Mobility-Aware Cell Clustering Mechanism for Self-Organizing Networks

Abstract

Self Organizing Networks (SON) which automate the mobile networks in a cost-efficient way can provide extensive benefits for Mobile Network Operators (MNOs) that are facing several challenges such as providing maximum coverage and balanced/efficient usage of spectrum and energy. However, a major challenge in future wireless cellular systems is the design of self-organizing architecture that can enable re-configurable, scalable, flexible, low-cost and efficient solutions for supporting increasingly diverse applications, products and services of user and network requirements. In this paper, we discuss the benefits and opportunities of mobility-aware clustering capability on different conventional SON functions and advanced Long Term Evolution (LTE) features. We further propose a novel cell clustering methodology which utilizes handover attempt data and aims the collect the cells that have higher mobility activity in a single cluster. The main benefit of the clustering mechanism is to reduce the complexity of network optimization processes via checking smaller number of cells instead of evaluating entire network as their number is gradually increasing. We consider real-world data of evolved Node-Bs (eNodeBs) in one of the major city while evaluating our novel clustering technique that considers the mobility activities and location information of eNodeBs. In our evaluations, k – Means clustering is used as a benchmark. The clustering technique provides the detection of the cells whose serving regions are overlapped. Our results indicate up to %6 increment is achieved in number of the ratio of intra-cluster handover attempts to total handover attempts. Additionally, the results further reduce the standard deviation of the number of cells within a single cluster with a ratio of %8, which yields more uniform distribution of the cells across different clusters.

Full text

Received October 1, 2018, accepted October 14, 2018, date of publication October 22, 2018, date of current version November 30, 2018.
Digital Object Identifier 10.1109/ACCESS.2018.2876601
Mobility-Aware Cell Clustering Mechanism
for Self-Organizing Networks
OMER NARMANLIOGLU 1, (Student Member, IEEE), AND ENGIN ZEYDAN 2, (Member, IEEE)
1P.I. Works, 34912 Istanbul, Turkey
2Türk Telekom Labs, 34889 Istanbul, Turkey
Corresponding author: Omer Narmanlioglu (omer.narmanlioglu@piworks.net)
The work of E. Zeydan was supported by the framework of the Eureka projects PULPA under Project 10725 and TUBITAK TEYDEB
under Project 9150200.
ABSTRACT Self-Organizing Networks (SON) which automate the mobile networks in a cost-efficient
way can provide extensive benefits for mobile network operators that are facing several challenges, such
as providing maximum coverage and balanced/efficient usage of spectrum and energy. However, a major
challenge in future wireless cellular systems is the design of self-organizing architecture that can enable
re-configurable, scalable, flexible, low-cost, and efficient solutions for supporting increasingly diverse
applications, products, and services of user and network requirements. In this paper, we discuss the benefits
and opportunities of mobility-aware clustering capability on different conventional SON functions and
advanced Long-Term Evolution features. We further propose a novel cell clustering methodology, which
utilizes handover attempt data and aims to collect the cells that have higher mobility activity with each other
in a single cluster. One of the main benefits of the clustering mechanism is to reduce the complexity of
network optimization processes via checking smaller number of cells instead of evaluating entire network,
as their number is gradually increasing. We consider real-world data of evolved Node-Bs (eNodeBs) in one
of the major cities while evaluating our novel clustering technique that considers the mobility activities
and location information of eNodeBs. In our evaluations, k-means clustering is used as a benchmark. The
clustering technique provides the detection of the cells whose serving regions are overlapped. Our results
indicate that up to %6 increment is achieved in number of the ratio of intra-cluster handover attempts to total
handover attempts. Additionally, the results further show a reduction in the standard deviation of the number
of cells within a single cluster with a ratio of %8, which yields more uniform distribution of the cells across
different clusters.
INDEX TERMS Self-Organizing Networks, mobility-activity, clustering, Radio Access Networks.
I. INTRODUCTION
International Telecommunication Union (ITU) is expected to
approve Fifth Generation (5G) standards around 2020 and
initial commercial deployments are expected to start in early
2020s. The 3rd Generation Partnership Project (3GPP) is
currently standardizing 5G in Release 15 and will advance
the support for new features in Release 16 [1]. Existing
commercial systems on the other hand, (both 3GPP and
non-3GPP based) heavily rely on static and detailed net-
work planning. However, depending on traffic load and
mobility characteristics, the network infrastructure needs
to be flexible and elastic, i.e., shrink or enlarge depend-
ing on demand as required. Moreover, the timescale of
this scaling should be much shorter than current timescales
of days. As a result of this elasticity demands, tradi-
tional network services (e.g. providing mobility support,
session management) need to be adapted and self-organize
themselves to the current needs without strict and conserva-
tive network planning.
Advanced physical and link layer solutions (includ-
ing orthogonal frequency division multiplexing (OFDM),
multiple-input multiple-output (MIMO), adaptive modula-
tion and coding scheme (MCS) etc.) included by specifica-
tions of Global System for Mobile Communications (GSM),
Universal Mobile Telecommunications Service (UMTS), and
Long Term Evolution (LTE) with the aim of improving
the link efficiency have reached their theoretical limits [2].
Therefore, increasing node deployment density is the only
possible solution to improve the Radio Access Network
(RAN) performance [3]. Heterogeneous networks (HetNets)
that include picocells, femtocells, relay nodes etc. have been
increasingly gaining momentum to meet the requirements of
the data explosion. On the other hand, HetNet developments
have been bringing with both capital expenditure (CapEx)
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O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
and operating expenditure (OpEx) increments due to the
deployment of large number of the nodes, planning of their
locations, their construction and management etc. For today’s
complex networks, various futuristic network solutions are
being considered especially in wireless cellular network
domain [4]. Self-Organizing Network (SON) concept intro-
duced within 3GPP has emerged which can provide advanced
features including self-configuration, self-healing and self-
optimization for next generation cellular networks [5]. SON
has the potential for enabling easier management, configu-
ration, planning and optimization of the cellular networks
while reducing both CapEx and OpEx and providing better
quality-of-experience (QoE) and overall improved network
performance. Network densification with the introduction
of HetNet has brought rapid evolution and integration of
SON functions with the purposes of minimizing human
intervention in the networking processes and automating
mobile infrastructure operation, administration and manage-
ment (OAM). Therefore, SON is considered to be the driving
technology that leads the next stage in the current cellu-
lar network evolution towards 5G. Due to different require-
ments such as energy saving, throughput enhancements,
latency reduction, fair load distributions; resource optimiza-
tion, interference mitigation, mobility management (e.g. for
reducing number of handover), caching, fault detection, cell
sleep management, cell cooperation etc. are required in the
next generation networks. For this reason, clustering, an unsu-
pervised learning approach, that generates isolated group
of cells based on interaction among them is considered to
be a common solution for challenges of 5G networks [6].
