Scope of-automated-organizing-network-in-telecom-industry

International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September-2013 672
ISSN 2229-5518
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Scope of Automated Organizing Network in
Telecom Industry
Md. Al-Beruni, Mahfuz Alam, Rashedul Qayum
Abstract— Self-Organizing Networks (SON) is a collection of functions to the radio and network components to interact among
themselves to configure, tune, optimize and healing of cellular system in real time. It is considered to be a necessity in future mobile
networks and operations, due to the increased cost pressure and minimizing trend of human intervention. This paper describes scope,
architecture of reference model, evaluations on the use of some of the most important SON components. We also included the application
of a revolutionary algorithm; genetic algorithm. The application of this algorithm in telecommunication network optimization has presented
and shown with the effective choice of some important parameter values involved in optimization and the implementation strategies.
Nowadays mobile networks are getting more complex to configure, optimize and maintain. SON functions will perform to give cost savings
and performance benefits from the very beginning of a network deployment to its life-cycle.
Index Terms— 3G, 3GPP, CPICH power, eNodeB, Genetic Algorithm, GoS, LTE, SON, UMTS, UE.
——————————  ——————————
1 INTRODUCTION
ELF-Organizing Networks (SON) are one of the critical
factors for the widespread deployment of mobile network
technologies. In present time, network complexity has
reached the stage at which self-configuring, self-optimizing
and self-healing functions are now indispensable for running a
modern mobile network. Hence effective operation means the
automation of as much network planning, design, build and
optimization as possible. It is considered to be a major necessi-
ty in future mobile networks and operations mainly due to
possible savings in capital expenditure (CAPEX) and opera-
tional expenditure (OPEX) by introducing SON shown in Fig.1
This paper considers SON for the 3GPP family (GSM, UMTS,
and LTE) as well as Wi-max technologies.
In some recent research of Aircom,[13] they stated that traffic
will grow at a 48% CAGR (compound annual growth rate)
from 2010 to 2015, from 225PB (Petabyte) per month to 1603PB
per month .In developed regions, revenue per gigabyte will
fall from USD23.21 in 2010 to USD4.27 in 2015 .Not changing
the current business model and cost structure puts in jeopardy
the sustainability of the business.
In this situation SON will help us increase the revenue and
lower the costs of continuous operation. To be more specific,
the main benefits of introducing SON functions in cellular
networks are as follows:
• Increases QoS and support.
• Reduced installation time and costs.
• SON brings benefits to all phases of a mobile network
life cycle.
• Reduced Opex due to reductions in manual efforts in
connection with monitoring, optimizing, diagnosing,
and healing of the network.
• Reduced CAPEX due to more optimized use of net-
work elements and spectrum.
• Improved user experience.
• Improved network performance.
• Dynamic in resource utilization management.
Fig. 1. Network Traffic & Cost Structure.
2 SON ARCHITECTURE
There are three main alternatives regarding the architecture of
SON functions in cellular networks. These are denoted as cen-
tralized, distributed, and hybrid architectures, Fig. 2 Different
SON functions can be implemented by different architectures
in the same network. In this chapter, we briefly describe the
three different architecture options and discuss pros and cons
S
————————————————
• Md. Al-Beruni, Department of EEE
Islamic University of Technology (IUT)
Gazipur, Bangladesh
E-mail: al.beruni05@gmail.com
• Mahfuz Alam, Department of EEE
East West University
Dhaka, Bangladesh
E-mail: mahfuzalam_eee@yahoo.com
• Rashedul Qayum, Department of EEE
Islamic University of Technology (IUT)
Gazipur, Bangladesh
E-mail: bornil20@yahoo.com
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of the different types. [24][22]
Fig. 2. Architecture of a Self Organizing Network.
2.1 Centralized SON Architecture
In a centralized SON architecture, the algorithms are executed
at the network management level. Commands, requests and
parameter settings data flow from the network management
level to the network elements, while measurement data and
reports flow in the opposite direction. The main benefit of this
approach is that the SON algorithms can take information
from all parts of the network into consideration. This means
that it is possible to jointly optimize parameters of all central-
ized SON functions such that the network becomes more
globally optimized, at least for slowly varying network charac-
teristics. In addition, centralized solutions can be more robust
against network instabilities caused by the simultaneous oper-
ation of SON functions having conflicting goals.
