE Wireless Commun.,
vol. 22, no. 2, Apr. 2015, pp. 152–60.
[14] H. Zhang et al., “Coexistence of Wi-Fi and Heterogeneous
Small Cell Networks Sharing Unlicensed Spectrum,” IEEE
Commun. Mag., vol. 53, no. 3, Mar. 2015, pp. 158–64.
[15] H. Zhang et al., “Cooperative Interference Mitigation and
Handover Management for Heterogeneous Cloud Small
Cell Networks,” IEEE Wireless Commun., vol. 22, no. 3, June
2015, pp. 92–99.
bIogrAphIes
Haijun ZH ang [M’13, SM’17] (haijunzhang@ieee.org) is cur-
rently a full professor at the University of Science and Technolo-
gy Beijing, China. He was a postdoctoral research fellow in the
Department of Electrical and Computer Engineering, University
of British Columbia (UBC), Vancouver, Canada. He received his
Ph.D. degree from Beijing University of Posts and Telecommu-
nications (BUPT). From 2011 to 2012, he visited the Centre for
Telecommunications Research, King’s College London, United
Kingdom, as a visiting research associate. He has published
more than 90 papers and authored 2 books. He serves as an
Editor of IEEE 5G Tech Focus, the Journal of Network and Com-
puter Applications, Wireless Networks, Telecommunication Sys-
tems, and KSII Transactions on Internet and Information Systems,
and serves/has served as a leading Guest Editor for IEEE Com-
munications Magazine, IEEE Transactions on Emerging Topics in
Computing, and ACM/Springer MONET. He serves/has served
as General Co-Chair of GameNets ’16 and 5GWN ’17, Sympo-
sium Chair of GameNets ’14, Track Chair of ScalCom ‘15, and
Co-Chair of the Workshop on 5G Ultra Dense Networks at IEEE
ICC 2017 and GLOBECOM 2017. He received the IEEE Com-
Soc Young Author Best Paper Award in 2017.
na Li u (eeliuna@gmail.com) received her B.S. degree in elec-
tronic information engineering from Beijing University of Chem-
ical Technology, China, in 2016. She is currently pursuing an
M.S. degree at the Laboratory of Wireless Communications and
Networks from the College of Information Science and Technol-
ogy, Beijing University of Chemical Technology. Her research
interests include resource allocation, power control, energy
efficiency in wireless communications, software-defined wireless
networks, and visible light communications.
XiaoL i CHu (x.chu@sheffield.ac.uk) is a senior lecturer in the
Department of Electronic and Electrical Engineering at the Uni-
versity of Sheffield, United Kingdom. She received her B.Eng.
degree from Xi’an Jiao Tong University in 2001 and her Ph.D.
degree from the Hong Kong University of Science and Technol-
ogy in 2005. From 2005 to 2012, she was with the Centre for
Telecommunications Research at King’s College London. She
has published over 100 peer-reviewed journal and conference
papers. She is the lead editor/author of the book Heteroge-
neous Cellular Networks — Theory, Simulation and Deployment,
Cambridge University Press, May 2013. She is an Editor for
IEEE Wireless Communications Letters and IEEE Communica-
tions Letters. She was Co-Chair of the Wireless Communications
Symposium at the IEEE ICC 2015, and Workshop Co-Chair for
the IEEE International Conference on Green Computing and
Communications 2013.
Keping Long [SM] (longkeping@ustb.edu.cn) received his M.S.
and Ph.D. degrees at UESTC in 1995 and 1998, respectively. He
worked as an associate professor at BUPT. From July 2001 to
November 2002, he was a research fellow in the ARC Special
Research Centre for Ultra Broadband Information Networks
(CUBIN) at the University of Melbourne, Australia. He is now a
professor and dean at the School of Computer and Communi-
cation Engineering (CCE), Iniverstiy of Science and Technology
Beging. He is a member of the Editorial Committee of Scienc-
es in China Series Fand China Communications . He has also
been a TPC and ISC member for COIN, IEEE IWCN, ICON, and
APOC, and Organizing Co-Chair of of IWCMC ’06, TPC Chair
of COIN ’05/’08, and TPC Co-Chair of COIN ’08/’10, He was
award-ed the National Science Fund Award for Distinguished
Young Scholars of China in 2007 and selected as the Chang
Jiang Scholars Program Professor of China in 2008. His research
interests are optical Internet technology, new generation net-
work technology, wireless information networks, value-added
service, and secure network technology. He has published over
200 papers, 20 keynotes, and invited talks.
