son- from conception to realisation

8/22/2019 SON- From Conception to Realisation

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Self Organising Networks (SON): From

Conception to Realisation

Ali Imran, Qatar Mobility Innovations Center, Qatar

Mischa Dohler, Centre Tecnolgic de Telecomunicacions de Catalunya

Tuesday, April 23, 2013

[email protected]; [email protected]

QMUL, London, UK

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Outline What ?: What is Self Organization (SO)?

Why ?: Why we want SO?

How?: How to Design SO?

Introduction

Standardization

Projects

Open literature

Industrial products with SON capabilities

Characterization

SON for short term dynamics

SON for Medium term dynamics

SON for Long term dynamicsSome Selected Solutions

EnablingSON

NeedforSelfCoordination

Designtools

and

Challenges

Open Research Challenges

[email protected]; [email protected] 2


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Introduction

Standardization Projects

Open literature


Characterization

Self Configuration

Self Optimization:



SON for Long term dynamics

Self Healing

Some Selected Solutions

EnablingSON


Designtools

and

Challenges




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Introduction


Open literature


Characterization

Self Configuration

Self Optimization:




Self Healing


EnablingSON


Designtools

and

Challenges




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What is self Organisation?

Exact definitions are specific to context.

In simple words :a system is said to

have self organization in its behavior if

it can organize itselfwithoutany

external or central control entity.[1]

Case studies in nature can help tomake some key inferences

Shoal of fish Swarm of insects

AgilityManoeuvrability

Flock of Common

Cranes

scalabilityNocentralcontrol

StabilityNochaos

Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

[1]C.Prehofer andC.Bettstetter,Selforganizationincommunicationnetworks:principlesanddesignparadigms,CommunicationsMagazine,IEEE,vol.43,no.7,pp.78 85, july 2005.

A nature inspired phenomenon, too old to date.

Applications to man made systems started half a century ago in cybernetics[Ashby1947], then in thermodynamics, physics andcomplex systems.ad hoc networks and sensor networks.

For the CS, first time [Spilling2000], introduced the concept and need for SO.



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Adaptive /autonomous functionality in a

system is said to be self organizing if it is

scalable, stable and agile enough to

maintain its desired objective(s) in the faceof all potential dynamics in its operating

environment.

O. Aliu, A. Imran, M. Imran, and B. Evans, A survey of self organization in future cellular networks, Communications Surveys Tutorials, IEEE, vol.

PP, no. 99, pp. 1 26, 201


An alternative definition

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Classifying SON Solutions:

Possible Taxonomies



8/[email protected]; [email protected]

Classification of SON

Times scale based classification

Various SON solutions tackle problems arising on different time scale

Objective based classification

Different SON solutions have different objectives

Phase based classification

A WCS has three phases into life cycle, deployment, operation,

maintenance/update but the boundary between them is gray

8


9/[email protected]; [email protected]

Time Scale based classification

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Phase and objective based classification

SOPhases

SO use

Cases/objectives

Self

Configuration

IPAddressandConnectivity

Configuration

NeighbourhoodandContext

Discovery

RadioAccessParameter

Configuration

Self

Optimisation

LoadBalancing

CoverageOptimization

InterferenceandEnergy

ConsumptionMinimization

SelfHealing

Detection

Diagnosis

Compensation



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Overall Description

SELF

ORGANISATION

To implement a self configuring, self optimising and self healing functionalities

future cellular networks



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SelfConfiguration Important

Self

Configuration

Functions:

authentication

addressallocation

secureOAMtunnelsetup

SWinstallation

inventory

management transportparameterssetup

radioparameterssetup

selftest


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Self-Optimisation

AutomaticNeighbourRelation

MobilityRobustness/HandoverOptimisation

(Mobility)Load

Balancing

RACHOptimisation

CoverageandCapacityOptimisation

EnergySaving

InterferenceCoordination

HeNBSON

3GPP

QoS Optimisation(advancedRRM)

Scheduler

admissioncontrol

congestioncontrol

linklevel/L2/HARQretx/MIMOparameters

additionalfromSocrates/NGMN(notdiscus)



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Self-HealingImportant

Self

Configuration

Functions:

failurerecovery

celloutagecompensation



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Introduction


Open literature


Characterization

Self Configuration

Self Optimization:




Self Healing


EnablingSON


Designtools

and

Challenges




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Why we want SO?

Moneymatters!

Growingnumberofnodes/Heterogeneity

Increasingoperational


SemistaticdesignofWCSbut

highlydynamicecosystem 16

Ok d t i i i th it 't th


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Ok data is growing, so is the capacity, aren't the

operators earning more?

Applicationsonsmartdevicesarelimitedby

only,Imagination!

