[ieee 2013 ieee wireless communications and networking conference (wcnc) - shanghai, shanghai, china...
TRANSCRIPT
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
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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
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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
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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
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[4] G. Hui and P. Legg, “Soft metric assisted mobility robustness
optimization in LTE networks,” International Symposium on
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[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,
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[7] 3GPP TS 36.331, “E-UTRA Radio Resource Control (RRC);
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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
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