Hamid agHva mi [F] (hamid.aghvami@kcl.ac.uk) joined the aca-
demic staff at King’s College London in 1984. In 1989 he was
promoted to reader, and in 1993 was promoted to professor in
telecommunications engineering. He is/was the founder/direc-
tor of the Centre for Telecommunications Research at King’s.
He has published over 580 technical journal and conference
papers, and filed over 30 patents. He was an executive advisor/
chairman/managing director of many wireless communications
companies. He was a member of the Board of Governors of
the IEEE Communications Society in 2001–2003, was a Distin-
guished Lecturer of the IEEE Communications Society in 2004–
2007, and has been a member, Chairman, and Vice-Chairman
of the Technical Program and Organizing Committees of a large
number of international conferences. He is also founder of the
International Symposium on Personal Indoor and Mobile Radio
Communications, a major yearly conference attracting some
1000 attendees. He was awarded the IEEE Technical Committee
on Personal Communications Recognition Award in 2005. He
is a Fellow of the Royal Academy of Engineering and the IET,
and in 2009 was awarded a Fellowship of the Wireless World
Research Forum.
viCto r C . m. Leung [S’75, M’89, SM’97, F’03] (vleung@ece.
ubc.ca) is a professor of electrical and computer engineering
and holder of the TELUS Mobility Research Chair at UBC. His
research is in the areas of wireless networks and mobile systems.
He has co-authored more than 900 technical papers in archival
journals and refereed conference proceedings, several of which
have won best paper awards. He is a Fellow of the Royal Soci-
ety of Canada, a Fellow of the Canadian Academy of Engineer-
ing, and a Fellow of the Engineering Institute of Canada. He is
serving on the Editorial Boards of IEEE JSAC-SGCN, IEEE Wireless
Communications Letters, IEEE Access, and several other journals.
He has provided leadership to the Technical Program Com-
mittees and Organizing Committees of numerous international
conferences. He was the recipient of the 1977 APEBC Gold
Medal, NSERC Postgraduate Scholarships from 1977–1981, a
2012 UBC Killam Research Prize, and an IEEE Vancouver Sec-
tion Centennial Award.
There is still no proper
approach to integrate
network slicing with
C-RAN, SDN and
NFV to provide point-
to-point connection
between physical radio
equipment and radio
equipment controller.
Cooperation between
network slicing and
other 5G technologies
is necessary to enable
more network slices in
future 5G networks.
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on July 01,2022 at 05:32:49 UTC from IEEE Xplore. Restrictions apply.
... Each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network, which delivers a set of services with similar characteristics and is isolated from others [3], [4], [5]. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes [6], [7]. RAN enforcement mechanisms enable a highly efficient resource management service and maximizes the resources configured [8]. ...
... The key aspect of RAN slicing is that the role of the MNO is to coordinate and allocate resources of the substrate network to ensure the harmonic coexistence of multiple RAN slices, while the role of the enterprise is to place slice requests and then manage the provided slices Fig. 1: A high-level architecture for RAN slicing. [17], [18], [19], [6]. In particular, to prepare for a new slice initialization, the enterprise must first determine the required slice functionality and resources needed for VNFs of the requested slice. ...
... The algorithmic considerations of this step are described in the following sections of this paper. Based on the solution to this problem, the MNO can embed more VNFs in the substrate network using SDN and NFV technology [6], [32]. ...
Preprint
Full-text available
5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate's resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efficiently. In particular, we propose four efficient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also conducted to validate the robustness of RAN slicing via the proposed algorithms.
... In recent years, 5G-enabled internet of things (5G-IoT) have attracted much attention due to the features of low cost, low power consumption, and wide coverage, which put forward a challenge on the modulation technique. As it is well known, orthogonal frequency division multiplexing (OFDM) is one classical modulation technique and has been widely applied in many communication standards due to the low implementation complexity and ability to fight against multipath channels [1][2][3]. However, as one of multicarrier modulations, OFDM suffers from the problem of high peak to average power ratio (PAPR), which severely impairs the efficiency of power amplifiers and is unfavourable for the features of low power consumption and wide coverage. ...