Capacityofwirelesssystemisboundbyfundamentallawsofphysics![1]

Legacycellularsystemshavealreadyyieldedtotheirrupturepoint.

1000folddatagrowthexpectedby2025[2]

[1]Dohler,M.;Heath,R.W.;Lozano,A.;Papadias,C.B.;Valenzuela,R.A.,"IsthePHYlayerdead?,"CommunicationsMagazine,IEEE,vol.49,no.4,pp.159,165,April2011[2]Wiseharbor,2025forecast

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But:

LTE roll-out could cost tier one operators

up to $8BN USD in CAPEX over the first3-5 years.

Adding LTE could add another 30% to

todays OPEX. (Aircom, reports)

Smaller cells are also inevitable due to

higher frequencies Larger number of nodes.

3 different technologies to manage and

operate GSM, UMTS, LTE

All IP, packet based access means always

active control plane.


Will LTE solve the dilemma?

Longtermsolution2025

http://www.3gpp.org/FutureRadioin3GPP300attend

Onlyin

terms

of

temporary

Needforspeed,

Maybeyes!

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ARPB dilemma, a seriously Growing Concern for Operators!

TotalCost

of

Ownership(TCO)

Vs

revenue

not

adding

up!

Tsunamiofsmartphonessome notreallysmartapplicationsiswashingawaytheAverageRevenuePerBit(ARPB)!

OperatorsMAY chargefordata,butnotforcontrolsignaling

Energybillsaregoinghigherandhigher!

ARPB=AverageRevenuePerbit 19


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Social messaging applications cost mobile networkoperators $13.9bn (8.8bn) in lost SMS revenue last year, arecent report has claimed.

http://www.bbc.co.uk/news/technology-17119768

While data traffic grows more than 1,000-fold, operatorrevenue yield per megabyte will decline dramatically from

$100 with SMS, $1 in voice and

$0.10 with mobile data in 2010

To $0.001 with data predominating in 2025 (global averagesincluding postpaid and prepaid plans).

(Wiseharbor, 2025 forecast)


Not Convinced, Need more Evidence?

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Why we want SO?

Moneymatters!

Growingnumberofnodes/Heterogeneity



SemistaticdesignofWCSbut

highlydynamicecosystem 21


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System Degrees of Freedom

Degreesof

Freedom

(DOF)

/Area:

newsystemDOF=oldsystemDOF x2030

newsystemdensity=oldsystemdensity x4

newDOF/km2=oldDOF/km2 x100

1

100

10000

1000000

0000000

1E+10

1E+12

GSMEDGE

UMTSHSPA

LTELTEA

CELLULAR+WIFI+FEMTO

CELLULAR+WIFI

CELLULAR



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Likelihood of Failure/Outage

ImportantHigh

Level

Insides:

DOF/areaincreases failures/outagesincrease:

lossinrevenueifnothingdone,and/or

verycostly

if

addressed

by

human

labour

[htt

p://www

.sc

ience

direct.com

/scie

nce

/art

icle/p

ii/S0951832004000031]



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Therefore

Moneymatters!Growingnumberofnodes

/Heterogeneity



Semistatic

design

of

WCS

but

highlydynamicecosystem

Somethingmorethanlarger

Capacityand

HigherData

ratesis

required,

fortheoptimal

performance

andmakeARPB

right.24


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What is this something more we need

For commercial and technical viability future WCS

need to be autonomously adaptable while being

Scalable

Stable

Agile

i.e. WCS need to have Self organisation

ContextandMotivation

Selforganization

agility

stability

scalability

Problem CauseCounter

Measure

Suboptimal

performance

Range of

Dynamics

Semi static

design

Adaptability

Agility

Increasing

Cost

Ubiquitous

deployment

Automization

Increasingcomplexity

ScalabilityManual

intervention

+

+

+



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Deployment

survey

Planning

Deployment

Configuration/

commissioning

Operation

Remote

monitoring

Infieldmeasurements

Manualoptimization

Maintenance/upgrade

ManualProblem

Diagnosis

Manual

Compensation

Installationofnewnodesneedupdateof

neighboring

nodes


Which of the Operators processes SON can Eliminate?

LifeCycle

of

Cellular

System

CAPEX70%ofTCO

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What is happening now in operation of typical cellular system?

Database

Measurements

User Reports

based

Drive

test

based

Parameter update

Remote

In field

Performanceanalysis Optimalparameter

calculation

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How SON will benefit?

Database

Measurements

User Reports

based

Drive

test

based

Parameter update

Remote

In field

Performanceanalysis Optimalparameter

calculation

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How Self Optimization will work?

User Reports

based

Parameter update

CentralizedAutonomous

Distributed

Autonomous

Continuous SelfOptimization

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How much time does the modeled task require?

How many staff are needed?

How frequently is the task performed?

