Analysis of Handover Performance in LTE Femtocells Network · PDF fileorganized as follows: In Sect. 2, the system architecture of femtocell network, the access control and the handover

Analysis of Handover Performance in LTE FemtocellsNetwork

Nurul ‘Ain Amirrudin1• Sharifah Hafizah Syed Ariffin1

•

Nik Noordini Nik Abd. Malik1• Nurzal Effiyana Ghazali1

Published online: 9 October 2017� Springer Science+Business Media New York 2017

Abstract Handover management is one of the main factors representing the effectiveness

of every wireless network technology. Due to the special characteristics of a femtocell,

unnecessary handover occurs more frequently. This issue has attracted interest in devel-

oping a new handover algorithm in femtocell network. The standard handover algorithm

relies on Reference Signal Received Power or Reference Signal Received Quality (RSRQ)

level. However, this technique causes an unnecessary handover and reduces the user

throughput. Mobility prediction is one of a popular technique to be implemented in han-

dover algorithm. This paper analyzes the handover performance in femtocell network by

using two types of handover algorithm which are standard A2-A4-RSRQ handover algo-

rithm and proposed prediction handover algorithm. The analysis is performed in terms of

the number of handover, the number of unnecessary handover, and the user throughput.

The root cause of user throughput degradation is also analyzed. The results show that the

prediction handover algorithm provides better performance than the A2-A4-RSRQ han-

dover algorithm in terms of the number of handover and user throughput.

Keywords Femtocell � Long Term Evolution (LTE) � Handover � RSRQ � Prediction

& Nurul ‘Ain [email protected]

Sharifah Hafizah Syed [email protected]

Nik Noordini Nik Abd. [email protected]

Nurzal Effiyana [email protected]

1 Faculty of Engineering, MAHSA University, Selangor, Malaysia

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Wireless Pers Commun (2017) 97:1929–1946DOI 10.1007/s11277-017-4222-3

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1 Introduction

Long Term Evolution (LTE) has been introduced by the Third Generation Partnership

Project (3GPP) due to high demand for higher data rate and quality of service (QoS). LTE

is a latest standard in the mobile network technology tree that previously realized the GSM/

EDGE and UMTS/HSxPA network technologies [1]. The objective of LTE development is

to build a system that meets demand for a high data rate, low latency, and optimization for

packet switched traffic [2]. The LTE is expected to provide peak data rates of at least

100 Mbps in downlink and 50 Mbps in uplink. End-user latency is reduced to 10 ms in

round trip times while control plane latency (i.e. transition time from idle state to active

state) is less than 100 ms [3, 4]. A major difference of LTE to other technologies is the

radio interface where in LTE, Orthogonal Frequency Division Multiplexing (OFDM) and

Single Carrier Frequency Domain Multiple Access (SC-FDMA) are used as radio access

schemes for downlink and uplink respectively [5].

In a wireless network, voice calls and data traffic are dominated by broadband services

indoor [6, 7]. Therefore, it is extremely important for mobile operators to provide a better

coverage in an indoor environment, not only for voice, but also for video and high speed

data services, which are becoming increasingly important. Hence, femtocells have been

introduced to enhance indoor coverage, deliver high bandwidths and new services to end-

customers, as well as can off-load traffic from the existing macro-cellular networks.

Moreover, from the mobile operator point of view, when the indoor coverage is good, this

may indirectly increase Average Revenue per User (ARPU) and enhance customer loyalty

[8].

By definition, a femtocell is a low-power wireless access points with small cell cov-

erage. It operates on a spectrum licensed to connect standard mobile devices to a mobile

operator’s network using residential digital subscriber line (DSL) or cable broadband

connections. Besides than offering broadband services indoor (i.e. homes and offices),

femtocells also have been developed to provide a service at outdoor scenarios with a very

limited geographical coverage [9]. Since the femtocell mainly focuses on the indoor

environment, it is known as a home based station. In 3GPP LTE, the femtocell is more

commonly known as home evolved Node B (HeNB). Femtocells require low power,

ranging between 13 and 20 dBm with coverage from 15 to 50 m [10].