Article
Full-text available
For existing orthogonal frequency division multiplexing (OFDM) in 5G internet of things (5G-IoT) systems, one of the critical problems is the high peak to average power ratio (PAPR), which seriously degrades the energy efficiency. To this end, we propose a novel modulation technique with low PAPR for IoT systems, which preserves the advantage of low implementation complexity and ability to fight against multipath channels. The key methodology is the employment of symbol repetition in the frequency domain. Hence, by designing the appropriate phase factors on the repeated symbols, the PAPR of transmitted signals is equivalent to that of an OFDM signal with a reduced size of discrete Fourier transform (DFT). It is demonstrated that, even if there exists repetitive symbols, the proposed method can still maintain an unreduced spectral efficiency performance. Moreover, to evaluate the proposed method, the Monte-Carlo simulations are conducted for the complementary cumulative distribution function (CCDF) and the bit error rate under multipath fading channels. The simulations show that, as CCDF = 10 − 3 , the proposed method can achieve 2.5 dB gains about the PAPR compared with the original OFDM signal.
... The key aspect of RAN slicing is that the role of the mobile network operator(s) (MNOs) is to coordinate and allocate resources of the substrate network to ensure the harmonic coexistence of multiple RAN slices, while the role of the enterprise is to place slice requests and then manage the * Corresponding author: Tu N. Nguyen provided slices [1][2][3][4]. In particular, a RAN slice that is independent from others consists of a set of VNFs. ...
Preprint
Full-text available
5G radio access network (RAN) slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network, which delivers a set of services with similar characteristics. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. A key challenge in building a robust RAN slicing is, therefore, designing a RAN slicing (RS)-configuration scheme that can utilize information such as resource availability in substrate networks as well as the interdependent relationships among slices to map (embed) VNFs onto live substrate nodes. With such motivation, we propose a machine-learning-powered RAN slicing scheme that aims to accommodate maximum numbers of slices (a set of connected Virtual Network Functions - VNFs) within a given request set. More specifically, we present a deep reinforcement scheme that is called Deep Allocation Agent (DAA). In short, DAA utilizes an empirically designed deep neural network that observes the current states of the substrate network and the requested slices to schedule the slices of which VNFs are then mapped to substrate nodes using an optimization algorithm. DAA is trained towards the goal of maximizing the number of accommodated slices in the given set by using an explicitly designed reward function. Our experiment study shows that, on average, DAA is able to maintain a rate of successfully routed slices above 80% in a resource-limited substrate network, and about 60% in extreme conditions, i.e., the available resources are much less than the demands.
... Virtual Network Functions (VNFs) are used to create slices of physical infrastructure based on the grouping of requirements, which include virtual resources, logical topology, traffic regulation, and node and link provisioning rules [4]. The 3rd Generation Partnership Project (3GPP) authorized the 5G system architecture that supports network slicing in its first edition of 5G normative standards [5]. ...
Article
Full-text available
Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to maximize the use of shared resources, it is critical to provide an efficient virtual network embedding strategy for mapping each user’s requests to a physical infrastructure. Virtual network embedding primarily deals with the two most important network parameters—node mapping and link mapping. This paper proposes the heuristic fuzzy algorithm for node mapping and Dijikstra’s algorithm for link mapping. The proposed fuzzy based multi-criteria decision making technique uses membership functions for node parameters to prepare node mapping. By determining the shortest path, Dijikstra’s algorithm is used to provide link mapping. The proposed strategy is tested under dynamic physical infrastructure conditions for validation. The average acceptance ratio, cost-revenue ratio, and average utilization of node capacity and link bandwidth are used to evaluate the performance of the proposed strategy. In addition, the obtained results are compared to the literature to show that the proposed strategy is effective.
... The joint development of emerging mobile communications technologies and healthcare applications for e-health networking contributed to consolidating mobile healthcare (m-health) as a new paradigm that will boost the 5G e-health vertical. Due to its intrinsic characteristics and service properties, the 5G standards classify the e-health vertical as the Enhanced Mobile Broadband (eMBB) type of service [1]. ...