2.2 Distributed SON Architecture
In a distributed SON architecture, the SON algorithms are run
in the network nodes and the nodes exchange SON related
messages directly with each other. This architecture can make
the SON functions much more dynamic than centralized SON
solutions, so that the network can adapt to changes much
more quickly. It is also a solution that scales very well as the
number of cells in the network increases. However, in this
architecture, optimizations are done in cell level, which may
differs from network level optimizations.
2.3 Hybrid SON Architecture
Hybrid SON works in the network part by part. For example,
part of its algorithm executes in network management level
and part is run in network elements. This architecture is based
on the advantages of centralized and distributed SON sys-
tems.
This SON architecture has the ability to coordinate centralized
SON functions and it can response quickly to changes at net-
work element level.
3. EVALUATION OF SOME SON APPLICATION
From an operator’s point of view, where the main issue will be
the maturity and possible timing for deploying these features
in mobile networks. We will also touch upon possible savings
and performance gains by introducing SON but we must ad-
mit that the current knowledge is scarce and uncertain in some
cases. The SON functions are usually categorized into three
main groups; self-configuration, self-optimization, and self-
healing. It should be noted that a given SON function can be-
long to more than one of these categories.
3.1 Self-Configuration
The Self-configuration is a collection of algorithms that aims at
reducing the amount of human intervention in the overall in-
stallation process by providing “plug and play” functionality
in network elements such as the E-UTRAN Node-Bs. [19]. This
will result in faster network deployment and reduced costs for
the operator. In addition to a more integral inventory man-
agement system that is less prone to human errors. Self-
configuration is a broad concept which involves several dis-
tinct functions that are covered through specific SON features,
such as automatic software management, self-test, physical
cell ID configuration (PCI), and automatic neighbour relations
(ANR).
The later function is not only used during installation but is
also an important part during normal operations.
Some scopes for self-configuration SON:
• Location verification and update.
• Self-configuration system identifier and parameters.
• Automatic neighbor discovery.
• Auto-configuration of physical radio parameters.
• Plug and play of various equipment and self-setup.
3.1.1 Automatic Neighbour Relation Update
Self-organizing network (SON) capabilities are an important
feature of cutting edge mobile networks. An automated con-
figuration of neighbour cell lists, technically known as Auto-
matic Neighbour Relation (ANR) function, is one of the first
SON features successfully deployed in commercial telecom
networks. [18].By deploying this feature, even for light user
densities, the network attains good handover performance
within the first couple of hours. Without the Automatic
Neighbour Relation (ANR) function, network planning data
has to be used to determine the neighbour cells.
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Fig. 3. Significance of Automatic Neighbour Relation.
Here we are considering eNodeB (enhanced nodeB) for LTE
technology, shown in Fig. 3 Operator’s engineers would have
to manually download these neighbour relations on the
eNodeBs after they have been installed in the field. This pro-
cedure is costly and might lead to a sub-optimal neighbour
relation configuration, given that the level of detail of network
planning data is limited. For instance, it might happen that
handovers between a pair of cells occur in a network which
could not have been predicted from the network planning
data. Furthermore, with the increase of subscriber every time
a new eNodeB is added, again a network planning step and
manual configuration tasks are required to update the affected
NRTs in Fig. 3 The base station maintains a neighbour relation
table (NRT) for each cell. Roughly, each entry contains every-
thing the base station needs to know about a neighbour. Cell 1
has a complete knowledge of its neighbours. Cell 2 only
knows about Cell 1 and not Cell 3. This may be due to predic-
tion errors as a result of inaccuracies in the signal propagation
model and map based data used in a manual planning step.
Cell 4 is newly installed cell and is thus it has not any infor-
mation about its neighbour. A neighbour relation is infor-
mation that a neighbour cell is a neighbour to an eNB. Each
eNB holds a table of detected neighbour cells which are used
in connection with handovers. But in the time of network ex-
pansion updating neighbour relations is continuous and hectic
activity. If the ANR function is active, every active mobile
terminal is configured to report discovered cells if their signal
strength exceeds a predefined threshold. Thus the system can
add necessary neighbour relation automatically and effective-
ly.
3.2 Self-Optimization
Self-optimization functions are aiming at maintaining network
quality and performance with a minimum manual interven-
tion from the operator. This function monitors and analyses
performance data and automatically triggers optimization
action on affected network elements when necessary.
[7][1]This significantly reduces manual interventions and re-
places them with automatic adjustments keeping the network
optimized at all times. Self-optimizing SON functions make it
possible to introduce new automatic processes that are too
fast, and/or too complex to be implemented manually. This
will improve the network performance by making the net-
work more dynamic and adaptable to varying traffic condi-
tions and improve the user experience. Some of the most im-
portant self-optimization SON use:
• Physical cell ID.