What level of expertise is required for the task?

What are the labor costs?


Quantifying SONs Value

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iGR forecasts a saving of:

2.34 billion in LTE Capex

4.5 billion in LTE [email protected]; [email protected]

Any estimates on savings SON can provide?

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Outline What ?: What is Self Organization (SO)? Why ?: Why we want SO?


Introduction


Open literature


Characterization

Self Configuration

Self Optimization:




Self Healing


EnablingSON


Designtools

and

Challenges




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How to design SO

Biomimetic Approach Biomimetics is branch of science where the design and operational

principles of complex natural systems are studied with aim to extractdirect or indirect design strategies for man made systems

Direct biomimetics

Design of air craft wings Radar

Camera Indirect biomimetics

Ant colony optimisation

Neural networks

Game theory A-Biommetic Approaches

Classic analytical tools,

e.g. optimisation, etc..

Aselforganisingsysteminnature:Fishshoal

For more details please look at:

[1]AliImran,MehdiBennisandLorenza Giupponi,UseofLearning,GameTheoryandOptimizationasBiomimetic ApproachesforSelfOrganizationinMacroFemtocell Coexistence, acceptedinWCNC,2012.

[2]A.Glenn,A.Imran,Muhammad.A.Imran,R.Tafazolli,ASurveyofSelfOrganisationinFutureCellularNetworks,inpressinIEEEJournalofSurveysandTutorials



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SON Architecture Approaches [1/2]

Levels of SON Execution: localised: autonomous SON execution based

on purely local information at (H)eNB & UE

distributed: autonomous SON executionbased on information exchanged with

neighbouring (H)eNB (eg via X2 interface) centralized: decision taking based on (fairlycomplete) system information (eg at

NM/DM/EM levels)

hybrid: any mixture of above

X2

NM

DM/EM

SON

SON

DM/EM

SONSON

SON

NM

=

Network

ManagementDM=DeviceManagement

EM=ElementManagement



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SON Architecture Approaches [2/2]

centralized distributed localized hybrid

KPI=KeyPerformanceIndicator


K Ti I t l


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Key Time Intervals

For Centralized SON, timing is important: Collection Interval: time period during which statistics

and data are collected; limited by vendors OAM

bandwidth; typical 5min (i.e. not at scheduling level!) Analysis Interval: time period needed to draw decision;

typically several collection intervals (filtering effect by

considering also prior data history) Change Interval: time period between executing the

changes in the network; typically limited by systems

operational constraints



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Important SON Trade-Offs

Centralised

multiplecellslongtermstatisticsusesOAMmultivendor/multiRAT

selfconfigurationcoverageoptimisation(loadbalancing)

Distributed

normally ca.2cellsusesX2

multivendor(X2)

handoveropt.loadbalancingRACHopt

Localised

adv.RRMsmallimpactonncellsshorttermstatistics

scheduleroptLinkadapt.optRACHopt

faster&

less

prone

to

single

point

of

failure

infofromnumberofcells/RATs


Coordinated SON Model


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Coordinated SON Model

very

difficult to

standardise:lotsofdifferentwaystoimplementaSONfunctionare

possible

e.g.

load

balancing

(LB)

done

at

NMlevelbuthandover(HO)

optimizationdoneateNB level

andbothwant toadjustthesame

parameter

in

eNB

practicalwayaround today:iffunctionatNMlevel,

configureis

standardised

ateNB levelnotstandardised

currentlyprimary&secondary

targetsaredefined

[basedonS5102029]

management

decisiontaking

e

xecution

feedback



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Introduction


Open literature


Characterization

Self Configuration

Self Optimization:




Self Healing


EnablingSON


Designtools

and

Challenges



s npu o


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s

npu

o

SON

NextGeneration

Mobile

Networks

(NGMN)Alliance:

createdin2006bygroupofoperators

business requirementsdriven

oftenbasedonusecases ofdaily

networkingroutines

NGMNandSON:

SONinput

to

3GPP

since

2006

10SONusecaseshavebeendefined

(seeright)whichinputto3GPPRx

QoS SONis

likely

to

appear

shortly

g eve


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g

eve

Structure

maintenance/

development

ofGSM/GPRS/

EDGERAN

maintenance/

development

ofUMTS/HSPA/

LTERAN

system

architecture,service

capabilities,codecs

(inc. EPC)

CNinterfaces,

protocols,

interworking,

IMS,

terminals,SIM

Management!

n


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n

Release8

3GPPR8

is

about

eNB self

configuration:

automaticneighbor relation

automaticphysicalcellID(PCI)assignment

automaticinventory

automaticsoftwaredownload

2007 2008 2009 2010 2011 2012 2013 2014Release8

Release9

Release10

Release11

Release12

n


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n

Release9

3GPPR9

is

about

network

optimization

procedures:

mobilityrobustnessandhandoveroptimization

RACHoptimization

loadbalancingoptimization

intercellinterferencecoordination(ICIC)