In terms of mobility management in femtocell deployment, there are several chal-

lenges. A femtocell is a small base station that deployed in an unplanned manner

because it is installed by the end-user. Therefore, there is a possibility that a large

number of femtocell may install in single macrocell. This scenario will create a large

number of neighbour cell list and interference problems [11]. When the neighbour cell

list is high, the network takes a long time to scan the best femtocell to attach to the user.

Moreover, the femtocell has small coverage, which is around 50 m. This characteristic

may cause the user pass-by the femtocell within a short time, and then attach to other

femtocell or previous femtocell. This unnecessary handover and ping-pong handover will

reduce system capacity and reduce user throughput. Therefore, it is necessary to design

an appropriate handover decision, in order to have a seamless handover and high user

throughput.

This paper discusses handover in femtocell network and analyzes handover perfor-

mance in terms of number of handovers, number of unnecessary handovers, and the user

throughput. Two types of handover algorithm are use, namely the standard A2-A4-RSRQ

handover algorithm and proposed prediction handover algorithm. The rest of the paper is

1930 N. ‘A. Amirrudin et al.

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organized as follows: In Sect. 2, the system architecture of femtocell network, the access

control and the handover scenario in femtocell are discussed. Then, Sect. 3 discusses

related work of handover decision on femtocell network. The handover call flow in

femtocell network describes in Sect. 4. In Sect. 5, two types of handover algorithms are

presented. The handover performance in femtocell network using both handover algo-

rithms are analyzed in Sect. 6, before concluding the analysis in Sect. 7.

2 LTE Femtocell Network

2.1 LTE Femtocell System Architecture

LTE has been defined as an IP-based and flat core network architecture. The architecture is

a part of System Architecture Evolution (SAE) that consists of Evolved Packet Core (EPC)

and Evolved Universal Terrestrial Radio Access Network (E-UTRAN). The EPC contains

of Mobility Management Entity (MME), Serving Gateway (S-GW), and Packet Data

Network Gateway (PDN GW). The MME is in charge of all the control plane (C-plane)

functions related to subscriber and session management. The SGW serves as a mobility

anchor for user plane (U-plane) during handovers, and also responsible to route and for-

ward user data packets. The PDN GW provides access from the User Equipment (UE) to

external packet data networks and allocates IP addresses for the UE and QoS enforcement.

The E-UTRAN consists of evolved Node B (eNB) and Home eNB (HeNB), referred to as

macro base station and femto base station in the LTE, respectively. The architecture may

deploy a HeNB Gateway (HeNB GW) in order to manage a large number of HeNBs in a

scalable manner.

The eNBs and HeNBs are interconnected with each other by means of X2 interface.

The S1 interface is a reference point between the E-UTRAN and the EPC. More

specifically, the E-UTRAN is interconnected to the MME with S1-MME interface, and

interconnected to the SGW with S1-U interface. The HeNB GW serves as a concentrator

for the C-plane, and is therefore connected with HeNBs and MME through an S1-MME

interface. The S5 interface is a reference point between HeNBs and SGW. The S5

interface is available if the HeNBs support the (Local IP Access) LIPA function. The

overall E-UTRAN architecture with deployed HeNB GW is shown in Fig. 1. The HeNB

shall acts as the eNB in terms of function supported and the procedures run between the

HeNB and the EPC [12].

2.2 Access Control of Femtocell

In LTE, the UE is designed to be aware of the femtocell, where the UE should be able to

recognize a cell, whether it is a macrocell or a femtocell. If the UE is not interested in the

femtocell, the searching of femtocell is avoided. Otherwise, if the UE is interested, it

quickly completes the searching procedure. There are three types of access control models

in femtocell, which are open, closed, and hybrid access mode.

For the open access mode, the femtocell acts as the macrocell where all users can access

to the femtocell. For example, an operator deploys this femtocell to provide a good cov-

erage in an area where there is a coverage hole. For the closed access mode, the femtocell

can be accessed by the users under closed subscriber group (CSG) only. For the hybrid

access mode, the femtocell is similar to the closed access mode, but also open to the non

Analysis of Handover Performance in LTE Femtocells Network 1931

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CSG if the bandwidth is available. To differentiate the femtocell access modes, CSG

identity (ID) and CSG indicator have been introduced. CSG ID is a unique numeric

identifier that broadcasts in system information (SI) by the CSG and the hybrid cell. CSG

indicators are presented with a value of TRUE for the CSG cell and absent for the hybrid

and the open cell [13–15].