Conference Paper
Full-text available
In the context of the 5G e-health vertical, Network Slicing (NS) promotes mobile e-health (m-health) applications with high innovative facilities through a set of network resource components that can be extended through physical resource virtualization strategies and softwarization. The Cloud-Network Slicing (CNS) approach was recently introduced to offer services across multiple administrative and technological domains distributed across the federated cloud and network infrastructures. The CNS approach can improve m-health user's experience by allowing high content and service delivery flexibility through Multi-Access Edge Computing (MEC) capabilities within the Radio Access Networks (RAN) closer to the healthcare data source. In this scenario, characterized by the inevitability of handover between the various cells existing in the RAN, the infrastructure management system must be extended with improved capabilities to enable handover decisions to maintain the m-health UE experience during mobility events. This paper introduces a network-slicing mobility-aware control approach for paving 5G CNS-enabled systems with automated and proactive mobility control and management capabilities. Simulation results revealed that our proposal could provide m-health applications with service-level slicing-driven handover procedures while keeping connectivity constraints.
... Prioritizing traffic, particularly, video and VoIP traffic will aid in the management of traffic congestion. To address the algorithm-driven mob performance issue, software solutions and network intelligence will be crucial in the proposed effort [17]. ...
Article
Full-text available
Consumer expectations and demands for quality of service (QoS) from network service providers have risen as a result of the proliferation of devices, applications, and services. An exceptional study is being conducted by network design and optimization experts. But despite this, the constantly changing network environment continues to provide new issues that today’s networks must be dealt with effectively. Increased capacity and coverage are achieved by joining existing networks. Mobility management, according to the researchers, is now being investigated in order to make the previous paradigm more flexible, user-centered, and service-centric. Additionally, 5G networks provide higher availability, extremely high capacity, increased stability, and improved connection, in addition to quicker speeds and less latency. In addition to being able to fulfil stringent application requirements, the network infrastructure must be more dynamic and adaptive than ever before. Network slicing may be able to meet the present stringent application requirements for network design, if done correctly. The current study makes use of sophisticated fuzzy logic to create algorithms for mobility and traffic management that are as flexible as possible while yet maintaining high performance. Ultimately, the purpose of this research is to improve the quality of service provided by current mobility management systems while also optimizing the use of available network resources. Building SDN (Software-Defined Networking) and NFV (Network Function Virtualization) technologies is essential. Network slicing is an architectural framework for 5G networks that is intended to accommodate a variety of different networks. In order to fully meet the needs of various use cases on the network, network slicing is becoming more important due to the increasing demand for data rates, bandwidth capacity, and low latency.
... Through network slicing, different logical networks (i.e., slices) are constructed on a shared physical network via SDx, NFV, and cloud/edge computing technologies. Each slice can be considered as an end-to-end virtualized network instance and is customized in terms of communication, computation, and storage resources to meet the specific service requirements [12]. For example, the uRLLC applications demand a low throughput but a very stringent one-way latency of 1 ms, the eMBB applications demand a one-way latency of 4 ms but an average user experience throughput of 50 Mbps in uplink and 100 Mbps in the downlink, and the mMTC applications demand a oneway latency of 10 ms but a very high device density. ...
Article
The fifth-generation of mobile radio technologies is expected to be agile, flexible, and scalable while provisioning ultra-reliable and low-latency communication (uRLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC) applications. An efficient way of implementing these is by adopting cloudification, network function virtualization, and network slicing techniques with open-radio access network (O-RAN) architecture where the base-band processing functions are disaggregated into virtualized radio unit (RU), distributed unit (DU), and centralized unit (CU) over front/mid-haul interfaces. However, cost-efficient solutions are required for designing front/mid-haul interfaces and time-wavelength division multiplexed (TWDM) passive optical network (PON) appears as a potential candidate. Therefore, in this paper, we propose a framework for the optimal placement of RUs based on long-term network statistics and connecting them to open access-edge servers for hosting the corresponding DUs and CUs over front/mid-haul interfaces while satisfying the diverse QoS requirements of uRLLC, eMBB, and mMTC slices. In turn, we formulate a two-stage integer programming problem and time-efficient heuristics for users to RU association and flexible deployment of the corresponding DUs and CUs. We evaluate the O-RAN deployment cost and latency requirements with our TWDM-PON-based framework against urban, rural, and industrial areas and show its efficiency over the optical transport network (OTN)-based framework.