It reduces the complexity of network optimization processes
via checking smaller number of cells instead of evaluating
entire network.
Main SON functionality can be divided into three cat-
egories: (i) self-configuration, (ii) self-optimization, and
(iii) self-healing. SON functions in the category of self-
configuration enable automatic parameter adjustments such
as transmit power, electrical antenna angle, handover man-
agement parameters including measurement report triggering
event offsets, thresholds, system updates. Additionally, auto-
mated network integration of new evolved Node-B (eNodeB)
by auto connection and auto configuration, core connectiv-
ity (via S1 interface) and automated neighbor site config-
uration (via X2 interface) can also be considered as some
additional actions inside this category. Self-optimization tar-
gets achieving the main network level goals by optimizing
various aspects such as link quality, coverage and capacity;
auto-tune the network with the help of user equipment (UE)
and eNodeB measurements on local eNodeB level and/or
network management level. Finally, self-healing functions
enable network to recover from network related failures such
as cell outage, sick cell, sleeping cell, and removal of failures
for resiliency purposes. For example, when a cell or eNodeB
goes down during disaster scenarios, self-healing feature
may enable the traffic to use alternative routes for service
continuity.
Together with the expected exponential growth of wireless
devices and data rates of 5G networks, the amount of data
traffic generated inside telecommunication systems is also
expected to increase spectacularly fast. Hence, data ana-
lytic techniques such as machine learning will be promis-
ing solutions to combat performance inefficiency problems.
Some examples can be to reduce unnecessary handovers
and increase energy efficiency of Base Stations (BSs) while
making wireless cellular network more sustainable. There-
fore, the proliferation of new technologies such as machine
learning and advanced cellular techniques (e.g. SON) calls
for innovative ideas that can jointly analyze, optimize and
integrate Mobile Network Operators (MNOs)’ infrastructure
data. As such, it is time to develop innovative solutions that
consider emerging network intelligence, transform this data
surge into knowledge and perform smart self organizing net-
work management.
II. RELATED WORKS AND MAIN CONTRIBUTIONS
SON has recently evolved into a very active research field
as well as a topic of rapid technological process in both
Standard Developing Organizations (SDOs) and telecom-
munication industries [7]–[9]. SON for LTE has been pro-
posed by 3GPP with the aim of interference reduction,
Coverage and Capacity Optimization (CCO), random access
channel (RACH) optimization, Automatic Neighbor Rela-
tion (ANR) optimization, Physical Cell Identity (PCI) opti-
mization, Mobility Load Balancing (MLB), Cell Outage
Detection and Compensation (CODC), Mobility Robustness
Optimizer (MRO), Area Code Optimization (ACO) and
Energy Saving (ES) etc. through optimizing configuration
management (CM) parameters of the cells and/or eNodeB
in the mobile networks (SON functionality and principles
and SON use cases in [10] and [11]). Prior SON research
addresses the problem of self-healing, self-optimization and
self-configuration, i.e. automation of network functionality.
Machine learning approaches, which have evolved
as a sub-field of Artifical Intelligence (AI), have also
caused tremendous advances in many areas including
health, transportation, automotive and telecommunication.
In telecommunication domain, machine learning algorithms
can substitute the traditional configuration based network
operations where analytics on accumulated data can yield
better operational efficiency [12]. Current works on SON are
focusing on automatizing networks with machine learning
techniques such as supervised, unsupervised and reinforce-
ment learning [13]. The success of machine learning enabled
different applications of learning models where learning
process can be done with labeled or unlabeled data-sets
(unsupervised and supervised learning) and trial-and-error
based actions with rewards and punishment (reinforcement
learning). However, developing mobility-aware machine
learning techniques have not received much research focus.
Additionally, not so many telecommunication infrastructure
are utilizing learning models in MNO’s infrastructure espe-
cially for executing SON functionality.
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Relevant literature on unsupervised learning approach for
SON can be classified into different categories including:
operational parameter configuration [14], handover manage-
ment [15], spectrum learning [16], caching [17], [18], cell
outage management [19], [20]. A recent survey of application
of machine learning solution in the context of SON is given
in [21]. Application of clustering techniques on BSs have
shown many benefits in previous works [18], [22]–[28]. Zhao
and Lei [22] have investigated clustering techniques for the
purpose of cooperation between BSs in Coordinated Multi-
point (CoMP) transmission/reception. Zhang et al. [29] are
grouping BSs according to their heterogeneous traffic load
along space and time. He et al. [30] have introduced
clustering-enabled channel modeling where multi-path chan-
nel clustering approach using different clustering algorithms
are investigated. Some of the prominent applications of clus-
tering in the literature are: CoMP applications in cluster-
ing small cells for interference mitigation purposes [22],
increasing offloading or caching gains via clustering mobile
users [18], [23], [24], enhancing energy efficiency or chan-
nel quality in Device-to-Device (D2D) networks via device
clustering [25], [26], increasing system capacity based on
user’s content request and mobility pattern predictions in
Cloud RAN (C-RAN) by clustering remote radio head
(RRH) [27], radio access points clustering for fog computing
applications [28], grouping of cells with similar characteris-
tics (e.g. time) so that same configurations can be applied to
them [31] and so on.