2.3 Handover Scenario in LTE Femtocell

The latest E-UTRAN architecture as per discussed in [12] defines a direct interface

between eNB and HeNB. Therefore, it is possible to execute an X2-handover between eNB

and HeNB. If there is no X2 interface between them, the S1-handover must be performed.

It may have three handover scenarios in femtocell network:

1. Inbound handover; this represents the handover of the UE from the eNB to the HeNB.

The handover scenario is the most challenges since there are large numbers of possible

targets HeNBs and also the restriction of access control of the HeNB.

2. Outbound handover; this represents the handover of the UE from the HeNB to the

eNB. The handover scenario is not as complicated as inbound handover, since there is

only one candidate of eNB and it is open to all users.

3. Inter-HeNB handover; represents the handover between the HeNBs. The handover

procedure is similar to inbound handover because in this scenario, there are large

numbers of possible target HeNBs and also needs to consider on the access control of

the HeNB.

Fig. 1 Overall E-UTRAN architecture with deployed HeNB GW


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3 Related Work

Mobility management is a set of tasks for controlling mobile station in a wireless networks

to maintain their connections while moving [16]. In LTE system, fast and seamless han-

dover is one of the main goals. Due to its orthogonal frequency division multiple access

(OFDMA) technology, only hard handover is available [17]. The hard handover also called

as a ‘‘break before make’’ connection. Unlike soft handover, in the hard handover, the link

to the current base station (BS) is terminated before the new link is connected to the new

BS [18]. In other words, only one link is available at one time. This scenario may create an

interruption time in the user plane, and reduce handover performance in terms of success

rate and handover delay [5].

Several mobility techniques have been proposed to enhance handover performance,

especially to reduce unnecessary handovers. By reducing the unnecessary handover, the

ping-pong effect may decrease. The ping-pong effect occurs when a call is handed over to

a new cell and handed back to the previous cell in less than a critical time [19]. This ping-

pong effect will reduce user throughput and system capacity as well. The most usual

technique to mitigate the number of handover and unnecessary handover is by optimizing

the handover parameter which is handover margin (HM) and time-to-trigger (TTT). In

[19], the authors proposed a handover optimization algorithm by tuning the handover

parameter (i.e. hysteresis and TTT) based on handover performance indicator (handover

failure ratio, ping-pong handover, and call dropping ratio). The results show that the

handover failure ratio and ping-pong handover ratio are driven to zero after 500 s simu-

lation time when the optimization is adopted. [20] proposed a cell-type adaptive handover

margin. The algorithm assigns different HM based on target cells and user’s speed. The

results show that the ping-pong rate and radio link failure (RLF) rate are decreased when

compare to the constant HM value.

Since the coverage of the femtocell is small, user with a high velocity will pass-by the

femtocell in a short time. Therefore, by considering the user’s quality of service (QoS), it is

unnecessary for high speed user to execute handover, especially for non-real-time service.

Several researchers have proposed a handover algorithm by considering the user velocity.

In [11], the authors have proposed a new handover algorithm based on the user’s speed and

user’s application (i.e. real time service and non-real time service). The UE’s speed is

divided into three categories which are low, medium, and high speed. The handover

algorithm decides to not execute handover for high speed user and medium speed with

non-real-time service, and execute handover for other criteria. Besides user velocity, [10]

considered another parameter, namely interference. The interference level is considered for

non-CSG user that need to handover to hybrid femtocell. In order to reduce the interfer-

ence, the handover is needed for low speed user. Other than that, [21] have added several

parameters for the handover decision procedure. The mobility prediction of the user is

considered in handover decision procedure. Based on user current position and user

velocity, it can estimate where the user is moving, thus the next BS that user need to

handover can be predicted. Moreover, proactive and reactive handover is proposed, where

proactive handover focuses to minimize packet loss while reactive handover is aim to

reduce the number of handover. All proposed handover algorithms are aimed to improve

the handover performance by reducing the number of handover only without considering

on the user throughput. However, from user’s point of view, it does not matter how many

handover is occurred, as long as they get the higher throughput and do not lose a

connection.


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During handover executions in which the UE receives a handover command from the

source base station (i.e. eNB or HeNB), the UE cannot sends and receives any packet until

a new connection is established. Therefore, the handover latency needs to be reduced as

much as possible in order to achieve a seamless handover. One of the main factors of

handover latency is resource allocation that takes a lot of time. One of the most effective

approaches to reduce delay in resource allocation is to predict the next location of the user

[22]. The function of mobility prediction is to detect the identity of the future cell so that a

resource reservation can be performed prior to the actual handover. This technique has

attracted attention from researchers to enhance the handover performance. In [23], the

authors proposed a prediction based on user’s mobility history. The proposed algorithm

needs the network to recognize the user and record the movement information. Moreover,

the algorithm requires considering the signal strength of the user and then setting as a

candidate handover once the signal strength is higher than a certain threshold. The results

show that the more regularity of user movement, the better performance gain has been

gotten in terms of number of handover and ping-pong rate. During the normal handover

procedure, the handover decision is performed based on measurement reports sent by the

UE. The measurement report is sent frequently so that the network can aware the channel

status of the UE. However, this situation may decrease the control channel capacity for

downlink. Thus, in [24], a handover decision without relying on measurement report was

proposed. Based on past channel state information, the source base station can predict the

channel quality to perform the handover decision. The proposed scheme has reduced the

outage probability, but does not consider the number of handovers.

4 Handover Call Flow

In an LTE network, there are two types of handover, namely X2-based handover and S1-

based handover. By default, the X2-based handover is implemented unless there is no X2

interface between source eNB/HeNB and target eNB/HeNB or if there is a setting to use

S1-based handover. The difference between those handover types are the involving of the

MME. For an X2-based handover, the message from source eNB/HeNB is directly sent to

the target eNB/HeNB. However, for S1-based handover, the MME is required as a medium

to send the message. In this paper, the X2-based handover is used to analyze the handover

performance in the LTE femtocell network.

Handover can be divided into three phases; preparation (initiation), execution and

completion [25]. The message sequence diagram for X2-based handover is depicted in

Fig. 2. The first phase is handover preparation where involve the source HeNB, the target

HeNB and the UE. The main messages for this phase are described as follows:

• The source HeNB sends the measurement control to configure the UE measurement

procedure.

• The UE sends the measurement report after it meets the measurement report criteria

that set in measurement control.

• The source HeNB makes a handover decision based on the measurement report.

• The target HeNB performs admission control and checks for resource availability, then

reserves it.

• The source HeNB sends the handover command to the UE.

For handover execution, the procedures are described as follows:


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UE Source HeNB Target HeNB SGW/PGWMME

Measurement Control

Measurement Reports

Handover Decision

Handover Request

Admission Control

Handover Request Ack

Handover Command

SN Status Transfer

Detach from old cell, synchronize

to new cell

Handover Confirm

Path Switch Request

Modify Bearer Request

Packet Data Packet Data

Deliver buffered and transit packets

to target HeNB

Buffer packets from source

HeNB

Data Forwarding

Synchronisation

Packet Data

Switch DL path

End Marker

End Marker Packet Data

Modify Bearer Response

Path Switch Request Ack

UE Context Release

Release Resources

Packet Data

Han

dove

r Pr

epar

atio

nH

ando

ver

Exe

cutio

nH

ando

ver

Com

plet

ion

Fig. 2 X2-based handover call flow between HeNBs


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• The UE detach from the source HeNB and synchronize to the target HeNB.

• The source HeNB sends the Sequence Number (SN) Status Transfer to the target HeNB

to convey the Packet Data Convergence Protocol (PDCP) and Hyper Frame Number

(HFN) status of the E-UTRAN Radio Access Bearers (E-RABs). At this stage, the

source HeNB freezes its transmitter/receiver status, and no data can be sent or received

[26].

In the last phase, which is handover completion, the processes are described as follows:

• Once the UE has synchronized with the target HeNB, it sends the handover confirm.

• The target HeNB sends a path switch request to inform that the UE has changed cell.

• The SGW switches the path of downlink data to the target HeNB.

• The source HeNB releases radio and control of related resources once it receives the

UE context release message.

From the handover call flow, the handover latency can be measured, which is duration

between the UE sends the measurement report and when the UE sends the handover

confirm message. It is involving the handover decision and admission control. The more it

takes on handover decision, the higher the handover latency is. Moreover, if there is no

bandwidth available in the target HeNB, the handover process may take longer time to

complete. Therefore, it is important to have a simple handover decision so that it takes less

time to complete.

5 Handover Algorithm

Handover algorithm or handover decision is a most challenging part in the handover call

flow. The decision is made based on measurement reports provided by the UE. The

common system metrics include in the measurement report for handover decision are

Signal-to-Interference-and-Noise-Ratio (SINR), Received Signal Strength Indicator

(RSSI), Reference Signal Received Power (RSRP), and Reference Signal Received Quality

(RSRQ). These metrics are used to select the possible handover candidate. Besides, the

control parameters are tuned by the handover algorithm to increase the handover perfor-

mance of the network [19]. Normally, the parameters are hysteresis and time-to-trigger

(TTT). Other than that, user’s speed and user’s application (i.e. real-time application and

non-real-time application) may also be considered. The handover is triggered if the con-

dition set in the control parameter is fulfilled.

As stated, the measurement reports are sent by the UE if the measurement report criteria

are met. The measurement report criteria can be either event triggered or periodic. Periodic

reporting is typically used for measuring an automatic neighbour cell search. The event

triggered measurements based on E-UTRA measurements are listed in Table 1 [4, 27].

5.1 A2-A4-RSRQ Handover Algorithm

In an LTE network, the UE has to report two parameters on reference signal which are

RSRP and RSRQ every 200 ms [28]. RSRP is the absolute signal strength of the LTE

reference signal related in dBm while RSRQ is the DL signal-to-interference ratio in dB

measured on the LTE reference signals [29]. Both RSRP and RSRQ are used to determine

the best cell for the user. However, the RSRQ provides additional information to determine

interference level at the location. Therefore, RSRQ is more appropriate than RSRP for the


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femtocells network, which consists of large number of femtocells where the interference

level is high.

Figure 3 shows a simple A2-A4-RSRQ handover algorithm. The algorithm has been

discussed and implemented in Network Simulator 3.18 [30] based on standardization of

3GPP [28]. When the measurement report criteria are met, the UE sends the measurement

report consist of RSRP and RSRQ value of the source HeNB and all neighbour cells. The

source HeNB checks the RSRQ level either lower than the threshold level or not. If the

RSRQ level is lower than the threshold, look the neighbour cell with highest RSRQ level

and the difference is determined. The handover is triggered if the difference is higher or

equal to the neighbour cell offset. The value of the source threshold and the neighbour cell

offset is set by the network. The neighbour cell offset is act as a handover margin where it

can reduce the number of handover. The higher the neighbour cell offset value, the later the

handover is triggered which may increase the handover delay.

From the algorithm, it shows that there are two parameters to control the handover

decision, which are source cell threshold and neighbour cell offset. These parameters are

set by the network to control the handover decision. The source cell threshold is a mini-

mum value of the RSRQ before handover is triggered. For the second parameter which is

neighbour cell offset, the RSRQ value of the neighbour HeNB must be higher than the

source HeNB. If the criterion is not met, no handover is imposed. In other words, there is

no suitable HeNB to serve the user with acceptable RSRQ value. Thus, the user will

experience a bad QoS or even may lose the connection.

