MODELING, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF MOBILITY LOAD BALANCING FOR LTE DOWNLINK DATA TRANSMISSION

International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.5, September 2016
DOI: 10.5121/ijcnc.2016.8505 53
MODELING, IMPLEMENTATION AND
PERFORMANCE ANALYSIS OF MOBILITY LOAD
BALANCING FOR LTE DOWNLINK DATA
TRANSMISSION
Mohamed Escheikh1
Hana Jouini1
and Kamel Barkaoui2
1
University of Tunis El Manar, Enit, Sys’Com Box 37, LE BELVEDERE 1002 Tunis,
Tunisia
2
Conservatoire national des arts et métiers Box 2, conté 75003
Paris, France
ABSTRACT
We propose in this paper a simulation implementation of Self-Organizing Networks (SON) optimization
related to mobility load balancing (MLB) for LTE systems using ns-3 [1]. The implementation is achieved
toward two MLB algorithms dynamically adjusting handover (HO) parameters based on the Reference
Signal Received Power (RSRP) measurements. Such adjustments are done with respect to loads of both an
overloaded cell and its cells’ neighbours having enough available resources enabling to achieve load
balancing. Numerical investigations through selected key performance indicators (KPIs) of the proposed
MLB algorithms when compared with another HO algorithm (already implemented in ns-3) based on A3
event [2] highlight the significant MLB gains provided in terms global network throughput, packet loss rate
and the number of successful HO without incurring significant overhead.
KEYWORDS
LTE networks, Mobility load balancing, Handover, Simulation
1. INTRODUCTION
In recent years the demand for traffic in cellular radio networks has evolved vertiginously. In
order to cope with this demand, the organization of international standardization 3rd Generation
Partnership Project (3GPP) had introduced the new cellular radio system Long Term Evolution
(LTE). This latter adopts a simplified all-IP architecture providing spectral efficiency about two
to three times higher than that of the 3GPP Release 6 [3]. LTE will also offer up to 100 Mb/s of
throughput on the downlink (DL) with a spectral bandwidth of up to 20 MHz. LTE systems use
multiplexing and encoding data technique namely Orthogonal Frequency Division Multiple
Access (OFDMA) in the radio interface downlink (DL) transmission and the Single Carrier
Frequency Division Multiplexing Access (SC-FDMA) in the uplink (UL) transmission.
For LTE networks, the main challenges are to meet users' quality of service (QoS) requirements
especially for real-time traffic in terms of throughput, end to end delay. These challenges concern
also the satisfaction of cellular radio operator’s requirements in terms of radio resource
management (RRM), rationalization of operational expenses and optimization of the overall

54
network efficiency. To address these challenges, SON technologies have been introduced in LTE
networks from the 3GPP Release 9 specifications [4]. These technologies are designed to achieve
a high level of operational performance by automating a number of tasks such as configuration,
optimization and healing (repair). This enables also to reduce capital expenditure (CAPEX) and
operational expenditure (OPEX) to deal with the inter-operator competition.
We focus in this article on a key component of SON technologies namely self-optimization and
especially load balancing in mobile radio LTE networks. In the literature several research studies
have been proposed for investigating the load balancing problem between LTE cells and can be
classified in two categories:
• In the first category an overloaded cell (hot-spot cell) trying to borrow resources (radio
channels) to the least loaded neighbouring cells [3]. Yao Tien Wang et al. proposed in [5]
a method that fits into this category and is based on neural networks and fuzzy logic. The
proposed method performs a number of capabilities related to learning, optimization,
robustness and fault tolerance. This method is used in order to meet effectively the
stringent requirements for multimedia traffic in terms of QoS.
• In the second category the overloaded cell tries to transfer the traffic excess to the least
loaded neighbouring cells by dynamically adjusting the handover parameters (hysteresis,
time to trigger (TTT) ...) or using the cell breathing technique [6]. The principle of such
technique is to gradually shrink the cell coverage as the load increases. In the literature,
several research studies have addressed this issue. In [7, 8], new power control algorithms
have been proposed in order to dynamically adjust the scope of both overloaded and
under-loaded cells. The authors in [9] proposed an algorithm to jointly improve HO
performance and load balancing (LB) by introducing a co-weighted satisfaction factor. A
typical transfer approach to implement load balancing is presented in [10]. Such approach
chooses for this purpose as a source cell, the cell that has the highest utilization ratio and
the target cell the cell having the lowest utilization ratio of physical resource blocks
(PRB). In [11], cell offset (shift) is automatically adjusted according to the load of the
source cell and that of the target neighbouring cell. In [12] the authors propose a method
of estimating the load after handover completion. This method is based on the SINR
prediction and the measure of the user signal quality.
In [13] authors implemented two elementary procedures (EPs) related to load management (LM)
function of the X2-application protocol (X2AP) as specified in TS 136.423 [14].in order to
implement a MLB based adaptive handover (HO) algorithm.
The remainder of this paper is organized as follows: in section 2 we will briefly recall
architectures and SON features in LTE networks. We detail in this same section the load
balancing principle by dynamic HO adjustment. In Section 3 we describe the proposed MLB
algorithms. Section 4 is devoted to simulation results and numerical investigation. The last
section concludes thus paper.