Article
The rise of 5G networks promises a wide range of cutting-edge services with the aim of achieving high performance and reliability. Cutting-edge applications facilitated by 5G architecture make use of various enabling technologies, which introduce various new and emerging security threats and attacks. Threat modeling is a proactive approach to identify security requirements, as well as potential threats and vulnerabilities, and prioritize remediation methods. In addition, 5G networks are complex and are usually divided into separate layers to foster the understanding and management of different functionalities. The open nature of 5G envisages that multiple vendors and service providers might be working on network deployment and service provisioning; it is therefore necessary to address and categorize the threats at each layer distinctly. This paper presents a threat model for 5G-based systems. It leverages the layered 5G architecture, identifying threat categories and mapping these to corresponding layers. It also analyzes enabling technologies affected by identified threats along with threat actors, entry points, and the impact of threat categories. Through the development of this threat model, we envisage facilitating further research into specific threats and mechanisms to protect against them.
Article
Full-text available
The preceding decade has seen successful rollout of 5G and convergence of broadcast broadband and telecom sector. The near future bandwidth demands for services such as advanced immersive multimedia are even more challenging. This has led to advent of 6G. The access technologies need to provide virtually unlimited data rates to support majority of applications in 6G. The optical wireless communication (OWC) with its inherent advantages is a potential enabler in this scenario. However, phenomenon such as atmospheric turbulence poses a serious degradation to performance of such systems. For multimedia services, end user perception is the ultimate quality indicator. To ascertain this quality in quantitative terms, full reference quality metrics are employed for communication purposes. In this paper, digital video broadcasting terrestrial (DVB‐T) videos with varying complexities are transmitted over OWC‐passive optical network (PON) architecture. For performance enhancement 2 × 2 repetitive coding MIMO is employed with maximal ratio combining receiver. A total of 6 video quality assessment (VQA) metrics are evaluated for the system w.r.t channel parameter as Rytov variance. For all the VQA metrics, an enhancement in the performance is observed by using MIMO technique. This performance improvement is more prominent for more complex video as compared to less complex video. As for the limiting case BER of 10−3 gives a limiting link distance of 1350 m; however with VQA metrics, the limiting case distance is 1280 m. By the use of 2 × 2 MIMO techniques, this distance enhances by 20–25 m. In this piece of work, a DVB‐T video that is broadcasted over an OWC‐PON is at variable channel length. A total of 6 VQA parameters are evaluated, and their performance improves with MIMO diversity particularly for more complex videos. Limiting case distance for QoS evaluation is 1350 m, whereas QoE study restricts it to 1280 m, and MIMO enhances the same by 20–25 m.
Article
Full-text available
In this paper we discuss the evolution of mobility management mechanisms in mobile networks. We emphasize problems with current mobility management approaches in case of very high dense and heterogeneous networks. The main contribution of the paper is a discussion on how the Software-Defined Networking (SDN) technology can be applied in mobile networks in order to efficiently handle mobility in the context of future mobile networks (5G) or evolved LTE. The discussion addresses the most important problems related to mobility management like preservation of session continuity and scalability of handovers in very dense mobile networks. Three variants of SDN usage in order to handle mobility are described and compared in this paper. The most advanced of these variants shows how mobility management mechanisms can be easily integrated with autonomie management mechanisms, providing much more advanced functionality than is provided now by the SON approach. Such mechanisms increase robustness of the handover and optimize the usage of wireless and wired mobile network resources.
Article
Full-text available
As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.
Article
Ultra dense cloud small cell network (UDCSNet), which combines cloud computing and massive deployment of small cells, is a promising technology for the fifth-generation (5G) LTE-U mobile communications because it can accommodate the anticipated explosive growth of mobile users' data traffic. As a result, fronthauling becomes a challenging problem in 5G LTE-U UDCSNet. In this article, we present an overview of the challenges and requirements of the fronthaul technology in 5G \mbox{LTE-U} UDCSNets. We survey the advantages and challenges for various candidate fronthaul technologies such as optical fiber, millimeter-wave based unlicensed spectrum, Wi-Fi based unlicensed spectrum, sub 6GHz based licensed spectrum, and free-space optical based unlicensed spectrum.
