Handover Optimization Algorithm in LTE High-SpeedRailway Environment

Fang Yang1 • Honggui Deng1 • Fangqing Jiang1 •

Xu Deng1

� Springer Science+Business Media New York 2015

Abstract The traditional A3 event-based HO algorithms are mainly designed for the low

speed (\30 m/s) networks. That aren’t suitable for the high-speed railway scenario which

the link quality may deteriorate sharply and the wireless channel environment may become

unstable with the increase of velocity. To overcome the disadvantages of the handover

algorithms in LTE high-speed railway networks, we proposed a handover optimization

algorithm based on statistics, where we not only consider reference signal received power

and reference signal received quality at the same time but also the rate of cell resources

change. The simulation results show that the proposed algorithm has higher handover

success rate and lower handover numbers. Thus unnecessary handover is reduced by up to

47 % and the novel algorithm provides success rate of 0.5–13.9 % higher than the classical

A3 algorithm under different conditions of velocity, and greatly improve handover

performance.

Keywords LTE � High-speed railway networks � Handover algorithm � RSRP � RSRQ

1 Introduction

Universal terrestrial radio access network long-term evolution (UTRAN LTE), also known

as Evolved UTRAN (E-UTRAN), is the 4th generation cellular mobile system that is being

developed and specified in 3GPP [1]. LTE uses different radio access technologies for

downlink and uplink. Orthogonal frequency-division multiple access (OFDMA) and Single

carrier-frequency-division multiple access (SC-FDMA) is used for the downlink and the

uplink, respectively. OFDMA provides high spectral efficiency which is very immune to

interference and reduces computation complexity in the terminal within larger bandwidths.

& Honggui Dengdenghonggui@163.com

1 Changsha, Hunan, China

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Wireless Pers CommunDOI 10.1007/s11277-015-2704-8

LTE is designed to improve the capacity, coverage, and the speed of mobile wireless

networks over the earlier wireless systems. The requirements for 3GPP LTE include the

provision of peak cell data rates up to 100 Mbps in downlink and up to 50 Mbps in uplink

under various mobility and network deployment scenarios [2, 3]. As one of the crucial

aspects in radio resource management functionality, the handover performance becomes

more important, especially for real-time service, since the handover failure rate will

increase with the higher moving velocity. An additional requirement is the uninterrupted

provision of high data rates and call services.

LTE has a very simplified network architecture compared to universal mobile

telecommunications system (UMTS). The LTE network architecture is consisted of three

elements as shown in Fig. 1 [4]: evolved-NodeB (eNodeB), mobile management entity

(MME), and serving gateway (S-GW). The eNodeB performs all radio interface related

functions such as packet scheduling and handover mechanism. MME manages mobility,

user equipment (UE) identity, and security parameters. S-GW is a node that terminates the

interface towards E-UTRAN. This paper focuses on high-speed railway scenario, which

deploys eNodeB consisting of base band unit (BBU) and radio remote unit (RRU) along

the railway line as demonstrated in Fig. 2 [5].

Several studies have evaluated the handover performance or have proposed optimization

methods to improve the traditional handover algorithm in LTE system. In paper [6] and [7], a

soft handover algorithm is presented for TD-LTE system in the high-speed railway spe-

cialized network which has a much better performance comparing with hard handover

algorithm but at the expense of higher implementation complexity. A novel approach to

handover management for LTE femtocells is presented in [8], which runs on the femtocell

base station, does not require any prior knowledge of the architecture of the building in

which it is deployed; thus it is fully consistent with the self-organizing network plug-n-play

requirement. Three well known handover algorithms have been optimized in the LTE system [9].

Fig. 1 LTE system architecture

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And the simulation results show that this optimization outperforms non-optimized algo-

rithms by minimizing the average number of handover. In [10], the author proposes a new

handover strategy between the femtocell and the macrocell for LTE-based networks in

hybrid access mode, which consider some parameters for handovers, including interference,

velocity, RSS and quality of service (QoS) level. Furthermore, there are some new studies

focused on the self-organizing network (SON) and adaptive handover algorithm to increase

the robustness of the system performance [11] [12]. Since the traditional A3 event-based HO

algorithm is mainly designed for the low speed networks (e.g. speed\120 km/h) and the

above researches are also mainly evaluated with a low-speed. These studies aren’t suitable

for the high-speed railway scenario which the link quality may deteriorate sharply and the

wireless channel environment may become unstable with the increase of speed. Therefore, to

overcome the disadvantages of the handover algorithm in LTE high-speed railway networks

we proposed a handover optimization algorithm from statistics, which not only consider

RSRP and RSRQ at the same time but also the rate of cell resources change.

