A Network Controlled Handover Mechanism and its

Optimization in LTE Heterogeneous Networks

Zhou Guohua, Peter Legg, and Gao Hui

Huawei Technologies Sweden

Kista, Sweden

{guohua.zhou, peter.legg, gaohui}@huawei.com

Abstract—Mobility Robustness Optimization (MRO) is

proposed in Long Term Evolution (LTE) networks to improve

handover performance by 3GPP (3rd Generation Partnership

Project). However, conventional MRO algorithms which tune cell

(-pair) level handover parameters that control the generation of

measurement reports have restricted gain because of the uneven

interference distribution within the handover region in the

spatial domain, and also the interference variation in time

domain when the cell load changes. Mobility optimization is even

more challenging in heterogeneous networks (HetNet) due to the

more complex channel and load conditions there. In this paper,

we propose a network controlled handover mechanism to solve

these problems. The mechanism exploits measurement reports to

identify the best target cell and channel quality measurements to

realize a handover specific timing decision. System simulations

show the new mechanism can be configured by a MRO algorithm

to overcome many of the handover challenges in LTE HetNet

Index Terms—LTE, handover, MRO, SON, Heterogeneous

Networks.

I. INTRODUCTION

The Long Term Evolution (LTE) heterogeneous network

(HetNet) is a promising way-forward to meet the anticipated

wireless broadband capacity challenge [1]. A large number of

nodes will be deployed and these form different coverage

layers. Compared to a traditional macro-only deployment, it’s

more challenging from the handover (HO) point of view

because of a much higher handover frequency and more

complex handover situations. In 3GPP from Release 9 the Self-

Optimizing Network (SON) concept was introduced in the

LTE system to save the Operating Expenditure (OPEX) and

improve the network performance [2]. Within SON, the MRO

use case is tasked to optimize the mobility performance

automatically by self-optimizing the handover parameters such

as handover offset, time to trigger (TTT) and filter coefficient

K to optimize the trigger time of a handover. These attributes

govern the generation of a measurement report by the UE: in a

simple handover algorithm, that we term classic handover, the

handover is initiated as soon as a report is received by the cell

serving it (and the handover is directed to the cell identified in

the measurement report [3]). The offset can be set per cell pair,

TTT and K are usually set per cell. However, the handover

performance within a cell pair depends on many external

factors that do not influence the measurement report generation

such as the interference and shadowing distribution within the

handover region, cell load and UE speed [3][4]. A MRO

algorithm that tunes the handover parameters can react to the

whole cell pair handover performance only after assessing

many handovers over a period of time and cannot achieve the

real-time optimum timing for each handover individually. In

order to ensure the cell pair level failure rate target,

conservative parameters are needed to save the handovers with

highest risk of failure; these parameters could be too

conservative for UEs crossing the cell pair boundary at other

locations or points in time, thereby causing unnecessary

handovers.

Moving from macro deployments to HetNet, the cell load

variation could be even greater because there are fewer users in

each small cell. In this new network layout, the mobility

performance is challenging both for the increased handover

frequency and the raised risk of radio link failure (RLF) [5],

particularly for the handovers from a small cell to a macro cell.

The study in [6] shows handover specific trigger as one of

resource management approaches can also give significant end

user experience and system capacity gain, but no mobility

performance was assessed. In this paper we propose a

handover specific LTE triggering mechanism we term network

controlled handover (NCH), to evaluate the mobility

performance of the mechanism and further its parameter tuning

sensitivity. The performance gains over a classic handover are

demonstrated by system simulation in HetNet. Furthermore,

the tunability of NCH, which could be exploited by a MRO

algorithm, is demonstrated. The rest of the paper is organized

as follows. Section II introduces the models and procedures for

both the classic handover mechanism in LTE as the reference

case, and the NCH mechanism; MRO in each case is also

introduced. Section III gives two scenarios where the

traditional MRO based on classic handover mechanism is

expected to have difficulties. Section IV presents a comparison

of the algorithms by system simulation, and considers tuning of

NCH. Conclusions are drawn in Section V.

II. HANDOVER AND MRO METHODS

A. Classic Handover and MRO in LTE

A successful handover procedure in LTE includes “HO

trigger” and “HO execution” parts. The classic “HO trigger”

(Fig.1), includes:

• Measurement report trigger in UE side;

• Measurement report received in the serving cell and a

HO triggered in the source cell at the same time.