5.2 Prediction Handover Algorithm

A handover algorithm which relies on LTE measurements such as RSRP and RSRQ may

cause a ping-pong effect. This is because the RSRP and RSRQ values are fluctuated cause

by interference or other issues. The hysteresis and TTT are introduced to reduce the ping-

pong effect, however, it may delay handover for a while and cause a user throughput

degradation. Therefore, it is necessary to handover at correct time. Prediction in handover

algorithm is made to predict the next location of the user and predict the best time to trigger

handover to the new base station. By predicting the next location, the resource reservation

can made earlier and may reduce the handover latency. When predicting the best time for

handover trigger, the handover may trigger at the optimal time to mitigate the handover

delay. In a femtocell network where the coverage area is small, handover delay should be

reduced as much as possible. This is because when the handover is late to trigger, this will

result in packet loss. Moreover, delaying too long may make it impossible for the user to

meet its QoS objectives.

Table 1 Event triggered reports for E-UTRA [4, 27]

Event triggered Criteria

Event A1 Source cell becomes better than an absolute threshold

Event A2 Source cell becomes worse than an absolute threshold

Event A3 Neighbour cell becomes an amount of offset better than source cell

Event A4 Neighbour cell becomes better than an absolute threshold

Event A5 Source cell becomes worse than an absolute threshold 1 and neighbourcell becomes better than an another absolute threshold 2


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Figure 4 shows the optimal point to trigger handover from source HeNB to target

HeNB. As the user moves towards the target HeNB with a certain velocity, the handover

shall occur once the reported RSRQ value of the target HeNB is greater than the source

HeNB. Theoretically, the handover shall be performed at the optimal handover point as

illustrated in Fig. 4. Later or earlier from this point may affect the handover performance in

terms of user throughput or may lose a connection. One of the ways to achieve an optimal

handover point is by mobility prediction.

Start

Source HeNB receives measurement reports from

UE (Event A2 and A4)

Source HeNB RSRQ<= Source Cell

Threshold?

Look for neighbour cell with the highest RSRQ

(best neighbour RSRQ - source HeNB RSRQ)

>= Neighbour Cell Offset?

No

No

Yes

Yes

Trigger handover procedure for this UE to the best

neighbourEnd

Fig. 3 A2-A4-RSRQ handover algorithm


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In this paper, mobility prediction based on the user’s mobility history as discussed in

[31] is analyzed. The prediction technique is intended to predict the best target HeNB,

while the best time to trigger handover is relies on the User velocity and the user’s

direction. It assumes that the user sends their location to the network constantly. Thus, the

network can determine the user’s direction and the user velocity. From user velocity, the

optimal handover point can be determined. This paper analyzes the effect of the handover

performance when the handover is triggered at optimal handover point based on prediction

and compare it with the A2-A4-RSRQ handover algorithm.

6 Performance Evaluation

6.1 Scenario

In this paper, a scenario of 22 HeNBs is used as illustrated in Fig. 5. Distance between the

HeNBs is 100 m, and the coverage area is overlaps with each other to avoid any inter-

ruption. The initial state of the user is set between the HeNB1 and HeNB12, and it is

attached to the HeNB1 at start point. The user is moving from an initial state towards the

HeNB11 and HeNB22 with constant velocity. User velocity is varied from 1 to 10 m/s. All

HeNBs are using an open access mode, thus the user can attach to all HeNBs without any

restriction. Both handover algorithm types, namely the A2-A4-RSRQ handover algorithm

and prediction handover algorithm, are used to evaluate the handover performance. Details

of simulation parameters are listed in Table 2.

Fig. 4 Optimal handover point


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6.2 Handover Performance Indicators

The handover performance indicator (HPI) is measured in terms of the number of han-

dovers, the number of unnecessary handovers, and the user throughput. The number of

handover is defined as a number of successful handover, while the number of unnecessary

handover is defined as a scenario where the handover is triggered to a new cell and within a

Fig. 5 The scenario of the evaluation

Table 2 Simulation parametersMetrics Value

Simulation time 110–1100 (s) [depends on user velocity]

Simulation area 1100 (m) 9 200 (m)

Number of HeNBs 22

HeNB transmit power 20 (dBm)

Distance between HeNBs 100 (m)

Number of user 1

User velocity 1–10 (m/s)

Source cell threshold 30

Neighbour cell offset 1

Critical time 3 (s)

0 20 40 60 80 100 120 140 160 180 200 220

HeNB5

HeNB10

HeNB15

HeNB20

Simulation Time [s]

