wireless pers commun doi 10.1007/s11277-015...
TRANSCRIPT
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 [email protected]
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.
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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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