• Automatic energy saving.
• Inter-cell Interference coordination.
• Mobility robustness optimization.
• Mobility load balancing optimization.
Fig. 4. SON application of Load Balancing.
3.2.1 Load balancing among cells
The objective of mobility load balancing (MLB) is to intelli-
gently spread user traffic across the system’s radio resources
in order to optimize system capacity. MLB can be used to
shape the system load according to operator policy, or to emp-
ty lightly loaded cells which can then be turned off in order to
save energy. Support for mobility load balancing consists of
one or more of following functions:
• Load reporting.
• Load balancing action based on handovers, traffic
push between cells etc.
• Adapting handover and/or reselection configuration.
In Fig. 4 three neighbor cells traffic load illustrated. In this
case, cell 1 was overloaded, carrying traffic more than 100%.
Whereas, cell 2 and cell 3 were low utilized. However, after
activation of load balancing some extra traffic of cell 1 has
been shared among the other two cells. Thus the system be-
come more stable and all cells loads were around the load
threshold. This SON function not only ensures resource’s
proper utilization but also it provides quality service for the
mobile subscribers.
3.2.2 Automatic Power Saving
With the introduction of new access technologies, such as
Universal Mobile Telecommunications System (UMTS), High
Speed Packet Access (HSPA), and Long Term Evolution
(LTE), and with notably rising data volumes in mobile com-
munication networks, operators have to deployed more and
more equipment in the field. A typical critical cost for the op-
erator is the energy expense which is unavoidable. Today, the
energy demand for a major mobile network is on the order of
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several thousand gigawatts per hour per year. [20][14]
Fig. 5. Illustration of Automatic Power Save.
The objective of energy saving based on switch on/off of a
cell. It results cut of operational expenses. To save energy, dif-
ferent strategies can be applied. They differ by their reaction
time and complexity with respect to the impact on neighbor-
ing cells. From the energy consumption point of view, low
loads should be avoided. Instead, two types of mechanisms
should be applied to reduce idle and unused capacities. As a
first step, all energy-consuming equipment should implement
power-reduction mechanisms while operation mode is run-
ning, adaptation of the actual load (short-term strategies). Se-
cond, the traffic should be reshuffled to a smaller number of
highly loaded sectors or processing entities and the others
should be switched-off (long-term strategies). However, the
coverage and the quality of service must not be degraded ob-
viously. Some of the possible solutions are listed with the de-
ployment of a large number of home eNBs, the energy con-
sumed by home eNBs becomes a big consideration. Since
some home eNBs are just deployed for some special time, dy-
namic activation and deactivation of home eNBs are proposed.
The main issues are when to activate and deactivate the home
eNBs and how. One solution is cell based and MME (MME is
the key control node for LTE access network) involved activa-
tion and deactivation. If the transmission power is too high for
the deployed area, it can also be reduced without impact on
the whole capacity. The adaptation can be based on UE (user
equipment) measurements. When power amplifiers and other
hardware components are introduced that can be operated at
different power levels, or parts of the can be shut down tem-
porarily according to the traffic load, obviously a high power
saving potential can be addressed. Fig. 5 shows the potential
energy savings when adapting the power consumption to the
actual traffic load. This SON function successfully turn off
some active sites when low traffic is monitored in the network.
It is estimated that the average base station energy demand
can be reduced by at least 50 percent by such methods.
3.2.3 Automatic Tilt Optimization
Antenna tilt is defined as the angle between the main beams of
the antenna and the horizontal plane. It is measured in de-
grees and can have positive and negative values. Positive val-
ues mean that the beam is directed downwards, the procedure
is called down tilting, and the tilt value is referred to as down
tilt. Negative values mean that the beam is directed upwards,
the procedure is uptilting and the tilt value becomes up tilt.
[12][1] There are two methods by which tilt can be adjusted,
either mechanical or electrical. Mechanical tilt implies adjust-
ing the mounting brackets of the antenna in such a way that
the whole antenna will be tilted in the desired direction, leav-
ing the radiation pattern unchanged. Electrical tilt is achieved
with a phase shifter in the feed network of the individual an-
tenna's elements, which will allow for a uniform modification
of the radiation pattern. This is a powerful parameter for op-
timization of a cellular network as it has the most direct im-
pact on shaping the best-server boundary of a cell and hence
on the coverage and interference parameters of the network.