In summary, current approaches towards realizing SON
infrastructure using machine learning concepts are in use
in many different research studies. However, considering
mobility-awareness while exploiting machine learning capa-
bilities in SON has not attracted much research focus in
cellular network design. To the best of our knowledge, none
of the previous works in the literature use the relational
mobility data such as handover attempts to improve the accu-
racy of the cell clustering mechanism. While recent advances
on machine learning have provided increased opportunities
for enabling SON for MNOs, the application of those algo-
rithms on real-world data-sets still remains an open research
area. Although extensive research has been established in
the field of machine learning and SON, practical results
closing the gap between theoretical and practical knowledge
still need to be further investigated. This is where work-
ing on real-world data becomes useful where analytic algo-
rithms and data generated from network infrastructure can
be brought together. In contrast to previous application of
different machine learning algorithms into SON, in this paper
we propose a mobility-aware cell clustering methodology for
cellular network systems. Main of contributions of the paper
can be summarized as follows:
•We first discuss the benefits and opportunities of
clustering capability on different conventional SON
functions including PCI optimization, ANR, ACO,
CODC, ES, and MLB and advanced LTE features
including carrier aggregation (CA), CoMP, distributed
MIMO (D-MIMO), and Single Frequency Network
(SFN) after briefly describing their main functionalities.
•We then propose a novel mobility-aware cell clus-
tering methodology which includes a pre-processing
step to update cell locations based on relational
handover attempts data before performing conven-
tional distance-based clustering. It can decrease
the inter-cluster handover attempts while increasing
intra-cluster handovers attempts, which aims easier and
faster detection of the cell sets in which overlapping
serving regions exist. Hence, network optimization
phase becomes more effective by evaluating a smaller
number of cells (e.g., only intra-cluster or intra and
adjacent clusters) rather than the entire network.
•A real world data-set is used for validation purposes
of appropriately grouping cells that have both outgo-
ing and incoming handover attempts from neighboring
cells. In order to investigate the performance of the
algorithm, we define an indicator which is the ratio of
total intra-cluster handover attempts to overall handover
attempts (including both intra-cluster and inter-cluster).
The performance is later evaluated with conventional
distance-based clustering algorithm, k−Means, which
is utilized as a benchmark. The results reveal that novel
clustering algorithm with pre-processing step outper-
forms the conventional distance-based clustering.
The rest of this paper is organized as follows. In Section III,
we present our system architecture including working prin-
ciples of the clustering algorithms and SON functions &
advanced LTE features. In Section IV, we discuss conven-
tional SON functions & advanced LTE features and the
benefits of our proposed clustering algorithm over them.
In Section V, we explain the proposed algorithm and and we
demonstrate the performance of the mobility-aware cluster-
ing algorithm in Section VI. Finally, we conclude the paper
in Section VII.
Notation: The scalars are represented by regular symbols
and vectors/matrices are denoted by bold face regular letters,
e.g., for a vector of x,xidenotes the i-th element of x.
III. SYSTEM MODEL AND ARCHITECTURE
Self-organizing RAN architecture with learning capabil-
ity including multi-vendor network equipment is depicted
in Fig. 1. It includes several macro and small cells which
have different number of frequency layers in bottom layer
of the figure. Each eNodeB has multiple sectors (generally
3 sectors) and each sector can include different number of
carriers (also called as cells). In addition, there are several
overlapping areas covered by more than one eNodeBs and
their corresponding carriers. Information exchange is done
between network management entity and domain manage-
ment entity, domain management entity and eNodeB and
eNodeB-eNodeB though northbound interface (Itf-N), south-
bound interface (Itf-S) and X2 interface, respectively. The
basic idea behind self organizing framework together with
proposed clustering mechanism of Fig. 1is to help MNOs
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FIGURE 1. Self-organizing RAN architectures with domain management and network management entities.
develop advanced SON functionality within their infrastruc-
ture. In this architecture, the clustering algorithms described
in the following sections and other SON related functions
can be deployed in network management entity. This will
cater vendor-agnostic centralized SON architecture while
exploiting the existing infrastructure data. The centralized
information is used to tune the vendor-dependent CM param-
eters of different LTE features that work under eNodeBs in
distributed manner.
Fig. 2shows the aggregated handover attempts with the
neighbor cells for different types of environments includ-
ing outdoor (residential, highway and rural) and indoor
(shopping mall) locations. The dashed lines in all sub-
figures of Fig. 2represent the neighbor cells with non-
zero incoming handover attempt in Neighbor Relation Table
(NRT)1of the considered example cell which is denoted
by black color. It should be noted that the neighbor cells
that do not have any incoming handover attempts from
the cell under consideration and other near cells are not
displayed. Fig. 2(a) shows an example cell located in
a residential district. This site is in one of the highly
active region of the city. There are several handover
attempts to neighborhood cells in many directions from the
considered cell. However, most of the handover updates were
1NRT includes neighbor cell information such as E-UTRAN Cell Global
Identifier (ECGI), PCI and relational attributes such as handover allowed,
removing option etc.
done towards north-west region (which is the serving region)
of the considered cell’s location. Note that the north-west
direction is matched with azimuth direction of the cell. Fig. 2
(b) illustrates another example eNodeB that serves to outdoor
location serving to highway. Compared to residential site,
this site has less neighbor cells and coverage and service
is more concentrated towards a certain direction, i.e. high-
way. The neighborhood sites with high mobility handover
attempts are observed in western areas, whereas no neigh-
borhood is observed in eastern area of the graph due to cell
antenna positioning towards highway. Fig. 2(c) shows the
neighbor list of the example site located in rural outdoor
region. In rural environment, there are few neighbors and
with less number of handover attempts compared to residen-
tial district. Therefore, only one neighborhood site on the
south-west direction has excessive handover attempts from
the considered site. Finally, Fig. 2(d) gives another example
of a site with omni-directional antennas located in indoor
environment, i.e. in shopping mall. In this figure, the neighbor
list relation is only with two sites that have non-zero incoming
handover attempt, and there are several intra-eNodeB han-
dover attempts compared to inter-eNodeB handovers.
