Upload
hamien
View
222
Download
2
Embed Size (px)
Citation preview
SON in 4G Mobile Networks
Self-Optimization Techniques for Intelligent Base Stations
Bell Labs Stuttgart
Ulrich Barth
9. Fachtagung des ITG-FA 5.2, Oktober 2010
All Rights Reserved © Alcatel-Lucent 2008, XXXXX
Self- organizing Radio Access Networks
Motivation
Current situation for radio access network management� Deployment and maintenance become more and more complex and costextensive
� Trend to smaller cells, multi-band operation, heterogeneous mobile networks
� High manual intervention for configuration, capacity upgrade or in failure cases required
� High effort required for optimisation of system performance
� Deep system expertise required
� High effort necessary for measurement campaigns (drive tests)
� Different tools for planning, configuration, measurement/KPI acquisition and optimisation involved
increasing effort for network management and optimisation
���� new concepts for simplified network operation required
All Rights Reserved © Alcatel-Lucent 2008, XXXXX
Self- organizing Radio Access Networks
Requirements
Management of radio access networks has to be self-organized in future � Automated configuration, optimization and fault management:
� towards real plug-and-play self-configuration
� continuous up to autonomous self-optimization
� fast self-healing mechanisms
� Paradigm change:
� to put network optimization know how into intelligent self-x algorithms
� to focus network management on high level monitoring and performance tuning
� High performance self-x algorithms required:
� fast convergence
� stable operation
� tuneable according to operator requirements
� managing mutual dependencies between self-x use cases
All Rights Reserved © Alcatel-Lucent 2008, XXXXX
Self-X Architecture
� “NEM less” network management
� Fully autonomous, distributed
RAN optimisation
� Self-x functions in UE and eNB
� measurements, UE location info
� alarms, status reports, KPIs
� distributed self-x algorithms
� Network management in NM OSS
focussed on
� network planning
� alarm and performance monitoring
� high level performance tuning
Vision of fully distributed self-management
eNB
LTE RAN
Network Management
eNB
eNB
self-x
NM OSS
Itf-N
X2-Itf
self-x
self-x
RAN self-optimization
���� performance monitoring
���� KPIs
���� alarms
���� high level network
performance tuning
OSS: Operation Support SystemNEM: Network Element Manager
All Rights Reserved © Alcatel-Lucent 2010 5
Mobility Robustness (Handover Optimization)
All Rights Reserved © Alcatel-Lucent 2010 6
Configuration Parameters for Handover in LTE
LTE handover performance has large impact on system
performance
� Configuration parameters
� Filtered RSRP values
� Handover Margin, i.e. hysteresis between source and target
� Time to trigger (TTT)
� Cell Individual Offset (CIO), add on handover margin
� Target
� High handover success rate
TTT(ms)
Handoverevent
from UEto eNB
FilteredRSRP
[dB]Source Cell
Target Cell
Time
X2 delay
Hyst(dB)
Handover
commandfrom eNBto UE
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
TTT(ms)
Handoverevent
from UEto eNB
FilteredRSRP
[dB]Source Cell
Target Cell
Time
X2 delay
Hyst(dB)
Handover
commandfrom eNBto UE
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
A3 HO event
source cell UEtarget cell
HO command
Normalized HO Rate Vs Residual BLER for ; TTT=0 to 200 ms; 20ms step
0
5
10
15
20
25
30
35
0 1 2 3 4 5 6 7 8 9 10
BLER [%]
No
rmal
ized
HO
Rat
e
BLER: block error rate of HO command � RLF
Normalized HO rate:without slow/fast fading
SON algo: find best trade-off between minimum BLER and minimum HO rate (ping pong)
All Rights Reserved © Alcatel-Lucent 2010 7
SON definition: too late Handover
RLF, as UE still associated to cell A
cell AUE
cell B
TTT(ms)
no uplink communication:no Handover event from UEto eNB A
FilteredRSRP
[dB]
Source Cell A
Target Cell B
Time
Hyst(dB)
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
TTT(ms)
no uplink communication:no Handover event from UEto eNB A
FilteredRSRP
[dB]
Source Cell A
Target Cell B
Time
Hyst(dB)
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
TTT(ms)
Handoverevent
from UEto eNB
FilteredRSRP
[dB]Source Cell A
Target Cell B
Time
X2 delay
Hyst(dB)
Handover
commandfrom eNBto UE
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
TTT(ms)
Handoverevent
from UEto eNB
FilteredRSRP
[dB]Source Cell A
Target Cell B
Time
X2 delay
Hyst(dB)
Handover
commandfrom eNBto UE
Radio LinkFailure (RLF)
threshold
Handover failuredue to RLF
Alternative 2: a failure occurs in the
source cell during the HO procedure
Alternative 1: a failure occurs in the
source cell before the HO was initiated
All Rights Reserved © Alcatel-Lucent 2010 8
Messages and algorithm for “too late” handover
SON Algorithm- sufficient measurements for decision of HO problem ?