HeN

B ID

A2-A4-RSRQ ho algorithm

Prediction ho algorithm

Fig. 6 Handover scenario for user’s speed 5 m/s


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critical time the call is handover back to the previous cell or to another cell. For the last

HPI, user throughput is an important metric to be measured to analyze the handover

performance. Throughput is defined as a ratio of total number of packets received over the

total simulation time. Mathematically, it can be defined as follows:

Throughput ¼ Total number of packets received

Total simulation timeð1Þ

6.3 Results

The experiments are evaluated by using network simulator 3 (NS3.18). Two types of

handover algorithms, A2-A4-RSRQ handover algorithm and prediction handover

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

UE velocity [m/s]

No.

of h

ando

ver

No. of handover A2-A4-RSRQNo. of unnecessary handover A2-A4-RSRQNo. of handover predictionNo. of unnecessary handover prediction

Fig. 7 No of handover and no of unnecessary handover for both handover algorithms

1 2 3 4 5 6 7 8 9 1053

53.5

54

54.5

55

UE velocity [m/s]

Use

r thr

ough

put [

pack

et/s

]

A2-A4-RSRQ ho algorithmPrediction ho algorithm

Fig. 8 User throughput for both handover algorithm


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algorithm are used to analyze the handover performance in femtocells network. Firstly, the

handover scenario is evaluated to show a scenario of unnecessary handover in the fem-

tocell network. Figure 6 shows the handover scenario for both handover algorithms. The

graph shows that there is a handover occur from HeNB1 to HeNB12 at time 18.8 s for the

A2-A4-RSRQ handover algorithm. The call is handed back to the HeNB1 after 1.4 s the

user attached to the HeNB12. This scenario called as unnecessary handover. It also shows

that there is no unnecessary handover occurred if the prediction handover algorithm is

implemented. Since the RSRQ value is fluctuated, such unnecessary handovers may fre-

quently occur. One of the ways to mitigate the unnecessary handover is by increasing the

hysteresis. However, the handover is intended to delay for a while and may cause user

throughput degradation.

(a)

(b)

0 20 40 60 80 100 120 140 160 180 200 220-11

-10.5

-10

-9.5

-9

-8.5

-8

-7.5

-7

Simulation Time [s]

RS

RQ

val

ue [d

B]

0 20 40 60 80 100 120 140 160 180 200 2200

20

40

60

80

100

120

Simulation Time [s]

Use

r thr

ough

put [

pack

et/s

]

Fig. 9 The effect of RSRQ level to user throughput. a The RSRO value of source HeNB. b The userthroughput


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Then, the number of handovers and the number of unnecessary handovers between both

handover algorithm types have been analyzed. Figure 7 shows the number of handover

when the User velocity is varied. The number of handovers is high for the A2-A4-RSRQ

algorithm due to unnecessary handovers. On average, the number of handover is 37 for the

A2-A4-RSRQ algorithm where most of it is denoted from the unnecessary handover. Since

no unnecessary handover occurred for the prediction algorithm, the number of handovers

was less at 10 handovers. Unnecessary handovers for the A2-A4-RSRQ algorithm are

fewer for low-velocity users, because the user spends more time in femtocell coverage than

a high-velocity user.

Third, the user throughput which is the most important HPI is analyzed. Figure 8 shows

the user throughput for both handover algorithms when the user velocity is varied. The

graph shows that the user throughput is high for prediction algorithm with percentage of

1%. The difference of user throughput between the handover algorithms is denoted from

the unnecessary handover scenario. However, the overall user throughputs for both han-

dover algorithm types are quite low, as only 54% of all packets sent were received by the

user. The User velocity does not influence so much for the user throughput.

Lastly, the cause of low user throughput has been investigated. The sample of data with

user velocity of 5 m/s with A2-A4-RSRQ handover algorithm has been taken. A rela-

tionship between the user throughput and the RSRQ value is shown in Fig. 9. The graph

shows that user throughput is decreased when the RSRQ value is decreased. The drop of

RSRQ value has occurred at the overlap of the femtocell coverage area. For example, at

time 20 s, the coverage area of the femtocell overlaps among HeNB1, HeNB2, HeNB12,

and HeNB13. This kind of overlapping scenario causes a high interference and finally

decreases the user throughput. As known, RSRQ value can determine the existence of

interference. If the RSRP value remains stable or becomes even better while the RSRQ

value is declining, this is an unambiguous symptom of rising interference [29]. Therefore, a

technique must to be implemented to reduce the interference level in femtocell network,

which is beyond the scope of this paper.