From Fig. 2, we can observe that the behavior of handover
attempts to neighboring sites depends highly on the type
of environment (indoor, outdoor) and cell serving region
(that depends on different factors such as location, azimuth,
antenna angle, transmit power etc.). Mobility-activity extracts
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FIGURE 2. Aggregated Handover Attempts and defined relations of the sectors. (a) Outdoor (Residential) (b) Outdoor (Highway) (c) Outdoor (Rural)
(d) Indoor (Shopping Mall).
the overlapping serving areas and best neighbor relations.
Therefore, grouping those handover attempts is expected to
be beneficial when designing a network planning strategy.
IV. BENEFITS AND OPPORTUNITIES OF
MOBILITY-AWARE CLUSTERING FOR
SON APPLICATIONS
In this section, we discuss about the benefits and opportuni-
ties of applying mobility-aware clustering in different SON
applications. The main functionality of SON mechanisms
discussed in this context are PCI optimization, ANR, ACO,
CODC, ES, and MLB.
A. AUTOMATIC CELL IDENTITY OPTIMIZATION
In LTE, mobility is based on PCI that is used to differentiate
one cell from the others. It also allows UEs to uniquely
identify the source of a received signal. There are total of 504
unique PCIs, which are determined by 3 different primary
synchronization signal (PSS) and 168 different secondary
synchronization signal (SSS). Similar to LTE, 5G New Radio
(NR) extends PCIs to 1008 including 336 different SSS [32].
Motivation: PCIs should be uniquely allocated to the
cells in a local area in order to avoid from PCI conflicts,
which is due to same PCI usage by at least two different
intra-frequency cells2serving in the same coverage area.
Using the same PCI on intra-frequency cells that have
overlapping serving region reduces the probability of suc-
cessful synchronization for UEs that are located at this region.
PCI confusions are also due to a cell that has at least two
intra-frequency outgoing neighbors using the same PCI and
it causes handover failures.
2The cells that are operating at the same carrier frequency
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Benefits & Opportunities: The local areas are determined
based on the overlapping or close coverage areas. The cells
that have overlapping or close serving areas can be found
from relational handover attempts. Then, the intra-frequency
cells serving the same or close areas should not use the
same PCI to decrease conflict cases. Additionally, at least
two intra-frequency cells that have common neighbor with
incoming relation should not use the same identity to avoid
from PCI confusions. The last situation depends on directly
relational information instead of overlapping or close cover-
age areas. Clustering mechanism based on handover attempt
plays a critical role while determining the local areas. The
mechanism extracts the cells that have overlapping or close
serving region or common neighbors that causes handover
failures due to cell identity confusion. After determining the
clusters, the intra-frequency cells within the same cluster
have higher priority than the others while allocating unique
identity codes in order to maximize the network performance.
B. AUTOMATIC NEIGHBOR RELATIONS (ANR)
ANR manages NRTs that include the neighbor cell relations
between LTE-LTE, LTE-UMTS and LTE-GSM. The neigh-
bors are defined regarding to different network handover
strategies in terms of source and neighbor’s operation fre-
quencies, measurement reports that are sent by UE, and cell
planning information.
Motivation: High number of defined neighbor relations
leads inefficient cell identity planning and it can block the
definition of necessary neighbor relations due to the limi-
tations on NRT. On the other hand, less number of defined
neighbor relations causes missing neighbor issues that results
in packet drops due to handover or connection failures.
Benefits & Opportunities: It should be noted that two
or multiple cells that have not any handover attempt with
each other due to not defined relations even if they have
overlapping coverages can be assigned to the same cluster
due to an intermediate cell that has overlapping coverage area
with them. To extract the best real or potential neighbors of
a given site, mobility-aware clustering mechanism that uses
the handover attempts can be used and neighbor definitions
of the cells within same clusters can be prioritized during
neighbor addition process. Hence, neighbors can be defined
based on their high mobility activities. Similarly, neighbor
definitions of the cells within different clusters should be
prioritized during neighbor removal process when they have
less mobility activities.
C. AREA CODE OPTIMIZATION (ACO)
LTE architecture has a multi-tier hierarchical structure to sup-
port mobility activities. Cells are located at the lowest level of
this hierarchy. LTE has Mobility Management Entity (MME)
pool including one or more MMEs and Serving Gateway
(S-GW) pool including one or more S-GWs. Those include
non-overlapping tracking areas. Each tracking area has a
tracking area code (TAC) for identification within a particular
network area and UEs obtain a tracking area identifier (TAI)
list that includes multiple TACs when they attach to an LTE
network.
Motivation: All codes defined above are necessary to track
the locations of mobile terminals. Moving to another area
triggers a change on those codes and the UE is supposed
to send tracking area update message when new TAC is
not included by previous TAI list so that it can generate a
signaling traffic3to core network. ACO aims at reducing this
signaling traffic from RAN to core network and minimizing
mobility activity at the area code borders. There is a trade-off
between number of code reuse and generated core network
signaling traffic in the network. High number of reusing the
same code yields reduced core network signaling traffic. This
is due to assigning the same codes to large tracking areas,
resulting in less UE-level updates even when UE has mobility.
However, this also brings unnecessary paging loads. On the
other hand, less number of reusing the same code reduces the
paging load, but leads to huge core network signaling traffic
and battery consumption at the UE side. Network optimiza-
tion aims to share the existing codes in an appropriate manner
inside the network such that reduction of paging load and core
network signaling can be achieved simultaneously.
Benefits & Opportunities: 3GPP Release 16, which is
the second phase of 5G, will add support for UE power
consumption reduction [1]. Therefore, reducing UE-level
power consumption is one of the envisioned significant
requirements for 5G deployment. During optimization phase,
all codes defined above should be distributed according to
mobility activity of cells with their neighborhood cells. When
cells that have higher mobility activity use the codes within
the same TAI list, even for moving UEs that perform cell
(re)selection mechanism, tracking area update messages are
not required. This benefits to reduction in core network sig-
naling traffic and UE battery consumption, and also keeps
paging load low. Therefore, mobility-aware clustering mech-
anism plays a key role to balance the trade-off between paging
load and core network signaling traffic.