This paper is organized in five different sections. Section 1 is background and related

works of handover algorithm in LTE system. In Sect. 2, the definitions of RSRP, RSSI,

RSRQ and the rate of cell resources change are explained in it. In Sect. 3, the classical

handover algorithm and the novel handover algorithm are shown. Then in Sect. 4, simu-

lation of the proposed algorithm is shown and the results are analyzed. Finally, a con-

clusion is drawn in Sect. 5.

2 Measurement Report in LTE

UE related measurements for the handover are defined in 3GPP specification in [13, 14].

For simplicity in simulation of handover, input measurements are divided into three signals

which are RSRP, RSRQ and the rate of cell resources change. Detail of them will be

explained below.

Fig. 2 eNodeB deployment along the railway line

Handover Optimization Algorithm in LTE High-Speed Railway…

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2.1 Reference Signal Received Power (RSRP)

RSRP is measured for a considered cell as the linear average over the power contribution

of the resource elements that carry cell-specific reference signal within the considered

measurement frequency bandwidth. The cell-specific reference signal can be used for

RSRP determination. RSRP can be calculated from serving cell eNodeB transmit power

(Ps), the path loss value from UE to the serving cell eNodeB (PLue) and additional shadow

fading with a log-normal distribution and a standard deviation of 3 dB (Lfad). Following is

the equation to calculate RSRP.

RSRPs;ue ¼ Ps � PLue � Lfad

2.2 E-UTRA Carrier Received Signal Strength Indicator (RSSI)

RSSI is the total received wideband power observed by the UE from all sources, including

co-channel serving and non-serving cell, adjacent channel interference, thermal noise and

so on. RSSI can be calculated as follows.

RSSI ¼ RSRPs;ue þ RSRPint;noise

2.3 Reference Signal Received Quality (RSRQ)

RSRQ can be calculated by the ration RSRQ = N 9 RSRP/RSSI, where N is the number

of resource block (RB) of the E-UTRA carrier RSSI measurement bandwidth. RSSI

includes thermal noise and interference generated in the target eNodeB, thus RSRQ can be

written as the relation between signal and interference plus noise as follows.

RSRQ ¼ N � RSRP

RSSI

2.4 The Rate of Cell Resources Change

The rate of cell resources change reflects the state of cell resources dynamic change,

because the size of the available resources can’t fully reflect the use of cell resources. If

you select the cell when the available resources and the rate of resources change are both

greater, handover are likely to cause other users to handover fails or call blocking. In order

to reasonably use the cell resources, the smaller rate of cell resource change is selected

when the available resources of cells are little difference, which can increase the stability

of the system and the handover success rate and reduce the blocking probability of the

system.

In this paper the rate of cell resource change is the statistics of resources periodically

change. It can be calculated as follows.

akpre ¼ x� ak þ 1 � xð Þ � ak�1

where aprek represents predictive value of the rate of cell resources change at k time. ak and

ak-1 are the rate of cell resources change at present and at k-1 time, respectively. x is the

weighting factor.

F. Yang et al.

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3 Handover Algorithm

3.1 A3 Handover Algorithm

The A3 handover algorithm [15] in LTE system is a basic but effective handover algorithm

consisting of two variables, handover margin (HOM) and time to trigger (TTT) timer. A

handover margin is a constant variable that represents the threshold for the difference in

RSRP between the serving and the target cell. HOM identifies the most appropriate target

cell when the mobile can camp on. A TTT is required for satisfying HOM condition. The

handover can only be executed after both the criteria of TTT and HOM are met. Figure 3

shows the basic concept of standard handover algorithm in LTE.

Handover is triggered when the triggering condition as following is fulfilled for the

entire TTT time duration followed by the handover command sent from the eNodeB to the

UE.

RSRPT [RSRPS þ HOM

where RSRPT and RSRPS are the RSRP received by a UE from the target cell and the

serving cell, respectively. TTT starts whenever the RSRP difference received by a UE from

the target cell and the serving cell is greater than the specified HOM value. The serving cell

starts observing the incoming consecutive time slots after TTT starts. If in any of the

incoming consecutive time slots the RSRP difference is less than or equal to HOM, the

handover process will be reset, otherwise handover process will be executed which

includes the handover decision and the handover command.