7 Conclusion

The femtocell has been introduced in Long Term Evolution (LTE) technology to provide

better coverage, especially in indoor environments and outdoor scenarios with limited

geographical coverage. As in other wireless technologies, mobility management is the

most important part to be catered to show the effectiveness of the technology. This paper

has analyzed the handover performance in femtocell network by using two types of han-

dover algorithm which are standard A2-A4-RSRQ handover algorithm and proposed

prediction handover algorithm. The experiments were used analyzed the handover per-

formance in terms of the number of handover and the user throughput. The results show

that by predicting the best target cell and the best time for handover cause a better

performance compare if only rely on RSRQ value. The experiments also analyze the root

cause of user throughput degradation. Further work on mobility management in femtocell

network shall examine the interference level as well.

Acknowledgements The authors would like to thank all who contributed toward making this researchsuccessful. The authors wish to express their gratitude to Ministry of Higher Education (MOHE), ResearchManagement Center (RMC) for the sponsorship, and Telematic Research Group (TRG), Universiti


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Teknologi Malaysia for the financial support and advice for this project. (Vot NumberQ.J130000.2509.05H58 and PY/2013/01168).

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Nurul ‘Ain Amirrudin received her Ph.D degree in Electrical Engi-neering (2016), and her B.E. in Electrical Engineering - Telecommu-nication (2008) from Universiti Teknologi Malaysia, Malaysia. Shejoined Telekom Malaysia Berhad as an assistant manager of ProductDevelopment and Management starting July 2008 until end of 2011before pursuing her Ph.D. She is currently a lecturer of Faculty ofEngineering in MAHSA University, Malaysia. Her research interestsinclude mobility management, mobility prediction and their applica-tions in Long Term Evolution.

Sharifah Hafizah Syed Ariffin received her Ph.D. degree in 2006from Queen Mary University of London, London, received her Masterdegree in Mobility Management in Wireless Telecommunication(2001) from Universiti Teknologi Malaysia and her B.E. (Hons) inElectronic and Communication Engineering from University of NorthLondon, London, England in 1997. She is currently an associateproffessor in the Faculty of Electrical Engineering, Universiti Tekno-logi Malaysia, Malaysia. Her research interests include WirelessSensor Network, IPv6 network and mobile computing system, handoffmanagement in WiMax, low rate transmission protocol using IPv6-6loWPAN, network modelling and performance, priority scheduling inpacket network.


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Nik Noordini Nik Abd. Malik graduated with B.E. (Electrical-Telecommunication); M. E. (Radio Frequency and Microwave Com-munications) and Ph.D. (Electrical Engineering) from UniversitiTeknologi Malaysia (UTM), Malaysia; University of Queensland,Australia and Universiti Teknologi Malaysia (UTM), Malaysia in2003, 2005 and 2013, respectively. She was a research and develop-ment electrical engineer in Motorola Solutions Penang, Malaysia in2004. Then, she has been a lecturer with the Faculty of ElectricalEngineering, UTM since 2005.

Nurzal Effiyana Ghazali received her B.E. (Hons) Electrical(Telecommunications) from Universiti Teknologi Malaysia in 2007.Master of Engineering in Electrical and Computer Science from Shi-baura Institute of Technology, Japan in 2010 and Master of Engi-neering in Electrical (Electronics and Telecommunications) fromUniversiti Teknologi Malaysia in 2011. Currently, she is a Ph.D.candidate and her research interests are Long Term Evolution (LTE),WiMAX, WiFi, Network Mobility, IPv6 network, Handover Man-agement in Proxy Mobile IPv6, Mobility Management and MobileComputing.


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Documents

Analysis of Handover Performance in LTE Femtocells Network · PDF fileorganized as follows: In Sect. 2, the system architecture of femtocell network, the access control and the handover