Cell Outage Management in LTE Networks COST 2100 TD(09) 941 M. - - PowerPoint PPT Presentation

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Cell Outage Management in LTE Networks COST 2100 TD(09) 941 M. - - PowerPoint PPT Presentation

Dec 21, 2022 134 likes •477 views

FP7 ICT-SOCRATES Cell Outage Management in LTE Networks COST 2100 TD(09) 941 M. Amirijoo (Ericsson), L. Jorguseski (TNO ICT), T. Krner (TU Braunschweig), R. Litjens (TNO ICT), M. Neuland (TU Braunschweig), L. C. Schmelz (Nokia Siemens

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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
  • ccurrence 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 Measurements
 Detec-on
 Compensa-on
 Operator
policy:
 Coverage,
QoS
 Control
 parameters
 Cov.
map
 es-ma-on
 Simula-on
tools
 Scenarios
 Assessment
criteria


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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

UE
genera-on
 Simulate
PHY,
RRM
etc
 Sample
Performance
 Compensa-on
Algorithm
 Final

 snapshot?
 No
 Round
of
algorithm
itera-on
 Pathloss
(G‐matrix)
 Cell
Selec-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 Group 2 RS power (ratio of nominal power )/ dB

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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 Group 2 RS power (ratio of nominal power )/ dB

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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 Not affected Not affected Not affected Coverage-driven, low load 92.5-96.5% 55-96% 82-96% Not affected Not affected Not affected 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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FP7 ICT-SOCRATES

Concept of X-Map Estimation

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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • Objective

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

  • f 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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

...

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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • 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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • 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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

Position Error Modelling - GPS

  • Application of Ray-Tracing to determine LOS Satellite-MS
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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • Direct path between UE and satellite  satellite is visible

Position Error Modelling - GPS

number of visible satellites histogram

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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • At the moment successive positions are uncorrelated
  • Next step: applying some kind of filter to get a "flat" route

Position Error Modelling - GPS

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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • Mean error: 7.4 m
  • Standard deviation: 4.9 m

Position Error Modelling - GPS

position error in m distribution function

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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

Poistion Error Modelling - OTDOA

inter-site distance in km histogram

  • Statistics on inter-site distance in simulation scenario
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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig

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 distribution function

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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • 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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  • Prof. Dr.-Ing. Thomas Kürner, TU Braunschweig
  • 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), mehdi.amirijoo@ericsson.com
  • L. Jorguseski (TNO ICT), ljupco.jorguseski@tno.nl
  • T. Kürner (TU Braunschweig), t.kuerner@tu-bs.de

(presenting author)

  • R. Litjens (TNO ICT), remco.litjens@tno.nl
  • M. Neuland (TU Braunschweig), m.neuland@tu-bs.de
  • L. C. Schmelz (Nokia Siemens Networks), lars.schmelz@nsn.com
  • U. Türke (Atesio), tuerke@atesio.de

Contact

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Thank you very much for your attention

FP7 ICT-SOCRATES