D. CELL OUTAGE DETECTION AND
COMPENSATION (CODC)
Hardware and software failures, external failures includ-
ing power supply and network connectivity, or even
mis-configuration decrease the network performance signif-
icantly. CODC aims to replace manual tasks for detection of
these type problems with autonomous processes including
both auto-detection and auto-compensation. It mitigates the
degradation on coverage, capacity, and service quality due
to unexpected failures in cell or eNodeB level including cell
outage, sick cell, sleeping cell etc.
Motivation: CODC minimizes the outage-induced per-
formance effects via tuning control parameters of the cells
surrounding the affected cells. In order to perform best com-
pensation actions, the serving region of the compensator cells
3UE level updates are also included in definition of core network signaling
traffic.
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should be overlapped with the serving region of the problem-
atic cells for offloading.
Benefits & Opportunities: To reduce the cost of search
mechanism of finding an optimal compensator cell(s), a pre-
defined list can be used. The list includes cells that have high
mobility activity and partially overlapping serving areas that
guarantee successful offloading process via tuning necessary
configuration parameters. In this case, clustering can be used
to generate these lists in which cell and potential compensator
cell(s) are mapped to each other. Hence, process time of find-
ing optimal compensator can be reduced in a failure situation.
E. ENERGY SAVING (ES)
MNOs deploy new sites or add a new carriers into exist-
ing sectors concentrating the capacity of each on a smaller
geographical area. This leads to overlapping coverage areas
served by multiple sites and carriers. Mobile networks are
provisioned and deployed in order to guarantee a specific
quality-of-service (QoS) under the consideration of busy
hours. However, these busy hours last approximately a couple
of hours which the network experiences the highest amount
of traffic over a 24−hour span. During non-busy hours which
are in majority, the network is significantly under-utilized and
power amplifiers and signal processing units still operate even
though there is no user traffic. Therefore, it is possible to
decrease energy consumption in mobile network infrastruc-
ture without any effect on QoS as a consequence of turning
off some parts of the network equipment.
Motivation: ES aims to detect under-utilized carriers
where turning-off those carriers does not cause any QoS
degradation. The cells that will be turned-off should be care-
fully selected to avoid from QoS degradation. This will also
avoid unnecessary inter-frequency handovers increments due
to cell turn offs of different frequencies.
Benefits & Opportunities: To prevent inter-frequency
handovers, local areas (clusters) including cells that have
overlapping or close serving regions can be generated. There-
fore, cells operating at same frequencies are turned off in
each local area. When local areas are generated based on
handover attempt, different frequencies (different number of
frequencies as well) can be selected to be turned off in each
area, since mobility activity is relatively less across cells in
the different local areas.
F. MOBILITY LOAD BALANCING (MLB)
Load imbalance between overlapping inter-frequency cells is
one of the most important problems that MNOs face today
due to different path loss coefficients, bandwidth etc. The
congested cells cause significant deterioration of system per-
formance due to these unbalanced load distributions. MLB
provides traffic load balancing between cells which have cov-
erage overlaps so that congestion problems can be avoided.
This can improve network performance by avoiding from
load imbalance situations and utilizing radio resources more
efficiently. This is achieved by managing cell and relational
parameters that handle unexpected overloading situations due
to dynamic nature of mobile traffic.
Motivation: Due to instant traffic increments or degrada-
tion, MLB has to find the best under-utilized neighbor cell(s)
where the traffic of over-utilized cells can be offloaded to.
Benefits & Opportunities: As in CODC’s case, a pre-
defined list including cells that have high mobility activity
and partially overlapping serving areas (that guarantee
successful offloading process via tuning necessary con-
figuration parameters such as a3offset,a5-Threshold1,
a5-Threshold2,cellIndividualOffset etc. [33]) can be gener-
ated. This can also reduce the cost of search mechanism.
In this case, mobility-aware clustering can be used to gen-
erate these lists in which cells and their potential neigh-
bor cell(s) are mapped to each other. Therefore, when a
cell instantly becomes over-utilized, process time of find-
ing the optimal neighbor cell can be reduced. As a result,
traffic can be offloaded to the neighbor cell(s) in a very
short-time.
In addition to SON algorithms, mobility-aware clustering
mechanism has different benefits and opportunities to several
LTE features that are designed to handle interference issues
and increase network performance in Heterogeneous network
(HetNet) environments. The benefits and opportunities of
applying clustering in LTE features including CA, CoMP,
D-MIMO, and SFN are explained in detail in the following
section.
G. ADVANCED LTE FEATURES
CA has been introduced with 3GPP Release 10 to deal with
1 Gbit/s downlink peak rate under the consideration of the
limited bandwidth of a single chunk of the spectrum. In 3GPP
Release 15 specification, up to sixteen NR carriers can be
bundled together for CA to increase the available bandwidth
to approximately 1 GHz [1]. The main aim is to enhance the
performance of UEs. However, the cells that are used in CA
mechanism for each UE have to be carefully selected. For
instance, serving UE should be located in the overlapping
serving region of the cells in order to get good signal quality
from each of them.
CoMP, another LTE network feature adopting intra-
frequency networking to improve the spectral efficiency,
increases received signal power from serving cell and reduce
the interference level at UE side with the aim of increasing the
throughput of cell edge UEs without decreasing the average
cell throughput. The feature coordinates Physical Downlink
Shared Channel (PDSCH) and Physical Downlink Control
Channel (PDCCH) of multiple cells that have overlapping
serving region. CoMP mechanism has multiple modes that
have different benefits according to network conditions.
Dynamic Point Selection (DPS) and Joint Transmission (JT)
are the two of them. In DPS, PDCCH and PDSCH data of
the UE are sent by serving cell to the coordinating cell, then
forwarded to UE by coordinating cell. DPS increases the
capacity of hot-spot cells when the network load is heavy
and imbalanced. To get the full benefit from DPS mecha-
nism, source cell should be heavy loaded and coordinating
cell should be selected among under-utilized cells. In JT
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O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
where network load should be light, coordinating cell first
receives PDSCH data from serving cell. Cell edge UEs then
receive PDCCH data from only serving cell and PDSCH
data from both serving and coordinating cells simultaneously.