2007 2008 2009 2010 2011 2012 2013 2014Release8

Release9

Release10

Release11

Release12

n


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n

Release10

3GPPR10

is

about

overlaid

networks:

coverage&capacityoptimization

enhancedICIC

celloutage

detection

and

compensation

selfhealingfunctions

minimizedrivetest

energysavings

2007 2008 2009 2010 2011 2012 2013 2014Release8

Release9

Release10

Release11

Release12

n


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n

Release11

3GPPR11

will

be

about

heterogeneous

networks:

automatednetworkmanagement

troubleshooting

multilayer,multiRATheterogeneousnetworks

SONcoordinationamongothers

2007 2008 2009 2010 2011 2012 2013 2014Release8

Release9

Release10

Release11

Release12

n


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n

Release12

3GPPR12

is

also

on

heterogeneous

networks:

MobilityloadbalancingbetweenLTEandHRPD.

AutomaticneighborrelationshipbetweenLTEandHRPD.

Mobilityrobustness

optimization

between

LTE

and

HRPD.

EnhancementinCCOandMDT

MultivendorPlugandPlayeNB connection

2007 2008 2009 2010 2011 2012 2013 2014Release8

Release9

Release10

Release11

Release12


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3GPP SON Status

PCI_Sel.

ANRMLB,MRO,RACH

EnergySaving(ES)

MinimizeDriveTest(MDT)

SONMgmt

SelfHealing

MDT

PCISelection

Energy

Saving

MLB,MRO,CCO

SA5

RAN

Rel9 Rel10Rel8

ANR=AutomaticNeighborRelation

PCI =PhysicalCellID

MLB=MobileLoadBalancing

MRO=Mobility

Robustness

Opt.

RACH=RandomAccessChannel

CCO=Capacity&CoverageOpt.

Rel11Rel12

HetNetMRO

HetNetES

SONCoordination

LTEHRPDinterRATMLO,

MLB,ANR

IntegrationofCCOand

MDT

MultivendorplugandPlay



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Standardization- NGMN recommendations

NGMN has come up with use cases related to self organising networks and overall descriptions 2006.

Deployment: self configuration

The eNB will support complete plug and play capability no provisioning of hardware resources is required. Inventory

information is automatically recorded and reported.

The eNB will algorithmically compute its physical cell ID through communication with neighboring eNBs.

The eNB will determine its neighbors with the help of user equipment. It will continue to optimize and refine this list

in real-time, discovering new neighbors and deleting stale neighbors.

The eNB will automatically determine and continually optimize its RF parameters, including antenna tilt, power output

and interference control

The eNB will automatically setup its transport capabilities, establishing contact with the Element Management System

(EMS), Mobility Management Entities (MME), etc.

The eNB will support a complete self-test of itself, allowing the technician to easily verify operation of the eNB after

installation.

Upon connection to the EMS, the eNB will automatically authenticate itself into the network and update to the correct

version of software, if necessary.



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Standardization-NGMN Recommendation(2)

.

Operation: self optimization

Automatic neighbor optimization, including the discovery of new neighborsand deletion of stale neighbors.

Automatic interference reduction, including coordination of sub-tones andpower levels across eNBs.

Automatic handoff optimization, including monitoring KPIs to optimizeintra/inter-RAT handoffs by iteratively adjusting target C/I and RSSI.

Automatic Transport QoS optimization, including monitoring KQIs toiteratively adjust QoS configuration.

Automatic energy savings, by examining service loading trends anddetermining when equipment can be powered down without adversely affectingservice.



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Standardization-NGMN recommendations(3)

Maintenance/update: self healing

Complete and standardized inventory reporting of all components from all NEs.

Robust cell outage detection capabilities for latent faults.

Integrated cell outage compensation capability that automatically reconfigures

surrounding cells to offset the effect of a failed cell.

First and second order root cause analysis and recovery of faults.

Real-time PM data to verify service capability after a repair or reconfiguration.

Multi-vendor subscriber and equipment trace, to aide system troubleshooting.


li


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Introduction


Open literature


Characterization

Self Configuration Self Optimization:




Self Healing


EnablingSON


Designtools

and

Challenges



j l d SO ( )


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Key Projects Related to SON(1)

Celtic Gandalf Project ----- (2005 - 2007) Main focus was to achieve automation of network management

tasks in a multi-system (GSM, UMTS) environment.

End to End Efficiency (E3) ----- (2008 - 2009) Design and develop solutions for seamless

interoperability between legacy systems and futurewireless systems across different access technologies.

SOCRATES ----- (2008 - 2010) