SON in LTE LSSummit 2010 - terjin.com · 15/06/2010 1 FP7 ICT-SOCRATES Self-Organisation in LTE...

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FP7 ICT-SOCRATES

Self-Organisation in LTE NetworksNetworks

LS telcom Summit 2010

Lichtenau, Germany, 23rd June 2010

Prof Dr -Ing Thomas KürnerProf. Dr.-Ing. Thomas Kürner

Institut für Nachrichtentechnik

Technische Universität Braunschweig

Introduction– Motivation for Self-Organisation

– FP7-ICT-SOCRATES

Outline

Exemplary Description of SON-functionalities– X-Map-Estimation

– Handover Optimisation

Concluding Remarks

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Thomas Kürner (TU Braunschweig)

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FP7 ICT-SOCRATES

IntroductionIntroduction

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SOCRATES– Self-Optimisation and self-ConfiguRATion in wirelEss networkS

Project period– 3-year duration: From 01/01/2008 until 31/12/2010

Project overview: facts and figures

Effort– Number of person months: 378

– Total project costs: € 4,980,433

Consortium

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Increasing network complexity requires increasing operational effort– new technologies, multi RAT; advanced services, high demand

Effectuate substantial OPEX reductions– minimise human involvement

Optimise network efficiency and service qualityCAPEX d ti

Project overview: main drivers for self-organisation

– CAPEX reduction

Enhance robustness/resilience in case of failures

G&A; 12%

Network Operations; 26%

Interconnection / Roaming; 14%

Terminals and Material; 15% G&A; 12%

Network Operations; 26%

Interconnection / Roaming; 14%

Terminals and Material; 15%

Site Rental; 40%

Power; 10%

Transmission; 20%

Spares, Support &

Training; 5%

Site Rental; 40%

Power; 10%

Transmission; 20%

Spares, Support &

Training; 5%

NetworkOperations

26%

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Marketing & Sales; 26%

Customer Care; 7%

Marketing & Sales; 26%

Customer Care; 7%

O&M; 25%

O&M; 25%

Sources: “Top down analysis of operator OPEX and Capex”. Yankee Group, Feb 2006“Operator BC for P3 with focus on reductions on OPEX”, March 2005, 221 02-FGC 101 456 Uen, rev. A


Self-organisation in wireless networks– Self-configuration

– e.g. ‘plug-and-play’ of new basestations

– Self-optimisation

Project overview: key issues

Self optimisation– measurements, processing,

parameter adjustment, …

– continuous loop

– Self-healing– failure detection

– automatic minimisation ofcoverage/capacity loss

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Focus on 3GPP LTE (E-UTRAN)

Evolutionary approach


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Main Objectives Development of concepts, methods and algorithms for self-organisation

– e.g. handover parameters, antenna parameters, admission control parameters, …

Specification of the required measurements

Project overview: objectives

Specification of the required measurements– statistical accuracy, methods of retrieval, needed protocol interfaces

Validation and demonstration of the solutions– using simulation tools

Assessment of the operational impact– e.g. radio network planning and capacity management processes

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Influence on 3GPP standardisation and NGMN activities


Radio network optimisation– Interference coordination – Self-optimisation of physical channels– RACH optimisation– Self-optimisation of home eNodeB

GOS/QoS related parameter optimisation

For each use case:• Description

• Objective

• Parameters

Triggers

Use cases: self-optimisation

– Admission control parameter optimisation – Congestion control parameter optimisation – Packet scheduling parameter optimisation– Link level retransmission scheme optimisation– Coverage hole detection

Handover related optimisation – Handover parameter optimisation – Load balancing

Neighbour cell list

• Triggers

• Required measurements

• Architect. aspects

• Potential gain

• Related use cases

• References (NGMN, …)

• ….

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– Neighbour cell list

Others– Reduction of energy consumption, Tracking areas,

TDD UL/DL switching point, Management of relays and repeaters, Spectrum sharing, MIMO


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Self-configuration– Intell. selecting site locations

– Automatic generation of default parameters for NE insertion

– Network authentication

Use cases: self-configuration and -healing

Network authentication

– Hardware/capacity extension

Self-healing– Cell outage prediction

– Cell outage detection

– Cell outage compensation

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Supporting Function– X-Map-Estimation


FP7 ICT-SOCRATES

X-Map-EstimationX Map Estimation

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Drive/walk tests are used for radio network planning and optimisation today– expensive– cover only a limited part of the network– capture only a snapshot in time

use mobiles as probes for the service quality

Motivation

use mobiles as probes for the service quality

X-map estimation function– continuously monitors the network– estimates the spatial characteristics of the network, e.g., coverage or throughput– connects the UE measurements to an estimated geographic position– may use other sources of information, e.g. prediction data

X-map is a geographic map with overlay performance information depending on

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– the positioning accuracy– the UE measurement accuracy– the number of measurements taken