- analysis of measurements and other data (own eNB and other eNB)
- modify HO parameters
- for all UE speed classes
- mobility state dependent
- start new measurement cycle
Update “Reestablishment”Statistics (PCI#A)
RLF INDICATION
UE eNB A eNB B
RLF
Update “Reestablishment”Statistics (PCI#A)
RRC Connection Reestablishment Request including PCI#A
UE connected to eNB A
Measurement Report (eNB B) HO Request
HO Request AcknowledgeRRC Connection Reconfiguration
RRC Connection Reestablishment
RRCConnectionReestablishmentComplete
UE Context Release
Update “Reestablishment”Statistics (PCI#A)
RLF INDICATION
UE eNB A eNB B
RLF
Update “Reestablishment”Statistics (PCI#A)
RRC Connection Reestablishment Request including PCI#A
UE connected to eNB A
Measurement Report (eNB B) HO Request
HO Request AcknowledgeRRC Connection Reconfiguration
RRC Connection Reestablishment
RRCConnectionReestablishmentComplete
UE Context Release
All Rights Reserved © Alcatel-Lucent 2010 9
SON based HO optimization
Characteristics of simulation scenario “Frankfurt”
Available ray-tracing data
� Input data characteristics:
� 16 real world antenna locations in the city of
Frankfurt
� Tri-sectorized configuration (i.e. 48 cells)
� individual antenna type information, heights
and beam directions
� topographic map of region (4000m ×××× 4000m)
� Output data characteristics:
� Integer type pathloss data
per 10m ×××× 10m grid point per antenna
� Range: -88dB to -214dB
All Rights Reserved © Alcatel-Lucent 2010 10
SON based HO optimization
Algorithm test in simulation
� Event based simulation of HO
events, one algorithm instance per
simulated cell
� Interference level: 50% load
� mobile speed: 3, 30, 120 km/h
– 16 mobiles active per speed
– moving along given roads
– at crossroads, mobiles choose randomly next road
� same initial mobility parameters at
simulation start
All Rights Reserved © Alcatel-Lucent 2010 11
SON based HO optimization
HO success rate window controller
Simulation results (Simulated network operation: 3 days = 259200 s)
� A quick convergence of the cell global
parameters can be observed for frequently
visited cells (i.e. on many HO events)
� Choosing initial HO parameters above optimal
settings causes a HO success rate below given
limit
� The algorithm instance tunes the parameters
towards earlier decision for HO, achieving an
improvement of the cell total HO success rate
Gained Key Performance Indicators for TRX #31
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,0
0 28800 57600 86400 115200 144000 172800 201600 230400 259200
Simulated Time [s]
HO
Su
cces
s R
ate
[%]
0
3
6
9
12
15
18
21
24
27
30
Mea
n M
ob
ile R
estin
g T
ime
[s]
HO Success Rate
Mean Resting Time
Mobility Parameters for TRX #31
0,00
0,25
0,50
0,75
1,00
1,25
1,50
1,75
2,00
2,25
2,50
0 28800 57600 86400 115200 144000 172800 201600 230400 259200
Simulated Time [s]
Hys
tere
sis
[dB
]
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
Tim
e-T
o-T
rig
ger
[s]
Hysteresis
Time-To-Trigger
All Rights Reserved © Alcatel-Lucent 2010 12
Coverage and Capacity Optimisation
All Rights Reserved © Alcatel-Lucent 2010 13
Antenna Tilt Optimization
Optimization goals:
� sector coverage
� sector capacity
� based on downlink performance metric bits/sec/Hz
Approach:
� Distributed optimization:
� optimization of single cells together with closest neighbors
� targeting global optimum
� by adjusting the antenna tilt for each sector individually
� distributed approach, co-operating eNB and neighbours
All Rights Reserved © Alcatel-Lucent 2010 14
Antenna Tilt Optimization
Best Serving Sectors, Displaced Site Locations
� sites are displaced but playground borders
are kept fixed, with wrap around
� slow fading with area correlated
shadowing is invisible in best server plots
due to the equal attenuation of all sectors
at a certain point, independent of the
direction of the signalafter optimization
All Rights Reserved © Alcatel-Lucent 2010 15
Antenna Tilt Optimization
Metrics
Cell wide optimization approach
)1()()()1()(max
min5 dGGTGpBWGTBWM tf
G
Gpercentiletf ⋅⋅−+⋅⋅= ∫−
M: performance metricG: GeometryB: bandwidthp(G): probabilityTtf: throughput per MCSW: weighting factor
)( 51 percentiletf GTBM −⋅=coverage metric:
dGGTGpBM tf
G
G)()(
max
min2 ⋅= ∫
capacity metric:
� weighted coverage/capacity metric:
target: coverage target: capacity
next charts:
concrete simulation studies:operator tunable weighting
parameter W = 0.91
All Rights Reserved © Alcatel-Lucent 2010 16
Antenna Tilt Optimization
Performance Gains of Sector Average Performance and Sector Edge Perf.
+7%
+44%
gain of utility +21% of 160 → 193
Results:
� significant gains with respect
to equal tilts
� capacity performance +7%
� coverage performance +44%
Optimized Performance (red) [%]
Reference Performance 15°(blue) [100%]
Trade-off given by Utility Metric (green):- weight factor W between coverage and capacity
defines the slope of this line
All Rights Reserved © Alcatel-Lucent 2010 17
Antenna Tilt Optimization
Convergence Speed and Gains of Performance Metric
two reference curves for all sectors with 14 and 15 degrees equal downtilt
with optimized tilts
one cyle through all sectors equals 57 steps
Results:
� promising,
playground wide
improvement
� non-oscillating
� fast convergence
All Rights Reserved © Alcatel-Lucent 2010 18
SON challenges
All Rights Reserved © Alcatel-Lucent 2010 19
SON use case a
optimization algorithm a
Mutual interactions of SON optimization algorithms
SON use case b
KPI/measurement 1
KPI/measurement 2
KPI/ measurement 3
target functionmetric a
target functionmetric b
Parameter III
Parameter I
Parameter II
Radio System Radio System
Coupling by same control parameter: One parameter is modified bydifferent SON algorithms
Handover <-> Load balancing
Mutual impact on optimization target:One metric is influenced by parameters of different SON algorithms
Interference Coordination <-> Loadbalancing
optimization algorithm b
eNB
• different coupling mechanisms, different coupling strength
⇒ solution required to manage SON use case interworking !
All Rights Reserved © Alcatel-Lucent 2010 20
www.alcatel-lucent.comwww.alcatel-lucent.com