Therefore, selection of cells and CoMP mode play a critical
role for performance of CoMP mechanism.
D-MIMO using multiple antennas for data transmission
in a coordinated way is benefiting from increasing spatial
channel resolution as a result of different spatial locations of
antennas. Similar to CoMP mechanism, D-MIMO improves
especially the cell edge UE throughput as a consequence of
high antenna array gains and interference suppression gains.
However, it is useful for UEs that are located overlapping
coverage areas of the cells.
SFN includes two or more RRUs and allows multiple
physical cells to be combined in a one logical cell. SFN aims
to mitigate inter-cell interference issues and reduce number
of neighboring cells. Using joint scheduling, received signals
from different cells that are interfering with each other in
distributed schema becomes multi-path signals arriving from
a single centralized node. This increases cell edge throughput
in a SFN cell. To get the full benefit from SFN mechanism,
the cells within an SFN should be determined under the
consideration of overlapping serving regions of the cells as
in CoMP and D-MIMO features.
Benefits & Opportunities: To make decision mecha-
nism easier and faster, pre-defined separate CA, CoMP,
D-MIMO, and SFN sets can be determined with respect to
handover attempts between cells and relation type.4Overlap-
ping regions across the multiple cells that are used in LTE fea-
tures are searched through within each cluster independently.
For instance, in CA, together with many sites and the ability to
aggregate up to sixteen NR component carriers (each having
a serving cell) for 5G node and UEs, the selection process of
component carriers and sites can be huge. Thanks to appropri-
ate mobility based clustering between cells, this search space
for finding the appropriate cells for CA can be reduced only
within each cluster with performance enhancements. Another
example of application of mobility-aware clustering is in the
process of selection of the most feasible cells in a SFN cell.
During this process, it is better to use cluster of cells that
has high intra-cluster handover attempts which is triggered by
coverage. This is due to increased overlapping areas between
the physical cells that become part of SFN cell center which
ensures efficient joint scheduling inside.
V. MOBILITY-AWARE CLUSTERING
ALGORITHM FOR SON
In this section, we first describe the conventional distance-
based k−Means clustering mechanism and later our pro-
posed location update algorithm using relational mobility
data which are basically handover attempts among cells. The
algorithm is used as pre-processing step before k−Means to
4For instance, cell list for CA includes inter-frequency relations in
the intra or inter-band. However, CoMP’s list should strictly include
intra-frequency relations.
generate clusters with cells having higher mobility-activity
rather than cells with closer distances.
A. DISTANCE-BASED CLUSTERING
The goal of k−Means algorithm is to find an assignment
of given Npoints, denoted by xn, to Kdifferent clusters5
using Ddimensional ukcenters (for k∈ {1,2. . . K}). Here,
n∈ {1,2. . . N}and each xn=[x1,nx2,n. . . xD,n]Thas
dimension D(or number of attributes). The aim is to minimize
the sum of the squares of the distances of each data point
to the closest center. The analytic model, which aims to find
optimum uk, can be defined in terms of cost function, Jas
J=
N
X
n=1
K
X
k=1
rn,kkxn−ukk2,(1)
where rn,kis defined as
rn,k=


1 if k=argmin
j
xn−uj

2
0 otherwise .
(2)
In order the minimize J, we first take derivative with respect
to ukand set it to zero, which gives
uk=
N
P
n=1
rn,kxn
N
P
n=1
rn,k
.(3)
Under the consideration of (3), calculation of rn,kgiven in (2)
is repeated until ukgiven in (3) is not changed. For the rest of
paper, we assume two-dimensional points (D=2) where the
first dimension denotes the latitude of the cell and the second
dimension denotes the longitude of the cell. The cells under
the same sector6are further aggregated and the cell set is
referred to sector in the following sections.
B. LOCATION UPDATE BASED ON MOBILITY ACTIVITY
The handover attempts between sectors (or aggregated cells)
and their neighbor sectors give an idea about their location.
The approximated location can be extracted from weighted
average of its neighbors. The weights are the ratio of individ-
ual handover attempt between source and destination sectors
to the total handover attempt associated with source sector.
The analytic formula can be written as
¯
xj=
N
P
i=1
¯
xi8j→i+8i→j
N
P
i=18j→i+8i→j
,(4)
where ¯
xjis updated location of the source sector and rest
of the sectors including neighbors and non-neighbors for
5The Elbow method which checks the percentage of variance explained
as a function of the number of cluster is used to determine Kvalue
6Sector is the group of cells that locate at the same site, operate at different
carrier frequencies, and serve in the same direction.
65412 VOLUME 6, 2018

O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
j-th sector and 8i→j7denotes the number of handover attempt
from ito j.
In order to consider the original locations while updating
the locations, we add another term (original locations denoted
by x) to (4) by introducing a weighted term, denoted by α,
between expected locations based on handover attempts and
real locations. The mathematical expression can be written as
¯
xj=α
N
P
i=1
¯
xi8j→i+8i→j
N
P
i=18j→i+8i→j
+(1−α)xj.(5)
After locations are updated for all sectors, differences
between current and previous locations are calculated. The
total difference at each iteration is denoted by 1. The process
is repeated until 1values are less than a pre-defined thresh-
old, 1t. A summary of the flow is given in Algorithm 1.8
Algorithm 1 Location-Update Algorithm-I
Input:1t,x,α,8i→jfor i,j∈ {1,2. . . N}
Output:¯
x
1¯
x=x
2while 1>1tdo
3¯
xp=¯
x
4for each xjj∈ {1,2. . . N}do
5¯
xj=α
N
P
i=1
¯
xi(8j→i+8i→j)
N
P
i=1
(8j→i+8i→j)
+(1 −α)xj
6end
71=
N
P
j=1

¯
xp
j−¯
xj


8end
9return ¯
x
C. CLUSTERING USING UPDATED LOCATIONS
k−Means clustering algorithm is run over the updated
locations, ¯
x, so that we change xnvariables in (1), (2), and (3)
with ¯
x. Hence, the resulting equations are as follows:
J=
N
X
n=1
K
X
k=1
rn,kk¯
xn−ukk2,(6)
rn,k=(1 if k=argmin
j
¯
xn−uj

2,
0 otherwise
(7)
uk=
N
P
n=1
rn,k¯
xn
N
P
n=1
rn,k
.(8)
7Based on i→jnotation, it is the incoming handover attempt value from
sector−ito sector−jand, similarly, it is the outgoing handover attempt value
from sector−jto sector−i.
8Note that starting point of j∈ {1,2. . . N}(refer to Line 4 in Algorithm 1)
changes the output results. In the numerical results section, starting and
following values are taken from a random sequence including 1,2. . . N
values at each iteration starting with Line 2.
Similar to conventional k−Means algorithm, (7) is repeated
until ukgiven in (8) is not changed.
D. COMPUTATION COMPLEXITY AND SIGNALLING LOAD
Note that handover attempt and site plan (e.g., latitude, longi-
tude, sector information etc.) data that are used by proposed
algorithm in location update phase are already available in
network management entity and utilized as input in most
of the centralized SON function. Therefore, collecting this
information from existing infrastructure yields no additional
traffic on network for clustering purpose. The analysis can
be performed offline and appropriate decisions can be fed
back to operational units for maximization of network effi-
ciency. In terms of computational complexity, location update
Algorithm 1’s overall complexity is O(N∗I) where Iis
the number of iterations. It can be observed that the main
computation complexity lies in the performance evaluation
of (5) for Ntimes in order to compute the updated loca-
tions as per line 5 of Algorithm 1. The time complexity of
Lloyds algorithm (as well as most variants) for k−Means
clustering is on the order of O(K∗N∗I∗D) [34]. Note
that the time complexity is also sensitive to initial conditions.
Therefore, the overall complexity is low and can be further
reduced through trade-off analysis between complexity and
convergence.
VI. NUMERICAL RESULTS
In this section, we describe our used mobility data-set and
present performance improvement using mobility data which
is handover attempt between cells. k−Means clustering
algorithm which is conventional distance-based clustering
without location updates is used as benchmark. To evalu-
ate the validation of the proposed mobility-aware cluster-
ing approach, we use a large-scale data-set containing one
week of mobility activity for one region. Weekly data is
collected from total number of 5732 LTE cells. The partic-
ular RAN under consideration includes 3 different frequency
layers with the different number of cells. Numerically, first
layer includes 2682 cells operating at lower band, second
layer includes 2365 cells, and third layer includes 685 cells.
There are totally 2848 unique sectors and 363 sectors have
omni-directional antenna with relatively less transmit power
than sectorial antennas.
The histogram and probability density estimation9of
aggregated handover attempts (natural logarithm-based) are
depicted in Fig. 3where natural logarithm of most of the
number of handover attempts are between 3.96 and 4.39
with frequency around 10,800 where the highest estimated
probability density is 0.18. Fig. 3shows a Gaussian-like
distribution with long right tail and the statistical information
our handover attempt data-set (natural logarithm based) is
depicted in Table 1.
We consider 20 clusters that give %95 variance explained
and 50 clusters that gives %95 variance explained (see Fig. 4).
9Kernel smoothing function is used to estimate probability density.
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O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
TABLE 1. Statistical information of handover attempt data-set.
FIGURE 3. Histogram and probability density estimation of collected
natural logarithm based handover attempt.
FIGURE 4. Number of clusters v.s. percentage of variance explained to
decide on number of clusters.
For 20 cluster, original and updated locations with respect to
different αvalues are depicted in Fig. 5where the dimensions
of location matrix is reduced to one using Principal Com-
ponent Analysis (PCA) for easy demonstration. The results
show that increment on αvalue make the sectors with high
handover attempt closer. This is due to prioritization of the
first term in (5) that denotes the locations extracted from
weighted average of neighbor cells’ locations.
In Figs. 6and 7, location shifts with respect to the previous
iterations and original values are depicted for different α
values under the consideration of 1t=10−10. When we
FIGURE 5. Cluster assignments with respect to (a) original locations of
the sectors and (b) new locations of the sectors with α=0.95.
increase the αvalue, shifts in each iteration becomes larger
(see Fig. 6). This is because of prioritized locations extracted
from weighted average of neighbor cells’ locations which
yields large shift with respect to original locations (see Fig. 7).
However, updating process duration becomes larger as well.
In Fig. 8, we present the ratio of number of intra-cluster
handover attempts (which is the summation of the handover
attempts between the sectors in the same cluster) to total
number of handover attempts including both intra-cluster and
inter-cluster (which is the summation of the total number of
handover attempts between the sectors) in different clusters
under the consideration of 20 number of clusters. α=0 is
the conventional distance-based clustering without location
updates (which is k−Means clustering) and it achieves a ratio
of 91.53% with the standard deviation of 0.71%. This value
is increased to 92.70% with αof 0.50 and increments on α
up to 0.95 yields a ratio of 93.13%. The results show that
increasing αachieves better intra-cluster to total handover
attempts ratio. This behavior is the result of collecting sectors
65414 VOLUME 6, 2018

O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
FIGURE 6. Amount of location shifts with respect to the location in
previous iteration.
FIGURE 7. Amount of location shifts with respect to the original location.
that have higher mobility interaction inside the same cluster.
Higher αvalues gives more priority to the weighted average
of neighbor cells’ locations that make the sectors including
higher interaction closer. Then, running k−Means algo-
rithm over the sectors with updated locations increases the
intra-cluster handover attempts significantly. Moreover, since
extending cluster size also leads to higher ratio between intra
and inter cluster handovers, the standard deviation of the total
number of sectors included in clusters is also investigated.
Conventional case achieves a standard deviation of 185.9 and
it is decreased down to 176.2 with the use of α=0.95.
The results reveal that proposed novel algorithm makes the
distribution of sectors across different clusters more uniform.