55
2. SON ARCHITECTURES AND FUNCTIONALITIES FOR LTE
NETWORKS
2.1. SON Architectures
There are three types of SON architectures in cellular radio networks [15] trying, each one, to
find a compromise between stability, scalability and agility. Before detailing the principle these
architecture,s we propose to recall the definitions for stability, scalability and agility [16]:
• Scalability: Scalability of the solution is its ability to reduce complexity and to maintain
its functionality to provide good performance even for large scale systems.
• Stability: a solution is considered stable if it avoids oscillations (e.g. instability means the
occurrence of ping-pong phenomenon when performing handovers in cellular radio
networks).
• Agility: is also a key feature of SON systems reflecting the system adaptability in its
operational environment changes. Thus an agile algorithm should not react too quickly to
temporary changes in the system to prevent large fluctuations between its states.
2.1.1. Centralized SON (C-SON) Architecture
In the C-SON architecture (Figure 1.a) algorithms are executed at the network management
system (NMS) level. The main advantage of this approach is that the SON algorithms can gather
information from all entities of the network into consideration. This means that it is possible to
jointly optimize the parameters of all centralized SON functions. The purpose behind is to
provide an overall optimization enabling further stability. Stability is especially useful for
networks having characteristics which vary relatively slowly. The C-SON architecture also
enables to facilitate coordination between the SON functions. In contrast, C-SON disadvantages
are particularly manifested by slow response times, high backbone traffic and a singular point of
failure. Notice also that the long duration of response time and the lack of agility can significantly
affect the network adaptation rate and cause instability problems.
2.1.2. Distributed SON (D-SON) Architecture
Algorithms in D-SON architectures (Figure 1.b) are executed in the network nodes. Thus the
exchange of SON messages can be directly achieved between ENodeBs LTE. Unlike the C-SON
architecture, D-SON architecture provides more dynamic for SON features and adapts more
quickly to changes in the network (agility). Another advantage of the D-SON solution is its
scalability. However, optimizations performed in cells do not necessarily lead to global
optimization which may cause undesirable instabilities.
2.1.3. Hybrid SON (H-SON) Architecture
The H-SON architecture (Figure 1.c) executes SON algorithms both in the NMS at the level of
network elements (NE). This type of architecture tries to take advantage of the previous two
architectures while circumventing disadvantages, which is not always easy to achieve.

56
Figure. 1: SON Architectures for LTE networks
2.2. SON Features
SON Features in LTE networks include self-configuration, self-diagnosis, self-healing and self-
optimizing. The SON technologies are designed automatically to adjust network settings. This is
achieved based on measurement reports from UEs and eNodeBs in order to ensure better network
service quality and to perform better services for UEs.
2.2.1. SON Auto-optimization
Self-optimization is a collection of algorithms aiming to maintain network quality and network
performance with minimal human intervention. The self-optimization functions automatically and
dynamically trigger, if necessary, optimization actions on the affected NEs. Among the most
important functions of self-optimization we distinguished MLB optimization, mobility robustness
optimization (MRO). MRO is used in conjunction with MLB to ensure more stability and to
mitigate the ping pong phenomenon. Adjusting the settings of MLB and the MRO may be subject
to conflicting objectives often harmonized through finding convenient compromise. In this article
we focus on MLB.