In 3G and wi-max networks only by physical optimization
almost 50% optimization can be performed. With the advent
of Remote Electrical Tilt (RET) antennas, tilt optimization
lends itself well to antenna based Self Optimizing Networks
(SON). In addition, antenna tilts are the most effective settings
in the network to affect both coverage and signal quality, bal-
ancing and interference reduction. The adjustments consist in
steps of down tilting for the congested cell and uptilting for
the neighbours. The first step is down tilting the congested
cell, followed by uptilting the neighbours. Since adjustments
should be done automatically. Remote Electrical Tilt (RET)
will be used. This implies that each base station antennas has
to be equipped with a RET module.
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Fig. 6. Illustration of Automatic Tilt Optimization.
The required number of tilt changes will vary depending on
the optimization target, i.e. FR (Full Rate) threshold, user loca-
tion inside the congested cell and the granularity of tilt varia-
tions. Also, a trade-off must be made between the duration of
the optimization process and its accuracy. Self-optimization of
antenna tilt is suited for implementation in a centralized SON
architecture, where the SON process resides on a dedicated
server. This architecture has the advantage of providing global
knowledge of the network, i.e. KPI's from all cells in the net-
work will be gathered and analysed in one location. This is
required both for the congested cell identification process and
for neighbour cell selection. Once a congested cell is identified,
the SON process will send uptilt and down tilt commands to
the RET controllers of the selected cells. After the first com-
mands are sent, the process in Fig. 6 will wait a defined period
of time to analyses the impact of tilt changes and decide if fur-
ther actions are needed or not. The tilt change is followed by a
period of observation of the KPIs and call traces to study the
impact of the change and keep evaluating the performance of
the Critical Zone. At the end of the observation period, the
change in the performance of the critical calls and the Critical
Zone is evaluated particularly with respect to the performance
indicators that identified the optimization problem and trig-
gered the algorithm. If the performance has improved with
respect to these KPIs, another small tilt change is made to the
antenna which, according to the performance indicators gath-
ered during the observation period since the last tilt change,
would have the most impact on optimizing the Critical Cell
and Critical Zone. This iterative process of tilt changes is re-
peated until an antenna tilt change results in performance
degradation as indicated by the performance indicators gath-
ered since the last tilt change.
3.3 Self-healing
Self-healing is a collection of SON procedures which detects
problems and solves or mitigates these to avoid user impact
and to significantly reduce maintenance costs. Self -healing is
triggered by alarms generated by the faulty network elements.
Self-healing encompasses a set of key functions designed to
cope with major service outages, including detection, root
cause analysis, and outage mitigation mechanisms. Auto-
restart and other automatic alarm features afford the network
operator even more quick-response options. It combines con-
figuration and optimization capabilities to dynamically re-
compute parts of the network. The two major areas where the
self-healing concept could be applied are as follows:
• Self-diagnosis: create a model to diagnose, learning
from past experiences.
• Self-healing: automatically start the corrective actions
to solve the problem.
Diagnosis model creation based on the experience of already
solved problems, using a database with faults and their symp-
toms. Automatic troubleshooting action can be done without
human intervention. Some scopes of self-healing SON are as
follows:
• Cell outage recovery.
• Detection and solve coverage hole.
• Self-recovery of network element (NE) software.
• Interference coordination.
• Self-healing of board faults.
For the time being only cell outage is available in vendor’s
roadmaps others will come eventually. This SON function has
two basic components, namely, cell outage detection (COD)
and cell outage compensation (COC). COD uses a collection of
evidence and information to determine if a particular cell is
not working correctly. However, in the case of complete site
failure, SON will be unable to take actions. For the moment
the self-healing SON functions are at its initial phase and are
not mature and stable, though some use cases have been indi-
cated by the vendors, like cell outage detection and compensa-
tion. It is expected that in recent future vendors will be inter-
ested to work on self-healing SON and make SON family
stronger.
4. GENETIC ALGORITHMS FOR NETWORK OPTIMIZATION
The idea of using genetic approaches for optimization origi-
nated from the adaptation in natural and artificial systems.
