cell outage management in lte networks - fp7 … 2100... · cell outage management in lte networks...

FP7 ICT-SOCRATES

Cell Outage Management in LTE Networks

COST 2100 TD(09) 941

M. Amirijoo (Ericsson), L. Jorguseski (TNO ICT),

T. Kürner (TU Braunschweig), R. Litjens (TNO ICT),

M. Neuland (TU Braunschweig), L. C. Schmelz (Nokia Siemens Networks),

U. Türke (Atesio)

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  Introduction   Components in cell outage management   First Results on the Controllability Study   Concept of X-Map-Estimation   Future work

Outline

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  The first release of the 3GPP Long Term Evolution (LTE) standard has been finalized

  Operators require significant reduction of manual network management for LTE

  Introduction of self-organisation functionalities in LTE –  Reduces manual network management –  Enhances network performance

  One aspect that benefits from self-organization is cell outage management (COM), which consists of:

–  Cell outage detection –  Cell outage compensation

Introduction

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  Reasons for outages, e.g.: –  hardware and software failures, –  external failures such as power supply or network connectivity

  Outages –  may not be detected for hours or even days –  may require manual analysis and unplanned site visits

  Outage detection function must timely inform the operator about the occurrence and the cause of an outage

  Automatic compensation actions are triggered to alleviate performance degradation

Introduction

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Components of Cell Outage Management

MeasurementsDetec-on

Compensa-on

Operatorpolicy:Coverage,QoS

Controlparameters

Cov.mapes-ma-on

Simula-ontools Scenarios Assessmentcriteria

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First Results on Controllability Study

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Simulator

  Monte-Carlo based static simulator will be used for cell outage compensation due to

–  Simpler modeling and –  Faster execution time

  At each iteration an eNodeB (sector):

–  Samples/gathers performance –  Updates radio parameters

  The time between two iterations is assumed to be in the order of minutes or tens of minutes

–  Small correlation between the samples

–  Correlation is ignored

UEgenera-on

SimulatePHY,RRMetc

SamplePerformance

Compensa-onAlgorithm

Finalsnapshot?

NoRo

undofalgorithm

itera-

on

Pathloss(G‐matrix)

CellSelec-on

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Scenarios

  Data traffic characterised by requested data rate DL = 1 Mbps and UL = 250 kbps

  Quality/Coverage targets –  10th-% DL Throughput > 256 kbps –  10th-% UL Throughput > 128 kbps

  Loads –  High load: load such that coverage/quality targets are satisfied (46 UEs/cell) –  Medium load: 50 % of high load (23 UEs/cell) –  Low load: 1 UE/cell

  Capacity driven layout –  ISD = 500 m –  Antenna downtilt = 15º –  Consider high load, medium load, low load

  Coverage driven layout –  ISD = such that coverage/quality targets are satisfied => ISD = 2450 m –  Antenna downtilt = 5º –  Consider low load

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Definitions

  Assessed region = first and second tier of sectors surrounding outage site

  Cell grouping: –  Group 1 = Blue group –  Group 2 = Yellow group –  Affected = 1st and 2nd tier of sectors

and outage cells

  Considered metrics: –  Coverage (RS&DL&UL) –  Quality: smallest 10-percentile DL & UL cell throughput

  Control parameters: –  Reference signal (RS) power –  Tilt –  UL power control parameter P0 (target received power)

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Coverage Driven Layout – RS Power

Need to increase the RS power to enhance coverage

Group 1 RS power (ratio of nominal power )/ dB Group 1 RS power (ratio of nominal power )/ dB

Gro

up 2

RS

pow

er (r

atio

of n

omin

al p

ower

)/ d

B

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Coverage Driven Layout – RS Power

Need to decrease the RS power to enhance UL quality

Group 1 RS power (ratio of nominal power )/ dB

Gro

up 2

RS

pow

er (r

atio

of n

omin

al p

ower

)/ d

B

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Capacity Driven Layout – High Load

Uptiling improves coverage (nominal tilt = 15 degrees)

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Capacity Driven Layout – High Load

Uptiling decreases DL and UL quality

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Overall Summary of Simulation Results

Impact on coverage

Impact on DL quality

Impact on UL quality

Capacity-driven, high load

87.5-94.5% 75-94% 86-99%

80-185 kbps 110-225 kbps 70-220 kbps

55-105 kbps 10-100 kbps 15-110 kbps

Capacity-driven, medium load

87.5-94.5% 81-98% 86-99%

170-340 kbps 210-410 kbps 125-400 kbps

160-250 kbps 50-250 kbps 60-250 kbps

Capacity-driven, low load

91.5-94.5% 75-99% 86-99%

Not affected Not affected Not affected


Coverage-driven, low load

92.5-96.5% 55-96% 82-96%


120-210 kbps 170-250 kbps 80-200 kbps

Control parameter = RS power, P0, Tilt

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Conclusion

  RS power, P0 and tilt have impact on coverage and DL/UL quality –  Degree of impact depends on network layout and load

  Coverage –  Tilt has highest impact –  Impact of P0 depends on load –  RS power has lowest impact

  DL quality –  All parameters have impact for capacity driven and high/medium load