57
2.3. MLB In LTE Networks Based On Dynamic Adjustment Of HO Parameters
Before detailing the principle of MLB by dynamically adjusting HO parameters, we present the
inter-cellular transfer (HO) principle in LTE networks.
2.3.1. Handover in LTE Networks
The handover is a major key procedure of mobile cellular radio networks to ensure that users are
moving freely across the network while maintaining continuous connectivity and access to
services [17]. Given that the handover success rate is a key indicator of network performance, it is
essential to achieve a quick and efficient HO process. The optimization of the handover aims to
dynamically adjust its setting parameters (offset, hysteresis ...). In LTE, several triggering events
may be considered [18]. In this article, we focused on the A3 event. This latter requires that the
difference between the received signal level of the current cell and that of the neighbouring cell
exceeds a given threshold.
2.3.2. MLB In LTE Networks
In LTE networks, the traffic demand of some cells may be much higher than the acceptable level,
while other neighbouring cells (of the overloaded cells) may have enough available resources to
serve more users. This situation causes, in absence of cooperation, load unbalance between cells.
In order to trigger load balancing between two cells (an overloaded source cell A (hot-spot) and
an under-loaded neighbour destination cell B) (Figure 2), two conditions must be satisfied:
• The source cell A load exceeds the predefined threshold to trigger MLB.
• The neighbouring cell B have enough available resources to accept cooperating and
handle the cell A traffic excess.
Once the above conditions are met, the cell A chooses, in a first step, a selected number of
attached users candidate to achieve HO to cell B. In a next step users adjust their own parameters
(handover, reselection, etc.) corresponding to the cell B. In the next section, we describe the
principle of load balancing algorithms in mobile networks (MLB) we proposed and implemented
in ns-3.
3. PROPOSED MLB ALGORITHMS
The two proposed MLB algorithms (Alg_MLB1 and Alg_MLB2) are based on dynamic adaptation
of HO parameters (hysteresis values) (Figure 2). The HO due to MLB is the transition of the
current overloaded cell (cell A) to one or more neighbouring cell(s) willing to cooperate. The
performance of these two algorithms will be compared to a standard HO algorithm without MLB
(Alg_without_MLB). Note that the above algorithms (with and without MLB) are based on
measuring the RSRP and use the A3 event of the 3GPP specification [2] for triggering the HO.
The origin of the HO in LTE networks implementing the MLB can be of two different types. The
first type is the realization of the A3 event, while the second refers to a load mismatch between
two neighbouring cells. Before detailing the principle of the two MLB algorithms, we will in
what follows set some notations and preliminary thresholds that will be commonly used in the
description of both algorithms Alg_MLB1 and Alg_MLB2.

58
Fig. 2: MLB with dynamic HO adjustment
Preliminary Notations:
• VAR(i): (Available Resources) The available amount of resources in ith
cell.
• VTR(i) (Total Resources): The total amount of resources in ith
cell. Notice for conventional
notation that i = 0 indicates the current cell and i > 0 indicates the ith
neighbouring cell.
• VAR(i)/VTR(i): The relative amount (in %) of available resources in cell i.
• ThPreMLB: The predefined threshold for triggering MLB (Figure 3).
• ThpostMLB: The threshold for disabling MLB.
• ThAvailMLB: The threshold to accept the MLB request.
The current cell is assumed overloaded if the following condition is satisfied:
Th
V
V
eLB
TR
AR
Pr
)0(
)0(
<
(condition 1)
If condition 1 is verified, the MLB procedure is triggered. The overloaded cell dynamically
configures the new HO hysteresis thresholds for different neighbouring cells with respect to their
relative amount of available resources. These new (updated) thresholds are calculated from the
following equation [19]:
( )10,. )0(),0( ≤≤= ii HysHys ThiTh αα (1)

59
with
( )
( )
( )
( )
( )
( )









<
≤≤
>
=
AvailMLB
TR
AR
eMLB
TR
AR
AvailMLBi
PostMLB
TR
AR
i
Th
iV
iV
if
Th
iV
iV
Thif
Th
iV
iV
if
1
0
Prβα (2)
and
( )
( )







−
−
−=
2lg_5,0
1lg_1
MLBAfor
MLBAfor
ThTh
iV
iV
Th
PostMLBAvailMLB
TR
AR
AvailMLB
iβ
Notice that ThHys(0) is the HO hysteresis threshold of the current cell before triggering MLB and
ThHys(0, i) the HO hysteresis threshold of the current cell (with index 0) toward the neighbouring
cell with index i.
Fig. 3: Triggering condition of the MLB algorithms in the current cell
As soon as MLB condition is satisfied (i.e. condition 1), the current cell updates HO hysteresis
threshold ThHys(0,i) and sends then the active UEs (attached to current cell) via a control message
measurement. In turn, the UEs update the new hysteresis thresholds as long as the condition on
the A3 event is checked. Moreover, once the condition 2 is verified, the MLB is disabled.
Th
V
V
PostLB
TR
AR
>
)0(
)0( (condition 2)
From the above description, we notice that the MLB is effective unless the two conditions
(condition 1 for the current cell and condition 3 for the neighbouring cell) are verified:
Th
V
V
AvailLB
TR
AR
i
i
>
)(
)( (condition 3)
Thus, one can distinguish an alternation between periods with and without MLB depending on
load variations of both the current cell and the neighbouring cell(s). Note that as soon as the
deactivation condition of the MLB process (condition 2) holds, the current cell needs to recover
the original hysteresis value by sending another measurement control message to the UEs. Figure
4 depicts the principle of the proposed MLB algorithms.
(3)

60
Fig. 4: Principle of the proposed MLB algorithms implemented in ns-3
4. SIMULATION ANALYSIS AND NUMERICAL RESULTS
To highlight the performance of the proposed MLB algorithms (i.e. Alg_MLB1 and Alg_MLB2),
we compare their performances with those of a standard algorithm without MLB
(Alg_without_MLB) for different UEs densities (i.e. for different global loads). Alg_without_MLB
only implements the A3 event of the 3GPP specification and without accounting of load
balancing. Such algorithm is used in our case study as a standard algorithm. Alg_MLB1 and
Alg_MLB2, associated with the A3 event, aim to ensure better distribution of the overall LTE
network load and to reduce packet losses.
4.1. Simulation Model
We design a simulation model composed of three equi-spaced eNodeBs with distance of 500m.
Each eNodeB includes three sectors. MLB algorithms are implemented in a distributed manner
(D-SON) in each sector. These algorithms dynamically adjust the HO parameters (hysteresis and
time to trigger (TTT)) as a function of the network load. TTT is a timer used in the 3GPP
specification to provide greater robustness and to better mitigate the ping-pong phenomenon. In
the investigated scenarios, we have focused on the dynamic adjustment of the hysteresis in the
range between 0 db and 3 db in steps of 0.5 dB for fixed values of TTT. Table 1 summarizes the
correspondence between the modulation and coding schemes (MCS) (used in our simulation
model) and the total capacity of a cell in Mbps for a bandwidth of 5MHz (25RBs).

61
Table 1: Correspondence between MCS and the total cell capacity in Mbps for bandwidth of 5Mhz (25RBs)
MCS Modulation Total cell capacity (in Mbps)
[0..9] QPSK 13.2
[10..16] 16QAM 26.4
[17..28] 64QAM 39.6
Table 2 recapitulates the main simulation parameters used in our case study.
TABLE 2: Simulation parameters
Parameter Value
Simulation duration 100 s
Number of eNodeBs 3 (9 sectors)
Distance between eNodeBs 500 m
Power transmitted by a eNodeB in
DL
46 dBm
Traffic nature TCP
UEs density (37/56/75 UEs) 2/6/8 * E-05
Minimal UE moving speed (60
Km/h)
16.6667 m/s
Maximal UE moving speed (60
Km/h)
16.6667 m/s
Bandwidth in UL and DL 5 MHz (25 RBs)
Time To Trigger 256 ms
Default hysteresis value 3 dB
Hysteresis margin with MLB [0..3 dB]
ThPreMLB 0.2
ThAvailMLB 0.3
ThPostMLB 0.4
In the next section, we present numerical results related to simulation investigations of the
proposed scenario.
4.2. Numerical Results
Figure 5 shows that the global network throughput provided by Alg_MLB1 and Alg_MLB2 is
greater than that provided by Alg_without_MLB for different UEs densities (37, 56, 75 UEs).
This may be justified by the MLB principle which favours the transfer of a part of the exceeded
load from the congested cell to the under-loaded neighbouring cells. Thus, the risk of traffic loss
in overloaded cell will be reduced, and possibly avoided by evenly spreading this load excess
toward under-loaded neighbouring cells. Another interesting observation can be done from Figure
5 is that the MLB certainly improves the global network throughput for different loads but this
improvement is not very significant for high load. The explanation of such behaviour is that for
such load it’s very likely that neighbouring cells (of an overloaded cell requesting their
cooperation for possible load balancing) are themselves overloaded. This intuitively corresponds
to a condition 2 highly probably not verified. We have also investigated the average throughput
per sector in DL for different UEs loads (37, 56 and 75 UEs). It is worth noting that the MLB
implementation improves the throughput of most areas (2, 4, 7, 8 and 9) (Figure 6). The results

62
for the sectors 1, 3, 5 and 6 are mitigated. This means that one of the two proposed algorithms
(Alg_MLB1 and Alg_MLB2) provides a better throughput than Alg_without_MLB.
Thanks to MLB implementation traffic in overloaded cells is substantially decreased since the
37 56 75
0
2
4
6
8
10
12
14
UEs number
Averagethroughput(Mbit/s)
A3 (without MLB activation)
A3+Alg_MLB1
A3+Alg_MLB2
Figure. 5: Global network throughput in DL with respect to UEs density
37 UEs
56 UEs

63
75 UEs
Figure. 6: Average throughput per sector in DL for different UEs densities (Alg_MLB1 Alg_MLB2 and
Alg_without_MLB)
exceeded load is transferred to the less loaded neighbouring cell(s). This results in a significant
reduction of the packet loss ratio compared to Alg_without_MLB (Figure 7). From Figure 7, the
loss ratio is an increasing function of the network load. With MLB, losses may occur in a cell if it
is enough overloaded and cannot find a neighbouring cell willing to cooperate. In other words, the
cell loss ratio of the current cell closely depends on both its own load and the load of the
neighbouring cell(s). Since MLB aims to reduce the loss ratio through an evenly load distribution
between cells, we observe a significant reduction of this metric for the scenarios with MLB when
compared with the scenario without MLB (Figure 7). Figure 8 shows the evolution of the number
of successful HO with respect the global network load (UEs density for the three investigated
algorithms (Alg_MLB1, Alg_MLB2 and Alg_without_MLB).
37 56 75
0
0.05
0.1
0.15
0.2
0.25
UEs number
Relativelossrate(%)
A3 (without MLB)
A3+Alg_MLB1
A3+Alg_MLB2
Figure. 7: Relative loss ratio vs UEs density