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X-map estimation approach

Propagation Model

X-mapPrediction Data

Propagation Model

X-Map Estimation

UE Location and Measurement Unit (LMU)

UE/RAN Measurement, Time, Position

RAN Measurement Unit (RMU)

Propagation Model Calibration

Bin Update

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approach 1: approach 2: Measurement, Time, Location Data

UE1 UEn

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city area of 1.5 km x 1.5 km in Germany

20 mobile users traces derived with the help of SUMO

Simulation scenario

network information available (site location, sector orientation, tilt)

realistic path loss predictions at 2.6 GHz– used for determining 30 strongest cells

for each user position

– reference for determining accuracy of the X-maps

Source: Google Earth 5.0

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satellite orbits for a specific day and time

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For LTE, three different localisation methods are foreseen– Assisted Global Positioning System (A-GPS)

– Observed Time Difference of Arrival (OTDOA)

– Enhanced cell ID positioning method

Position Error Modelling

Model for the minimum mean square position error based on the Cramér-Rao lower bound found in the literature*

This model is based on the– geometry of eNodeBs / satellites and the UE

– number of measured signals

– standard deviation of the measurement error

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* C. Fritsche and A. Klein, “Cramér-Rao Lower Bounds for Hybrid Localization of Mobile Terminals”, 5th Workshop on Positioning, Navigation and Communication (WPNC ‘08), March 2008


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Demonstration for approach 1

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Demonstration for approach 2

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Position error modelling

1000

1500

2000

2500

3000

1000

1500

2000

his

togr

am

GPS OTDOA OTDOA6

400

600

800

1000

1200

-1 0 1 2 3 40

500

1000

-1 0 1 2 3 4 5 60

500

0.6

0.8

1

trib

utio

n fu

nct

ion

number of visible satellites number of hearable eNodeBs number of hearable eNodeBs

GPS OTDOA6 GPS+OTDOA6

valid positions 77.1 % 66.4 % 90.6 %

0 2 4 6 8 10 120

200

400

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0 20 40 60 80 1000

0.2

0.4

cum

ula

tive

dis

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GPSOTDOA6

position error in m

mean error 8.3 m 16.3 m 8.8 m

standard deviation 6.7 m 31.4 m 6.7 m

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Comparison of X-maps with different positioning methods

GPS GPS+OTDOA6 OTDOA6

approach 1 approach 2

GPS 0.1 2.3 2.6 6.6

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GPS+OTDOA6 0.0 2.3 2.9 6.7

OTDOA6 0.0 4.6 4.6 6.7

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FP7 ICT-SOCRATES

Handover OptimisationHandover Optimisation

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Goal: – Improved handover performance

– Reduced number of handover failures

– Reduced number of “ping-pong” handovers

– Reduced number of call drops

Approach:

Goal and Approach

Approach: – Optimisation based on handover statistics

– Analysis of the current handover performance

– Adaptation of handover control parameters

Network monitoring

Handover statistics: HO failure ratio

Handover parameter adaptation

HO t

Performance Analysis

Optimisation Policy:Weighting of the

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HO failure ratioCall dropping ratio Ping-pong HO ratio

HO parameters:Hysteresis

Time-to-Trigger

Weighting of the handover statistics


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Network– LTE FDD, 2.6 GHz, 10 MHz bandwidth

– Realistic network layout:

– Realistic pathloss data (10m/100m resolution)

User mobility

Simulation set-up

– Microscopic road traffic simulator (SUMO)

– Detailed model (traffic lights, diff. speed, …)

LTE System-level simulator

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Analysis of the handover performance in all handover operating points– Hysteresis: 0 – 10 dB (0.5 dB steps)

– Time-to-Trigger : 0 – 5.12 s (18 steps 3GPP)

Oberservability study

Handover Failures

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4

6

8

10

0,1

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0,5

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2

50

0.05

0.1

Han

do

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failu

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atio

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Weighting function for all handover performance indicators:– HP = w1 HPIHOF + w2 HPIHPP + w3 HPIDC

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00 Hysteresis [dB]Time-to-Trigger [s]


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Demonstration of HO optimisation

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Concluding remarks

Concept of SON in LTE networks– Use cases defined in 3GPP and NGNM

– Use cases considered in SOCRATES

First (exemplary) results based on simulations in realistic scenariosX Map Estimation– X-Map-Estimation

– Handover Optimisation

For more information (deliverables, papers, events, …): WWW.FP7-SOCRATES.EU

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Thank you very much for

your attention

FP7 ICT-SOCRATES

your attention

Prof. Dr.-Ing. Thomas KürnerTechnische Universität BraunschweigTechnische Universität Braunschweig

Institut für NachrichtentechnikSchleinitzstr. 22

D-38092 Braunschweig, GermanyE-Mail: [email protected]

SON in LTE LSSummit 2010 - terjin.com · 15/06/2010 1 FP7 ICT-SOCRATES Self-Organisation in LTE...

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