978-1-4673-5939-9/13/$31.00 ©2013 IEEE978-1-4673-5939-9/13/$31.00 ©2013 IEEE

2013 IEEE Wireless Communications and Networking Conference (WCNC): NETWORKS2013 IEEE Wireless Communications and Networking Conference (WCNC): NETWORKS

1915

The handover trigger in the classic mechanism depends on

the measurement report trigger timing in UE and the delivery

delay of the measurement report. The A3 event measurement

report trigger in UE side depends on two conditions for [7]:

1. Reference Signal Received Power (RSRP) of target

cell j of UE k is larger than serving cell i RSRP

measured by k.

������, �� – ������, �� � �������, �� (1)

2. The condition 1 remains true during the TTT time.

The HO execution includes the following steps:

• Communication over the X2 between the source and

target eNB (HO preparation);

• Delivery of the HO Command RRC message from the

source cell to the UE;

• Successful random access and delivery of a HO

Confirm RRC message to the target cell.

For the traditional MRO algorithms based on the classic

handover mechanism, the HO trigger time is optimized by

adjusting the UE measurement report trigger time, through

adjustment of the HO offset, TTT or filter coefficient K.

B. Proposed Network Controlled Handover Mechanism

The NCH algorithm only differs in the handover trigger

mechanism compared to the classic handover. The steps from

HO preparation onwards are kept unchanged.

The new HO trigger mechanism in NCH comprises the

steps as below (Fig. 2):

1. The eNB configures a “triggered periodic” A3 event

with a small offset (say 1dB) and short TTT (say 0ms);

2. Once a report from UE k has been received, the source

eNB monitors the downlink SINR of UE k in the

source cell;

3. Once the SINR passes below a threshold value the

handover preparation is triggered for the strongest

target cell in the last measurement report.

Note NCH uses separate signaling mechanisms to identify

the target cell and to trigger the handover. The eNB can use

downlink measurements to reflect the downlink channel quality.

RSRQ is available but only reflects the SINR accurately under

full and equal downlink transmission power across the

frequency band. One possible approach is to use the filtered

wideband channel quality indication (CQI) based on UE CQI

reports. The MRO algorithm based on NCH can optimize HO

trigger parameters such as CQI threshold based on the statistics

of the HO performance.

III. EVALUATION SCENARIOS

In this paper, we evaluate the benefits of the proposed NCH

algorithm in two scenarios. One is an uneven interference

distribution within handover region; another is a time varying

downlink cell load.

A. Scenario A: uneven interference distribution

Classic handover is triggered by RSRP difference between

serving cell and one target cell (the strongest), but in many

cases other cells can be present and cause significant

interference to the HO Command transmission. In other words

the RSRP difference does not always reflect the SINR for the

HO Command. For example, in Fig.3 for the pico to macro

handovers within cell pair {p, m1} for UE1 and UE2, although

at the HO trigger point the RSRP difference between the target

and source should be similar, UE1 will suffer worse downlink

SINR for HO command transmission than UE2 because of the

existence of neighbor cells m2 and m3.

For the traditional MRO algorithm based on the classic

handover mechanism, in order to maintain the failure rate

target for cell pair {p, m1}, the conservative parameter must be

taken for all the handovers within this cell pair, even for those

handovers with lower interference such as for UE2.

Furthermore, these conservative parameters will probably

cause unnecessary handovers or ping-pongs when small

pockets of coverage exist because of shadow fading.

Fig. 3. Uneven interference distribution

Fig. 2. Proposed handover trigger mechanism

Fig. 1. Classic handover trigger mechanism in LTE

1916

B. Scenario B: varying downlink cell load

The downlink cell load from neighbor cells impacts the

downlink UE SINR greatly [4]. In HetNet because of the

limited number of users in small cells, the UE existence or not

can cause large cell load changes within a short time period.

When MRO adjustment period is longer than load changing

period, the traditional MRO averages over all handovers, but

needs conservative parameter settings such that handovers with

the highest risk of failure (high interference area or high load)

do not suffer excessive failures.

While in the NCH handover mechanism, the cell load

impact to the SINR in serving cell is already reflected in the

SINR. So we can expect the NCH to save handover failures

when target cell load is high and save handover numbers when

target cell load goes low.

In this scenario, the downlink cell load of each cell i

changes according to the function as defined below.

� ����, �� � ��1 � sin ����� � � · ��� !�/2! · 100% (2)

Where ���� is randomly selected from &0, 2'( for each

cell �; � is the time in seconds.

IV. SIMULATION RESULTS

In our LTE HetNet simulator, 57 pico cells are randomly

deployed within a regular 57 cell macro network with a

minimum pico to macro distance of 75m and minimum pico to

pico distance of 40m. A circular area was selected to test the

two handover mechanisms as depicted in Fig. 4. All the 456

moving UEs are randomly distributed between the two circles,

and move in a circular path with a randomly selected initial

direction (clockwise or anticlockwise).

In order to model the uplink interference, 57 stationary UEs

with uplink full buffer traffic are uniformly distributed within

the whole network area, 1 UE/macro cell on average, while

downlink interference is implicitly generated by setting

transmission power on a number of PRBs selected randomly

given the specific load. Simulation assumptions are given in

Table II.

In this section, we compared two handover mechanism

cases in the typical scenarios listed in Section III.

(1) Baseline case: MRO adjusts classic handover

parameters. Since MRO can adjust offset, TTT and

K to balance HO reliability and HO frequency,

here we fixed K=4 and used four sets of {offset,

TTT} combinations in Table I to show the HO

performance during MRO adjustment.

(2) NCH case: MRO adjusts NCH parameters such as

the CQI threshold, UE measurement reporting

offset and reporting interval to balance HO

reliability and HO frequency. Here we fixed the

reporting offset as 0dB and interval as 240ms, and

use four SINR thresholds in Table I to show the

HO performance variation from MRO adjustment.

TABLE II. SIMULATION ASSUMPTION

Feature Implementation

Network

topography

Macro cell:

Hexagonal grid of 19x3=57 cells

Pico cell: 1 pico/macro cell

Wrap-around included

Inter-site distance 350m in macro

Bandwidth 5MHz FDD at 2.6 GHz

BS power Macro cell: 43dBm

Pico cell: 27dBm

Antenna patterns Macro cell: 3D model as specified in

[1], Table A.2.1.1.2-2

Pico cell: Omni, as is specified in [1],

Table A.2.1.1.2-3

Channel model 6 paths, Typical Urban (TU)

Shadowing Log-normal Shadowing Mean 0dB,

Standard deviation: macro 8dB; pico

10dB

Propagation

model

Macro eNB to UE:

L= 130.5+37.6log10(R)

Pico eNB to UE;

L= 143.1+36.7log10(R), R in km

UE speed 10m/s; 1m/s

Physical layer EESM, Chase combined HARQ

DL/UL: 1x2 MRC, PRACH with

power ramping

L1 quality model Uses a measure of SIR of the cell

specific reference symbol

RLF detection by

Layer 1 of UE

T310=1s, N310=1, N311=1

Qin=-4.8dB; Qout=-7.2dB

L2 Full MAC, RLC without segmentation

L3 All RRC signaling explicitly modeled

TABLE I. MOBILITY PARAMETERS

Baseline case:

{offset (dB), TTT (s)}

{1, 32}; {2, 64};

{3, 128}; {4, 256}

NCH case: CQI

threshold (dB)

Macro to macro: [0.5, 1, 1.5, 2];

Pico to macro: [1.5, 2, 2.5, 3]

Fig. 4. Cell and UE distributions

1917

A. Performance in Scenario A

The simulation results show during the MRO procedure,

NCH mechanism always has much better HO performance

than classic handover mechanism. For both macro to macro

handover and pico to macro handover, the NCH case always

has a curve to the top-left side of the baseline one, which

means it can achieve lower failure rate and higher MTBH

(lower HO frequency).

To quantify the results, we take the HO failure rate target as

1%. For the macro to macro HOs, the selected NCH operating

point in Fig. 5 with failure rate below the failure rate target

(marked with black circle) shows an increase of the MTBH by

63%. While from the failure rate point of view with the same

MTBH=26s, NCH can reduce the failure rate from 2.5% to

0.4%, saving 85% of the failures.

For the pico to macro HOs, for the selected NCH operating

point (marked with black circle) in Fig. 6 can increase the

MTBH by 170% within the failure rate target.

B. Performance in Scenario B

In the downlink load varying HetNet scenario, it’s hard for

the cell pair level HO parameters to adapt to a quick load

change. However, NCH can adapt implicitly to the real load

change and decide each HO trigger time on demand, which

will avoid a lot of unnecessary handovers.

We can see NCH benefit is larger in this scenario because

NCH can solve the downlink SINR distribution difference both

in spatial domain and time domain that exists in this scenario.

For the macro to macro HOs, the selected operating point

result (marked with black circle) in Fig. 7 gives a 78% MTBH

gain within 1% failure rate target; and achieves very low

failure rate.

It's even beneficial for NCH to save pico-macro handover

numbers. That's because NCH can avoid a lot of unnecessary

macro to pico handovers when the pico load is low, which

cannot be reflected by RSRP difference of target and source

cells in classic HO mechanism. That's why in Fig. 8 the NCH

shows about 400% MTBH gain for the selected result of MRO.