64
37 56 75
0
50
100
150
200
250
300
350
400
UEs number
NumberofHOswithhysteresis
A3 (without MLB)
A3+Alg_MLB1
A3+Alg_MLB2
Figure. 8: Number of successful HO vs UEs density
The load is quantified through the number of active UEs. Notice that the number of HO is
significantly important for both Alg_MLB1 and Alg_MLB2 when compared with
Alg_without_MLB. This is explained by the fact that upon triggering MLB, a transfer of excess
traffic in the overloaded cell to less loaded neighbouring cells is performed. This load transfer is
achieved by adjusting the hysteresis value which favours handovers. We can also from Figure 8
establish a comparison between the performance of Alg_MLB1 and Alg_MLB2. Note especially
for relatively low loads that the number of handovers in the LTE network for the Alg_MLB1 is
considerably lower than for Alg_MLB2. As soon as the amount of available resources is beyond
the threshold ThAvailMLB . This may be explained by the following arguments:
• For Alg_MLB1: There is a gradual and linear reduction of the hysteresis value (and thus
increase of neighbouring cell size) as soon as the MLB process is initiated and the
available load level of the neighbouring cell(s) is greater than ThAvailMLB
• For Alg_MLB2: The hysteresis value of the neighbouring cell(s) decrease(s) sharply,
promoting hence the number of handovers compared to Alg_MLB1.
The comparative study investigated above shows that Alg_MLB1 algorithm provides indeed lower
loss ratio when compared with Alg_MLB2 however it may cause in return an increase of HO
frequency.
5. CONCLUSION
In this article we implemented through ns-3 new load balancing algorithms at ENodeBs level for
DL data transmission in LTE networks. We evaluated next the performance of these algorithms
through appropriate scenarios to MLB context. Performance evaluation focused specifically on
the investigation of the MLB impact on the global network throughput, the average throughput
per sector and the loss ratio and the number of successful HO, for different UEs densities. The
obtained results highlight comparative performance study between the simulation results with and
without MLB. We show through these investigations the advantage of each MLB algorithm in
finding convenient compromise in terms of throughput, loss ratio and number of successful HO.
As perspectives, we plan to conduct more exhaustive simulations on joint-optimization of MLB
and MRO. This leads to finding, for a given hysteresis value of a given cells’ loads, the optimal

65
TTT value. We also plan in future to model MLB using the Markov decision process. This will
allow to find the optimal values of various activation thresholds, acceptance and deactivation of
MLB according to the load algorithm. Our main contribution in this paper is the implementation
of MLB algorithms on ns-3. Note that the latest version on which we worked does not implement
the MLB in the LTE module of ns-3.
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Authors
Mohamed Escheikh received in 1992 the Diploma degree in electrical engineering.
In 1994, he received a Master’s degree and in 2001 a PhD degree with Distinction all
in electrical engineering from National Engineering School of Tunis ENIT (Tunisia).
He is currently Assistant Professor at ENIT since 2001, member of SYSCOM
laboratory at ENIT since 1992 and associate researcher with Vespa Team of Cedric
CNAM Paris. His research interests include in particular dependability analysis,
queuing systems, Petri nets, mobile networks optimization, cloud computing and
power management.
Hana Jouini received the Master's. degree in mathematics and computer science at
the university of Paris Descartes, Paris, France in 2013. She is currently a Ph. D.
student in computer science and telecommunication at the National engineering
school of Tunis and at CNAM Paris.
Kamel Barkaoui is full professor at the Department of Computer Science of Conservatoire National des
Arts et Métiers (CNAM - Paris) since 2002. He holds a Ph.D in Computer Science (1988) and Habilitation à
Diriger des Recherches (1998) from Université Paris 6 (UPMC). His research interests include formal
methods for verification, control and performance evaluation of concurrent and
distributed systems. He has published over 150 papers on these topics, as well as
supervising 25 PhD theses and several MSc thesis projects. He received the 1995
IEEE Int. Conf. on System Man and Cybernetics Outstanding Paper Award. He
served on PCs and as PC chair and OC chair of a number of international workshops
and conferences in his areas of research. He is the SC chair of the International
Workshop on Verification and Evaluation of Computer and Communication Systems
(VECoS). He was recently general co-Chair of the 18th International Symposium on
Formal Methods (2012) and general chair of 35th International Conference on Application and Theory of
Petri Nets and Concurrency (Petri Nets 2014) and the 14th International Conference on Application of
Concurrency to System Design (ACSD’2014). He is associated editor of the International Journal of Critical
Computer-Based Systems (IJCCBS).

MODELING, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF MOBILITY LOAD BALANCING FOR LTE DOWNLINK DATA TRANSMISSION

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MODELING, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF MOBILITY LOAD BALANCING FOR LTE DOWNLINK DATA TRANSMISSION