3.2 Proposed Handover Optimization Algorithm

3GPP protocol stipulates that measurement reports constantly satisfy the requirement in

trigger time, that is the concept of TTT. However, how to achieve the trigger delay is not

stipulated in the protocol. Therefore, we can trigger handover from statistics: The UE

continually receives measurement report of physical layer, then through the layer 3 fil-

tering to judge whether the measurement report meets trigger criteria. If satisfied, statistical

Fig. 3 The classical handoveralgorithm

Handover Optimization Algorithm in LTE High-Speed Railway…

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values of satisfied handover are accumulated (N = N ?1), then to judge whether N meets

Nt which the statistical threshold value is set. If N is satisfied, the measurement report is

sent to the eNodeB and handover will be triggered. Otherwise, continue to wait for the

arrival of the next measurement report.

In this paper, on the basis of the traditional handover algorithm, we not only consider

RSRP and RSRQ, but also choose the rate of change of cell resources as one of the selected

factors. At the same time, from the view of statistics we proposed a handover optimization

algorithm. Figure 4 presents a flowchart of the handover optimization algorithm.

Step 1 Define the initial parameters. V is the speed of the train. If V is greater than

120 km/h, trigger the new handover algorithm. Otherwise, the conventional

handover algorithm based on A3 event is used

Step 2 Calculate the value of the criterion function RSRPTi[RSRPS ? HOM, where

i ¼ 1; 2; . . .; n represents the total number of neighboring cells. When the

triggering condition as shown above is fulfilled for the entire TTT time duration,

go to Step 4. Otherwise go to Step 3

Step 3 Calculate the value of the criterion function RSRQTi[Thrs, where Thrs

represents the RSRQ threshold of adjacent cells. If the condition is satisfied, go to

Step 4. Otherwise continue to iterate

Step 4 If N[Nt is satisfied, the cell is added to the cell list. Otherwise continue to

search for the next cell

Step 5 Calculate the rate of neighboring cells resources change. If all neighboring cells

are searched and calculated, the cell of smallest rate of resource change is

selected to trigger handover. Otherwise continue to iterate

4 Simulation Results and Analysis

The simulation work is implemented by Matlab. The network scenario considered assumes

a chain structure with 5 cells (controlled by 5 eNodeBs respectively). The channel model

includes channel bandwidth, carrier frequency and path-loss model. The main system

simulation parameters are shown in Table 1.

UE should deliver its measurement report according to various moving speed, namely,

when UE running in a high speed, its HOM should be set smaller, vice versa. In this way,

UE’s handover action determined by HOM will be triggered strictly according to a same

physical distance in reality environment when UE is in different velocities. To simplify the

simulation, when the velocity of the UE is 0–30 m/s, 30–60 m/s and 60–100 m/s,

respectively, HOM is 6, 4 and 2 dB. The path loss is calculated with the Hata model [16]

and the baseband signal is transmitted through SCME (Spatial Channel Model Extended)

channel, shown as Table 1.

From the simulation results as shown in Figs. 5 and 6, we can conclude that RSRP

doesn’t almost change and RSRQ change obviously when SINR value is increased. If only

consider RSRP, as shown in Fig. 5, RSRP can well reflect the size of the signal strength.

When the channel environment is under relatively good condition, it can be used to make

decision. When the channel environment is bad, even if the signal strength is large, the

noise is also great, which will cause ping-pong handover and greatly deteriorate system

performance. So the algorithm of only considering RSRP is suitable for good channel

environment. However, the high-speed railway environment is very complex. We consider

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Fig. 4 Handover procedure ofoptimization algorithm

Handover Optimization Algorithm in LTE High-Speed Railway…

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both RSRP and RSRQ for harsh channel environment, which the former reflects signal

strength and the latter reflects channel environment.

Since the velocity of the UE is various dynamically from 0 m/s to 100 m/s during the

travel, parameters of handover algorithm should be adjusted accordingly to ensure

0 2 4 6 8 10 12 14 16 18 200

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5RSRP

SINR(dB)

RS

RP

RSRP

Fig. 5 The relationship between RSRP and SINR

Table 1 Parameters ofsimulation

Parameters Values

Channel bandwidth 10 MHz

UE number 1

eNodeB number 5

Height of eNodeB 35 m

Height of UE 3 m

Cell radius 1200 m

Distance between eNodeBs 2000 m

HOM 2 * 6 dB

Cell overlapping region 300 m

Velocity of UE 0 * 100 m/s

Vertical distance between eNodeB and railway 200 m

Carrier frequency 2000 MHz

Path loss model Hata model

eNodeB initial transmitting power 53 dBm

Measurement interval 0.01 s

TTT 180 ms

Nt 3

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0 2 4 6 8 10 12 14 16 18 200

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05RSRQ

SINR(dB)

RS

RQ

RSRQ

Fig. 6 The relationship between RSRQ and SINR

0 10 20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50Handover Number

Velocity of UE(m/s)

hand

over

num

ber

the Novel Handover Algorithm

the A3 Handover Algorithm

Fig. 7 HO number versus velocity of UE for different handover algorithms

Handover Optimization Algorithm in LTE High-Speed Railway…

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handover success rate and satisfying wireless communication quality in train. From the

simulation results as shown in Figs. 7 and 8, compared with the A3 handover algorithm,

the handover number of the new algorithm is greatly reduced and handover success rate is

significantly increased.

Figure 7 shows that the handover number increases with the speed of UE. When the

velocity of the UE is 30, 60, 100 m/s, the handover number of the A3 algorithm and the

handover number of the novel algorithm are 8, 17, 34 and 5, 11, 18, respectively. Thus we

can conclude that the handover number of the new algorithm is reduced compared to the

A3 algorithm. Frequent handover may lead to the interruption of business and deteriorate

QoS of users. By dynamically adjusting handover parameters in different speeds and

simultaneously considering the statistical characteristics to trigger handover, the algorithm

can reduce unnecessary handover by up to 47 %.

Figure 8 shows the handover success rate comparison between the proposed algorithm

and A3 algorithm. The handover success rate decreases when the speed of the UE

increases. In the low speed of the UE that is less than 30 m/s, the handover success rate of

the two algorithms has little difference. For instance, when the speed is 30 m/s, the

handover success rate of the A3 algorithm and the handover success rate of the proposed

algorithm are 91.9 and 92.5 %, respectively. If the speed of the UE is greater than 30 m/s,

the handover success rate of the optimization algorithm starts to be obviously higher than

the A3 algorithm, especially when the speed is higher than 70 m/s. For example, when the

speed is 100 m/s, the handover success rate of the A3 algorithm and the handover success

rate of the novel algorithm are 57.6 and 71.5 %, respectively. In contrast, the improved

algorithm provides 0.5–13.9 % higher success rate than A3 algorithm. This is because we

0 10 20 30 40 50 60 70 80 90 10050

55

60

65

70

75

80

85

90

95

100Handover Success Rate

Velocity of UE(m/s)

hand

over

suc

cess

rat

e(%

)

the Novel Handover Algorithm

the A3 Handover Algorithm

Fig. 8 HO success rate versus velocity of UE for different handover algorithms

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consider not only RSRP and RSRQ but also the rate of change of cell resources, simul-

taneously consider the statistical characteristics to trigger handover.

5 Conclusions

Handover algorithm is one of critical things in mobile communication environment.

Seamless handover can guarantee better QoS even UE be moved very fast by taking a high

speed railway train. This paper proposes a handover optimization algorithm from the view

of statistics to improve handover performance for LTE. The input signals are measured by

not only from two eNodeBs, but are able to receive from more than two eNodeBs. Based

on simulation, the algorithm has the ability to reduce unnecessary handover by up to 47 %

and provides 0.5–13.9 % higher success rate than A3 algorithm. The handover optimiza-

tion algorithm can greatly improve handover performance, especially in high-speed rail-

way environment.

Acknowledgments This work was supported by the Natural Science Foundation of Hunan project ring-resonator-spectroscopic-based detection mechanism and methods of gas pollution, Project No. 14JJ2013 andNatural Science Foundation of Xinjiang project Detection theory and methods of gas pollution, Project No.2013211A035.

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Fang Yang was born in Anhui, China, in 1988. She is a postgraduatestudent at the Institute of Physics and Electronics in Central SouthUniversity. Her research interests deal with wireless communication,source coding and signal processing.

Honggui Deng was born in Hunan, China, in 1965. He is a professorand vice president at the School of Physics and Electronics in CentralSouth University. His research interests include wireless communica-tion, information theory, source coding, and signal processing. He haspublished more than 50 academic papers.

Fangqing Jiang was born in Hunan, China, in 1989. He is a post-graduate student at Institute of Physics and Electronics in CentralSouth University. His research interests include visible communica-tion, information theory, source coding and signal processing.

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Xu Deng received the B.S. degree in electrical engineering fromCentral South University, Changsha, Hunan, in 2009. He is currentlyworking toward the M.S. degree at Carleton Univeristy, Ottawa,Canada. His reserach interestes include model order reduction and 5Gnetwrok.

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