  UL quality –  All parameters have impact for capacity driven and high/medium load –  P0 also impacts coverage driven layout (low load)

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Scenario

  Hexagonal grid, 19 sites with 3 sectors each   eNodeB height = 32 m, UE height = 1.5 m   BW = 10 MHz   Max BS power = 46 dBm, Max UE power = 25 dBm  RS power ~ 10% Ptotal   Noise spectral density:

–  DL N0 ≈ -199 dBW/Hz –  UL -195 dBW/Hz

  Pathloss = 128.1 + 37.6 log10(r) [3GPP@2GHz]   Shadowing STD = 8dB   Shadowing correlation = 0.5 (sites), 1 (sectors)   Decorrelation distance = ISD / 15;   Minimum coupling loss = 75 dB;   3GPP 3D antenna model

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Concept of X-Map Estimation

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WWW.FP7-SOCRATES.EU Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

  Objective –  To automatically derive X-maps based on UE measurements and other sources

of information requiring minimal human effort

  Main principle –  Connect UE event/measurements with estimated position –  Gather UE reports to build map relating geo reference data and metric of interest

  X-map can show, e.g., –  Coverage related entities, e.g., pathloss, RSRP –  Interference –  End user perception (e.g. voice quality, throughput) –  HO performance (success ratio, drop ratio)

Concept of X-Map-Estimation

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Concept of X-Map-Estimation cont.

  A UE delivers a measurement entitiy, e.g. –  Reference Signal Received Power (RSRP) –  CQI

  UE position information is essential in order to derive X-maps

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...

Implementaion of X-Map-Estimation

UE source 1 UE source n

Planning tool

X-Map Est

Measurement entity, Position estimate, Confidence, (RAT)

X-Map

Initial condition

Localization & Measurement Manager

Measurement entity, (Position)

Prediction, Confidence

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Accuracy of X-Map Estimation

  Confidence of measurement depends on –  Positioning accuracy –  Measurement accuracy

  Positioning accuracy is a function of, e.g.: –  Radio environment (urban, suburban, indoor, outdoor) –  Number of measured RBSs –  Dynamic range of UE –  Positioning technique

  SOCRATES –  is not interested in developing positioning techniques –  assumes that proper positioning techniques are in place –  is interested on good position error models

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  For LTE three different localisation methods are planned –  GPS –  Observed Time Difference of Arrival (OTDOA) –  Enhanced cell ID positioning methods

  Model for the position error based on the Cramér-Rao lower bound

  This model is based on the –  Geometry of eNodeBs / satellites and the UE –  Number of measured signals –  Standard deviation of the measurement error

(for GPS: 33.3 ns)

  In the following preliminary results for GPS and OTDOA are shown

Position Error Modelling

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  Small realistic scenario of 1.5 km x 1.5 km in Braunschweig

  Static and mobile users based on a mobility model

  Network information available

  Realistic path loss information derived from a prediction model

  Satellite orbit for a specific date and time

Simulation Scenario

Source: Google Earth 5.0

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Position Error Modelling - GPS

  Application of Ray-Tracing to determine LOS Satellite-MS

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  Direct path between UE and satellite satellite is visible


number of visible satellites

hist

ogra

m

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  At the moment successive positions are uncorrelated   Next step: applying some kind of filter to get a "flat" route


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  Mean error: 7.4 m   Standard deviation: 4.9 m


position error in m

dist

ribut

ion

func

tion

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Poistion Error Modelling - OTDOA

inter-site distance in km

hist

ogra

m

  Statistics on inter-site distance in simulation scenario

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Position Error Modelling - OTDOA

σ = 38.9 ns 5 7 10 15 20 30

mean error 14.9 8.9 3.8 4.5 3.9 3.1

standard deviation 11.4 5.8 6.0 2.8 2.4 1.9

position error in m

dist

ribut

ion

func

tion

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  Finalise the position error modelling –  Applying filter to get a flat route –  Applying OTDOA in case of no GPS position –  Determining number of measured eNodeBs based on path loss and SINR –  Determining standard deviation of measurement error based on SINR

  Modelling UE measurement accuracy

  Applying X-Map-Estimation to SON use cases

  Determining the required accuracy for SON

Next Steps in Position Error Modelling

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  Finalise the position error modelling –  Applying filter to get a flat route –  Applying OTDOA in case of no GPS position –  Determining number of measured eNodeBs based on path loss and SINR –  Determining standard deviation of measurement error based on SINR

  Modelling UE measurement accuracy

  Applying X-Map-Estimation to SON use cases

  Determining the required accuracy for SON

Next Steps in Position Error Modelling

?

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M. Amirijoo (Ericsson), [email protected]

L. Jorguseski (TNO ICT), [email protected]

T. Kürner (TU Braunschweig), [email protected] (presenting author)

R. Litjens (TNO ICT), [email protected]

M. Neuland (TU Braunschweig), [email protected]

L. C. Schmelz (Nokia Siemens Networks), [email protected]

U. Türke (Atesio), [email protected]

Contact

Thank you very much for

your attention

FP7 ICT-SOCRATES

cell outage management in lte networks - fp7 … 2100... · cell outage management in lte networks...

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