It can be concluded that location update process based on
handover attempts and neighbor cells’ locations increases the
uniformity of the cells over coverage areas. Hence, k−Means
clustering over updated locations provides more uniform sec-
tor distribution.
FIGURE 8. Ratio of intra-cluster handover attempts to total handover
attempts and standard deviation of the number of sectors within each
cluster after location updates and clustering with 20 clusters.
FIGURE 9. Ratio of number of intra-cluster handover attempts to total
number handover attempts and standard deviation of the number of
sectors within each cluster after location updates and clustering with
50 clusters.
In Fig. 9, we present the same ratio under the consideration
of 50 clusters which achieve %99 variance explained. α=0,
conventional distance-based clustering, achieves the ratio of
82.41% with the standard deviation of 0.60%. This value
is increased to 9531 with the standard deviation of 87.51%
using αof 0.5. The ratio becomes 87.66%, 87.75%, 87.95%,
88.17%, 88.21%, 88.27%, 88.30%, and 88.33% with the use
of α=0.50, α=0.55, α=0.60, α=0.65, α=
0.70, α=0.75, α=0.80, α=0.85, and α=0.90,
respectively. Increment of αup to 0.95 further yields the
ratio of 88.40%. The results show that increasing αachieves
better ratio of intra-cluster handover attempt to total handover
attempt. When we check the standard deviation of the total
number of sectors includes by clusters, conventional case
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O. Narmanlioglu, E. Zeydan: Mobility-Aware Cell Clustering Mechanism for SONs
achieves the standard deviation of 68.57 and it is decreased
up to 62.83 with the use of α=0.95. The deviations are grad-
ually decreased which leads more homogeneous distribution
while increasing αvalues. Numerically, for αvalues of 0.50,
0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, and 0.90, standard
deviations become 66.71, 66.15, 65.58, 65.57, 65.09, 65.08,
64.38, 64.22, 63.71, and 63.59, respectively. As in previous
case with 20 clusters, proposed location update algorithm
increases the intra-cluster handover attempt as a result of
collecting sectors that have higher mobility interaction inside
the same cluster. Additionally, based on decreasing standard
deviation values of cluster sizes as we increase αvalue,
uniformity is increased among number of sectors included
by clusters. The results reveal that an increment with the
amount of up to 6% in number of the ratio of inter-cluster
handover attempts to total handover attempts and a decrement
on standard deviation of the number of sectors within the
clusters with the ratio of %8.
FIGURE 10. Average distance values between the sectors within the same
clusters with 20 clusters.
FIGURE 11. Average distance values between the sectors within the same
clusters with 50 clusters.
In Figs. 10 and 11, average distance values between the
sectors within the same clusters are depicted for 20 and 50
clusters, respectively. The conventional distance-based case
achieves the average values of 9.861 km for 20 clusters and
3.898 km for 50 clusters. Locations are updated based on
calculating weighted average of neighbor cells’ locations and
making sectors with more mobility interactions closer to each
other. This causes higher distance between cells since mobil-
ity activity is included as pre-processing before k−Means
algorithm. Numerically, for αvalues of 0.50, 0.55, 0.60, 0.65,
0.70, 0.75, 0.80, 0.85, and 0.90, average distances become
4.411, 4.434, 4.503, 4.554, 4.625, 4.661, 4.690, 4.723, 4.762,
and 4.873, respectively, when 50 clusters are considered. For
the case of 20 clusters, 9.861 km average distance obtained
in conventional distance-based clustering is increased up to
10.004 km with the use of pre-processing step. However,
there is no concern about the average distance increment due
to the fact that the aim of the clustering is to find the cells that
have higher mobility activities rather than closeness.
VII. CONCLUSION
This paper is studying a novel mobility-aware cell cluster-
ing technique and its benefits and opportunities for con-
ventional SON functions including PCI optimization, ANR,
ACO, CODC, ES, and MLB and further advanced LTE fea-
tures such as CA, CoMP, D-MIMO, and SFN. We used
the mobility characteristics of real-world data of eNodeBs
located in different regions of a selected city. After describing
our particular RAN under consideration, we demonstrate that
the utilized method can increase the ratio of the intra-cluster
attempts to overall handover attempts. The method yields
better performance in terms of collecting the nodes that have
mobility-activity with each other within the same cluster as
compared to traditional network planning methods which use
only distance based metrics. Our new mobility-aware method
shows an increment with an amount of up to 6% in number of
the ratio of inter-cluster handover attempts to total handover
attempts. It further yields a decrement on standard deviation
of the number of sectors within the clusters with a ratio of %8.
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OMER NARMANLIOGLU received the B.Sc.
degree from the Department of Electrical and Elec-
tronics Engineering, Bilkent University, Ankara,
Turkey, in 2014, and the M.Sc. degree from
Ozyegin University, Istanbul, Turkey, in 2016,
where he is currently pursuing the Ph.D. degree.
He is also with P.I. Works as an RF & SON Con-
sultant. His research interests are the physical and
link layer aspects of communication systems and
software-defined networking paradigm for radio
access, transmission, and packet core networks. His major distinctions
include the Best Research Assistant Award at Ozyegin University in 2016,
the National Instruments Engineering Impact Award in Austin, TX, USA,
in 2017, and the Best Paper Award at the IEEE International Conference on
the Network of the Future, London, U.K., in 2017.
ENGIN ZEYDAN received the B.Sc. and M.Sc.
degrees from the Department of Electrical and
Electronics Engineering, Middle East Technical
University, Ankara, Turkey, in 2004 and 2006,
respectively, and the Ph.D. degree from the Depart-
ment of Electrical and Computer Engineering,
Stevens Institute of Technology, Hoboken, NJ,
USA, in 2011. He was an R&D Engineer with
Avea, a mobile operator in Turkey, from 2011 to
2016. He has been a part-time Instructor with the
Electrical and Electronics Engineering Department, Ozyegin University,
since 2015. He is currently a Senior R&D Engineer with Türk Telekom Labs.
His research interests include telecommunications and big data networking.
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