This algorithm has been using in various optimization and
machine learning process. For the optimizations of the param-
eters of a telecommunication network, i.e. UMTS network,
especially for the capacity optimization of a given UMTS net-
works. [4][2] Let’s consider two basic parameter of a UMTS
network, antenna tilts and CPICH powers. The goal of this
optimization process by genetic algorithm is to increase the
capacity of the network. Let us consider a term GoS (grade of
service), which denotes the ratio of served users over all exist-
ing users. [1] During the optimization process, GoS increases
from an initial value of 95% until it has reached 100%. Howev-
er, practical algorithm applies the following approach. When
GoS reaches a value of 96%, new users are added to the net-
work until the initial defined GoS 95% is reached again. Initial-
ly the value for CPICH power and antenna tilt values are de-
fined randomly. After the initial phase the whole system eval-
uated by network simulator in Fig.7 In the next step the GoS of
the best individual i.e. the set with highest cost value, is com-
pared to the limit of 96%. If the GoS is higher than additional
users are added if not it proceeds to four steps of genetic algo-
rithm. The first one is selection of the individuals for the new
population. In this case the system selects the best CPICH
power and antenna tilt which offers best cost effective output
of the network. These values are selected among the cells of
that network. After selection process next one is recombina-
tion. This process takes account the quality of the cells, i.e.
whether a cell is heavily loaded or not. To ensure this a several
number of mobiles put to extreme condition and record the
data of quality factor. After this the best genes for CPICH
power and antenna tilt are taken from the parents of best qual-
ity-factor indicating cell. Mutation operator is performed with
each individual of the population. CPICH power and antenna
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tilt both separately muted with a mutation probability. For
example, increase 0.5 dBm power for CPICH and decrease 0.5
degree in tilt or vice-versa. After evaluating the system there
comes local optimization. Local optimization consists several
processes like coverage shrinking or expanding by using the
two given parameters. Thus the algorithm continues until it
satisfies the termination condition.
Fig. 7. Flowchart of Genetic Algorithm implementation.
5 SUMMARY
To sum up the discussion in this section we have highlight
some of the main features that are important for an operator
concerning SON:
• Timing of introduction of SON functions.
• Possible savings.
• QoS and performance improvements.
• Coordination between SON functions.
• Expertise for troubleshoot of SON system.
The self-configuration part of SON are mature and should be
included at rollout while most of the self-optimization and
self-healing algorithms are not that important to include at
roll-out and could be set in operation in two to three year time
when the traffic load have increased. When it comes to possi-
ble savings and performance improvements we have only
classified according to high, medium, and low since actual
percentages will depend on several other factors which are
outside the scope of this paper. Another important factor what
SON can deal with in the energy save in the telecom industry.
Hence reducing the energy consumption is a valuable optimi-
zation. We know in all automated system user level interface
is always stress-free but it is very challenging when it requires
any system renovation. To minimize this problem universal
SON features can help to some extent. Moreover, algorithms
are always constituted a popular choice. Choosing the perfect
algorithm for each optimization is a challenge. Finally we can
conclude that adaptability with different network system of
SON module will increase its vast uses.
6 CONCLUSION
SON is designed to the next level of automated operation with
the twin aims of improving network quality and driving down
OPEX. It is therefore important to develop SON solutions for
heterogeneous access technologies. This work has already
started in 3GPP and for some SON functions. For operators,
testing of SON functions will be important. The testing must
include a variety of test cases from plain functional testing to
large scale testing involving several SON functions where also
the stability and interaction between SON components are
tested also with high network load. Today SON is part of the
move to the next generation of radio technology known as
Long Term Evolution (LTE). But to make the most of the very
real benefits of automation, SON’s scope must be extended
beyond LTE. By analysis several alternative solutions may be
compared to find optimal configuration of SON. Current cen-
tralized SON implementations are relatively simple and re-
quire little traffic between the network elements and the net-
work management system. However, as more advanced and
dynamic SON functionality are developed, this traffic will
increase significantly. Hence, a distributed architecture with
more autonomous network elements is expected to be the
most future-proof solution for most SON functions. We all
anticipate SON research and implementation must dominate
next generation mobile network.
ACKNOWLEDGEMENT
First of all, we would like to express our sincere gratitude to
Mustafa Mahmud Hussain, Assistant Professor, Dept. of ECE,
East West University, Bangladesh for generously allocating us
time and resources to complete this paper. We owe special
thanks to Md. Mahmudul Hasan, Senior Lecturer, UITS with
whom we had lengthy discussion related to the topic of auto-
mated UMTS optimization. We are grateful for the comments
received from Md. Moniruzzaman, Optimization Engineer,
Ericsson, Bangladesh & Volcan Ceslic, 3g Optimization Con-
sultant from Turkey. We wish to thank them that they have
supported and pushed for the publication of this manuscript.
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