C. MRO tuning NCH parameters

The MRO algorithm can tune the CQI threshold (Fig. 9)

and the A3 reporting offset (Fig. 10) to achieve better HO

success rate (HSR) and ping-pong rate (PPR) tradeoff. Here the

network wide parameter setting is used and also network wide

performance of all kinds of handovers (macro to macro, macro

to pico, pico to macro and pico to pico) is shown. CQI

threshold determines the trigger time and the offset determines

the acquisition of the target cell information. Larger offset

values will delay the HO for the lack of target information. Fig.

10 shows this trend that HO reliability drops with increasing

offset. Further, the HO ping-pong rate has the opposite trend

with the HO reliability, as expected. Note, the NCH

Fig. 8. Pico to macro HO performance for scenario B

Fig. 7. Macro to macro HO performance for scenario B

Fig. 5. Macro to macro HO performance for scenario A

Fig. 6. Pico to macro HO performance for scenario A

Failure rate gain

MTBH gain

MTBH gain

Failure rate gain

MTBH gain

1918

performance is speed dependent, a lower offset is required at

higher speed for the same failure rate. Similarly the failure rate

for a fixed CQI threshold demonstrates a speed dependency –

whilst UEs of different speeds can experience handover

triggers at the same SINR level, the SINR for the faster UEs

drops more quickly and this increases the probability of failure.

Thus MRO for NCH should tune the CQI threshold and report

offset to meet the failure rate requirements for the faster UEs.

As an example, we see in Fig. 9 the UE speeds of 5m/s and

10m/s require corresponding CQI thresholds of 0.5dB and

1.5dB to achieve the same HO success rate of 99.5%, with 1dB

CQI threshold difference; and in Fig. 10 they require

corresponding offsets of 5dB and 2dB to achieve the same HO

success rate of 99.0%, with 3dB offset difference. However,

the measurement reporting interval has very little impact to the

HO performance (Fig. 11). Although the trend is longer

reporting interval gives lower HO reliability, the degradation of

HO performance by longer reporting interval is very small, and

in the reality it is acceptable to use longer intervals to save the

reporting number over the air interface.

V. CONCLUSIONS AND FUTURE WORK

In this paper, we presented a new handover trigger

mechanism called network controlled handover, and its

optimization by parameter tuning. It can trigger each handover

on demand, which improves the tradeoff relationship of

handover reliability and handover frequency. The algorithm

performance was evaluated in two typical scenarios (uneven

interference, variable load) where the traditional cell-pair

specific parameter tuning by MRO has limitations. The results

show dramatic benefits achieved in both scenarios, when

compared to a baseline MRO algorithm with a classic handover

mechanism.

Further evaluation revealed that NCH shows a strong

sensitivity to both CQI threshold and A3 reporting offset, but

weak sensitivity to measurement reporting interval. A MRO

algorithm therefore has the scope to further improve the

handover reliability and reduce the number of measurement

reports. Finally, the NCH performance is UE speed dependent,

so a MRO algorithm able to rapidly adjust to changes in the

UE speed distribution, such as [4], is desirable.

REFERENCES

[1] 3GPP TR 36.814, “Further advancements for E-UTRA physical

layer aspects (Release 9),” V9.0.0, March 2011.

[2] 3GPP TS 36.300, “E-UTRA and E-UTRAN; overall description;

stage 2 (Release 9),” V9.3.0, December 2011.

[3] P. Legg, G. Hui, and J. Johansson, “A simulation study of LTE

intra-frequency handover performance,” 2010 IEEE Vehicular

Technology Conference Fall (VTC 2010-Fall).

[4] G. Hui and P. Legg, “Soft metric assisted mobility robustness

optimization in LTE networks,” International Symposium on

Wireless Communication Systems, ISWCS 2012.

[5] S. Barbera, P. Michaelsen, M. Säily, and K. Pedersen “Mobility

performance of LTE co-channel deployment of macro and pico

cells,” IEEE WCNC 2012.

[6] D. Triantafyllopoulou, N. Passos, A. Kaloxylos, and L. Merakos,

“Coordinated handover initiation and cross-layer adaptation for

mobile multimedia systems,” IEEE Transactions on Multimedia,

11(6), October 2009, pp. 1131-1139.

[7] 3GPP TS 36.331, “E-UTRA Radio Resource Control (RRC);

Protocol specification (Release 9)”, v9.2.0, March 2010.

Fig. 11. HO performance for report interval tuning

Fig. 10. HO performance for report offset tuning

Fig. 9. HO performance for CQI threshold tuning

1dB difference

3dB difference

1919