Article
As a chain is as strong as its weakest element, so are the efficiency, flexibility, and robustness of a mobile network, which relies on a range of different functional elements and mechanisms. Indeed, the mobile network architecture needs particular attention when discussing the evolution of 3GPP EPS because it is the architecture that integrates the many different future technologies into one mobile network. This article discusses 3GPP EPS mobile network evolution as a whole, analyzing specific architecture properties that are critical in future 3GPP EPS releases. In particular, this article discusses the evolution toward a "network of functions," network slicing, and software-defined mobile network control, management, and orchestration. Furthermore, the roadmap for the future evolution of 3GPP EPS and its technology components is detailed and relevant standards defining organizations are listed.
Article
As two major players in terrestrial wireless communications, Wi-Fi systems and cellular networks have different origins and have largely evolved separately. Motivated by the exponentially increasing wireless data demand, cellular networks are evolving towards a heterogeneous and small cell network architecture, wherein small cells are expected to provide very high capacity. However, due to the limited licensed spectrum for cellular networks, any effort to achieve capacity growth through network densification will face the challenge of severe inter-cell interference. In view of this, recent standardization developments have started to consider the opportunities for cellular networks to use the unlicensed spectrum bands, including the 2.4 GHz and 5 GHz bands that are currently used by Wi-Fi, Zigbee and some other communication systems. In this article, we look into the coexistence of Wi-Fi and 4G cellular networks sharing the unlicensed spectrum. We introduce a network architecture where small cells use the same unlicensed spectrum that Wi-Fi systems operate in without affecting the performance of Wi-Fi systems. We present an almost blank subframe (ABS) scheme without priority to mitigate the co-channel interference from small cells to Wi-Fi systems, and propose an interference avoidance scheme based on small cells estimating the density of nearby Wi-Fi access points to facilitate their coexistence while sharing the same unlicensed spectrum. Simulation results show that the proposed network architecture and interference avoidance schemes can significantly increase the capacity of 4G heterogeneous cellular networks while maintaining the service quality of Wi-Fi systems.
Article
5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020's, which are characterized by super high data rate, low latency, high mobility, high energy efficiency, and high traffic density. This paper provides an overview of China Mobile's 5G vision and potential solutions. Targeting a paradigm shift to user-centric network operation from the traditional cell-centric operation, 5G radio access network (RAN) design considerations are presented, including RAN restructure, Turbo charged edge, core network (CN) and RAN function repartition, and network slice as a service. Adaptive multiple connections in the user-centric operation is further investigated, where the decoupled downlink and uplink, decoupled control and data, and adaptive multiple connections provide sufficient means to achieve a 5G network with 'no more cells.' Software-defined air interface (SDAI) is presented under a unified framework, in which the frame structure, waveform, multiple access, duplex mode, and antenna configuration can be adaptively configured. New paradigm of 5G network featuring user-centric network (UCN) and SDAI is needed to meet the diverse yet extremely stringent requirements across the broad scope of 5G scenarios.
Article
Heterogeneous small cell network has attracted much attention to satisfy users' explosive data traffic requirements. Heterogeneous cloud small cell network (HCSNet), which combines cloud computing and heterogeneous small cell network, will likely play an important role in 5G mobile communication networks. However, with massive deployment of small cells, co-channel interference and handover management are two important problems in HCSNet, especially for cell edge users. In this article, we examine the problems of cooperative interference mitigation and handover management in HCSNet. A network architecture is described to combine cloud radio access network with small cells. An effective coordinated multi-point (CoMP) clustering scheme using affinity propagation is adopted to mitigate cell edge users' interference. A low complexity handover management scheme is presented, and its signaling procedure is analyzed in HCSNet. Numerical results show that the proposed network architecture, CoMP clustering scheme and handover management scheme can significantly increase the capacity of HCSNet while maintaining users' quality of service.
Article
The tremendous growth in wireless Internet use is showing no signs of slowing down. Existing cellular networks are starting to be insufficient in meeting this demand, in part due to their inflexible and expensive equipment as well as complex and non-agile control plane. Software-defined networking is emerging as a natural solution for next generation cellular networks as it enables further network function virtualization opportunities and network programmability. In this article, we advocate an all-SDN network architecture with hierarchical network control capabilities to allow for different grades of performance and complexity in offering core network services and provide service differentiation for 5G systems. As a showcase of this architecture, we introduce a unified approach to mobility, handoff, and routing management and offer connectivity management as a service (CMaaS). CMaaS is offered to application developers and over-the-top service providers to provide a range of options in protecting their flows against subscriber mobility at different price levels.