Collaborative Mobile Network Operators Framework for Efficient

Collaborative Mobile Network Operators Framework
for Efficient Spectrum Utilization in LTE-A
Subah Albinali, Omar Alani
School of Computing, Science & Engineering
University of Salford
Salford, U.K.
[email protected], [email protected]
Abstract— Next generation mobile networks present
significant technical challenges due to the tremendous increase
on data traffic in general and on the other hand limited
resources of the mobile networks’ to be utilized . As a key
element of radio resources management, an efficient and
effective admission control scheme is crucial to guarantee the
Quality of Services (QoS) and to maximize radio resources
utilization simultaneously. This work aims to benefit from the
roaming concept to reduce blocking probability by
implementing a collaborative framework among the mobile
network operators (MNO’s) in order to maintain the required
QoS.
traffic such as real time applications (videoconference,
online gaming and voice over IP) and non-real time
application (emails , downloadable video/audio files) in
addition to different customer classes (subscriptions classes,
emergency calls, priorities calls)are more challenging
problem due to the need to simultaneously adjust between
two kind of probability , namely a dropping probability and
blocking probability[6].
Keywords—QoS , MNO’s,LTE-A,LBO, Spectrum Efficiency,
Blocking probability, CAC.
I.
INTRODUCTION
Mobile networks subscriptions are increasing
significantly every year as more different mobile broadband
applications being adopted. Recent technical reports show a
tremendous increase in data traffic [1]. Between 2012 and
2018 data traffic is expected to grow around 12 times.
Worldwide mobile broadband subscription shows an
increase from 268 million in 2007 to 2.1 billion in 2013. This
reflects an average yearly growing rate of 40%, making
mobile broadband the most active ICT market [2]. All these
developments lead to a really harsh and continuing challenge
on mobile services providers from operational and cost
perspectives due to the limited dedicated mobile spectrum,
where providers have to ensure and maintain their Grade of
Services (GoS) alongside with Quality of Services (QoS).
Moreover, the necessity of providing services at reasonable
cost to the end users put an additional difficulty on the
network design [3]. This growth has been driven by many
factors , mainly by evolution of the mobile generation which
open opportunities for network applications to convert
from bandwidth simple applications [4]. High data rate,
enhanced performance(Quality of Service)and user
experience ( Quality of Experience) will be the key indicator
for the evolution of mobile broadband applications [5]. Fig. 1
shows the tremendous increase in global data traffic in
mobile networks, for illustration data traffic amount have
been doubled between end of 2011 and end of 2012[1].
Provisioning the QoS is a strategic topic in any service
provider network, and mobile service network is no
exception. Supporting QoS requirements of different types of
ISBN: 978-1-902560-27-4 © 2014 PGNet
Fig. 1. Global total data traffic in mobile networks, 2009-2013[1]
This work is about a collaborative framework to define a
Call Admission Control (CAC) by adopting the roaming
concept in terms of collaboration between Mobile Network
Operators (MNO’s) within the LTE-A system deployment,
to reduce the blocking probability and to maintain the
Quality of Service QoS. This approach is driven mainly by
the unbalanced spectrum utilization (occupancy) among
different MNO due to the mismatch of the static spectrum
allocation and dynamic spectrum demands. Additionally,
spectrum has been, and will remain to be a limited resource
for mobile communication. In particular, the continuous
increase in the data rates usage and the corresponding need
for wider bandwidths, it is expected that spectrum will
remain a finite resource, so there is a necessity to find a new
way to utilize the spectrum resources[7].
The paper is organized as follows: Section II covers the
underpinning concept; section III describes the related work;
section IV overviews the LTE-A QOS framework and
roaming infrastructure; section V presents the proposed
solution. Finally, Section VI concludes this paper.
TABLE I. CAC FOR CONTROLLING DROPPING/BLOCKING PROBABILITY [8].
Approach
Guard Band
Loading in home
cell and neighbour
cells
Recourse
availability in home
cell and neighbour
cells.
Estimating handoff
failure probability
Optimum CAC
with handoff failure
probability
constraint
Lower interference
threshold for new
calls
II.
Brief Explanation
A certain amount of
resources are reserved
exclusively for handoff
admission.
New call is admitted if
loading is less than a
threshold value in home
cell and neighbour cells.
New call is admitted if the
needed resources are
available in home cell and
neighbour cells.
Handoff
failure/overloading
probability is estimated
and used as the admission
criterion.
An admission policy is
determined by optimizing
some objective function
subject to handoff
probability constraint.
New calls and handoff
calls are admitted based
on the interference level.
Comments
Leads to high
blocking probability
Information exchange
is needed
Information exchange
is needed
Based on some
assumptions or
approximations
Usually solved by
Markovian Decision
Process
Used to control signal
quality and handoff
probability.
UNDERPINNING CAC CONCEPTS
In general, mobile network operators provide their
services to their mobile users in certain geographical areas,
which consist of cells, each of which has its own operational
bandwidth and base station that mainly control the cell
resources. Essentially, there are two sorts of calls occupying
these resources, handover calls and new calls respectively. In
both cases, a call admission control procedure is triggered to
determine whether or not to accept the call. Fundamentally,
the call admission control constrains the access to the mobile
network based on different criteria, but primarily channel
availability, in order to avoid network congestion and service
degradation as much as possible to fulfill the desired QoS
parameters[8, 9]. The performance of CAC techniques has a
direct effect on each user’s performance and on the
total network performance. Moreover, incoming calls
(handover/new) are accepted/rejected access to the network
by the call admission scheme based on predefined
considerations, (see Table1).Call admission control in
mobile networks has been receiving excessive attention
during the past two eras, in particular due to the evolution of
mobile technology and further due to the fundamental role
that CAC plays in QoS provisioning. In the first and second
generation of wireless systems, CAC was developed for
voice service, which was the dominating service at that time.
In the third generation and beyond, CAC schemes are
becoming more complex due to the evolution of mobile
systems that provide multimedia services with various QoS
levels [3]. In centralized schemes, such as the mobile
switching center (MSC) takeover the admission process in
the whole network as in 3G [10], while in decentralized
(distributed) schemes CAC is performed in each cell by the
base station, such as in the LTE
system [11].In
heterogeneous networks, as a result of collaboration between
different radio access technologies (RATs) such Wi-Fi and
mobile networks, the seamless connection and global
information mobility requirements need a type of
collaborative call admission control scheme, such that a call
in one network has to be capable to move and be handed
over to a different network technology. This is the so-called
vertical handoff [12]. From another point of view, the call
admission control approach, which is based on signal quality
parameters, used to trigger the handoff process may not be
sufficient, hence other system parameters have to be
considered, such as the congestion level in the system in
terms of channel availability [13]. For instance, non-real time
applications can be offered by WLANs in order to mitigate
the congestion in cellular networks. All these factors will
impact the call holding time distribution in both networks,
leading to an increase in the dropping and the blocking
probability, especially during peak hours [14].
III. RELATED WORK
Cognitive Radio (CR) technology assists the progress of
an intelligent and adaptive wireless communications system
that is basically aware of the radio frequency environment.
As such, the communication parameters (such as carrier
frequency, bandwidth and transmission power) can be
dynamically chosen, leading to an efficient spectrum
utilization [15]. However, this technology faces many
technical challenges including protocol design, interference
characterization, environment awareness, as well as
development of distributed algorithms, distributed
measurement techniques, QoS guarantees [16, 17]. In [18]
the author investigates the performance of spectrum renting
solutions to improve spectrum utilization among different
wireless networks for spectrum management. A
heterogeneous architecture that consists of multiple cellular
networks that rent spectrum from each other according to
their spectrum availability is considered. A similar study is
presented in [19], which examines cellular wireless networks
which lease spectrum bands from another system. A threedimensional approach (Markov chain) is developed to
analyze the performance of channel reservation in a cellular
wireless network. The aim is to reduce the blocking and
dropping of calls with the spectrum leasing concept with the
existence of the multi traffic types such as real-time and nonreal time traffic. Yet, the two previous studies show the
benefits of spectrum renting in heterogeneous architectures
and address the trade-offs between different call admission
control policies. These works in fact act as reserved band
approaches, to mitigate the blocking and dropping
probabilities in cellular wireless networks in general (Refer
to Table 1). In the LTE world, many researchers inspired by
the cognitive radio idea to optimize the strategy of call
admission control, intend to reduce the blocking and
dropping probability in addition to utilizing the network
resources in a more effective way. The author in [10]
proposes a cognitive scheme in radio admission control in
the LTE system by authorizing the base station to perform
Admission Control, using a cognitive engine that learns from
past experience of how the admission of a new session
would affect the QoS of all future session. In [20] a
secondary user (SU) access is based on a novel base station
assignment within the LTE-Advanced framework, where
(SU) coexists with the primary users (PU) on a common
basis. However, the proposed framework is meant to
increase the dedicated spectrum utilization within the static
spectrum licensed to the wireless service provider (WSP)
and assumes no cooperation and no spectrum sharing among
WSPs. The author in [21] considers the same idea as [15],
but taking advantage of involving the relay station to take
over the decision in the handover call between the cells. The
aim here is to reduce the transmission time and the spectrum
mobility ratio.
IV. LTE-A QOS FRAMEWORK AND ROAMING
INFRASTRUCTURE
scheduling scheme at eNB level for the channel allocation
process and is determined by the desired number of channels
(Physical Resource Blocks) to achieve the required bit rate.
The allocation and retention priority (ARP) is mainly used in
the bearer decision establishment and modification process
and based on the availability of resources. The priority level
in the ARP is used to differentiate bearers to ensure the
prioritization among them. In addition, the eNB can use
information contained in the ARP in case of congested
network to eliminate bearers due to resource limitations and
assign them to other highly important actions such as the
handover process [22]. Figure 2 shows the end-to-end
service delivery in LTE-A architecture.
A. LTE-A QoS framework .
LTE specifies a bearer’s QoS mainly by quality of
service class identifiers (QCI) as shown in table 2, which
considers as the most important elements the four following
parameters [22]:
1) The resource type: Guaranteed bit rate (GBR) or
Non-guaranteed bit rate.
2) The packet loss rate: An indication of the part of
packets those are lost as a result of errors in
transmission and reception processes.
3) The packet delay budget: An indication, with 98%
assurance, of the delay that a packet receives
between the mobile and the packet data network
(PDN) gateway.
4) The QCI priority level: Helps in scheduling
process.
Fig.2. End-to-End service delivery in LTE-A architecture [16]
B. LTE roaming infrastructure
Home
PLMN
Visited PLMN
S1-MME
S6a
TABLE II. STANDARDIZED QCI CHARACTERISTICS [22]
QCI
Resource
Type
Packet
error/los
s rate
Packet delay
budget (ms)
QCI
Priority
1
GBR
10-2
100
2
-3
2
10
150
4
EXAMPLE
services
Conversationa
l voice
Real-time
video
Real-time
games
Buffered
video
3
10-3
50
3
4
10
-6
300
5
10-6
100
1
IMS signaling
Buffered
video, TCP
file transfers
5
Non- GBR
MME
10-6
300
6
7
10-3
100
7
8
10-6
300
8
9
10-6
300
9
Voice, video,
real-time
games
Buffered
video, TCP
file transfers
Buffered
video, TCP
file transfers
The guaranteed bit rate (GBR) value is a predefined
control function executed at base station (eNB) level; else, an
EPS bearer is considered as a non-guaranteed bit rate bearer
(Non-GBR) [7, 16]. The GBR value is controlled by the
LTE
Signalling
E-UTRAN
S11
S1-U
SGi
S8
S-GW
S5
P-GW
PDN/s servers
SGi
P-GW
6
LTE Traffic
HSS
PDN/s servers
Fig.3. LTE-A roaming infrastructure
The basic principle of roaming is transferring the
information of communication services subscription from
HSS in the home mobile network operator’s (HMNO)
network to MME, correspondingly, in the visited mobile
network operator’s (VMNO) network. Accordingly,
international calls can be conducted using the mobile
infrastructure of the VMNO and the cost of this service
remains with HMNO. However, determining the charging is
decided by the intricate operators. As a result, the roaming
subscriber remains as a HMNO subscriber. Fig. 3 depicts
this concept. Eventually, the home public land/mobile
network (PLMN) would charge the subscriber for those calls
received abroad and a portion of those charges is paid to the
visited PLMN operator [23].
V. PROPOSED SOLUTION
A. Scenario and Discussions
An urban area spot has been considered with three
MNOs sharing the same location with non-uniform mobile
user distribution, which gives this scenario a realistic nature.
As a result, a disparity in utilization will appear, (see Fig.4).
The idea here is to benefit from the unbalanced
utilization (occupancy) among the mobile service providers
to trigger a collaborative concept approach in the congested
network to offload data traffic. This work considers the LTE
roaming infrastructure in terms of the control and charging
policies between MNOs and aims to take advantage of the
local breakout (LBO) feature. LBO is a mechanism where
roaming traffic does not traverse back to the home network
and is handled by the local operator, allowing for cheaper
tariffs and leading to increased localized revenue (see Fig.
5). Networks are only likely to allow LBO given the
guarantee of shared partnerships with other networks or the
creation of a larger network than one that exists in a single
country[24]. In this context, the assumed scenario the
situation is coped with by deploying the roaming concept
and activating the LBO solution, significantly reducing
backhaul traffic and its associated costs.
Fig. 4. Collaprative scenario.
B. Assumptions and considerations for the collobritve
framework and the call admisson contorol
1) Network
infrastructure:
LTE-A
network
infrastructure only, to avoid interoperability and
protocols mapping challenges in legacy systems.
2) Charging and control policy: The roaming model
in [23] will be adopted with some modification in
terminology to cope with the proposed scenario.
3) Mobile Network Operators (MNO’s) capability:
Three mobile operators will be considers with
different capability such as joint partnership and
licenced spectrum (capacity) to help in the offload
process, from cost and operational perspectives.
4) Call type: New call is considered in this work since
the priority is always given to handover call over
the new call request, in which this work intend to
solve the dropping blocking. In addition, to allow
the operators to maintain the QoS without any
degradation below the acceptance level to accept
new call.
5) Call traffic class: Dealing with non-real time call
to allow the collaborative process between the
operators within reasonable timely manner without
harming the QoS in addition and also to allow the
local break out feature to join in the offloading
process.
Fig. 5. Illustrations to LBO in Roaming [23]
VI. CONCLUSION AND FUTURE WORK
Radio resource management (RRM) is the controller of
mobile network performance level. It controls the level of
co-channel interference and other radio transmission
characteristics in the mobile network via schemes and
algorithms for adjusting parameters such as transmission
power, channel allocation, data rate, handover criteria,
admission criteria, modulation and error coding schemes.
The objective in this work is to utilize the limited spectrum
resources and mobile network infrastructure, in an efficient
and effective approach to overcome the call blocking; and to
maintain the quality of service. Since the cost of deploying a
network element, such as a base station, is not efficient in
some cases due to site considerations, which come with
additional expenses (for example operational cost or
frequency license fees) or even QoS degradation due to
possible interference. An overview of a proposed
collaborative framework has been given motivated by the
unbalanced occupancy phenomena among different mobile
services providers, which gives an opportunity to offload
data by diverting it to alternative mobile providers and also
by implementing LBO, which acts as a second method of
diversion. A call admission control will be defined to handle
this dilemma after specifying interworking level, interfaces
and involved protocols. One potential solution, which will be
considered as a future approach, is virtualization techniques
that have the potential of enabling network sharing along
with management flexibility and independence between the
sharing entities where the physical network infrastructure is
shared by multiple entities. To this end, sharing networks is a
practical solution to utilize the spectrum resources in a more
efficient way as well as to reduce network expenditure.
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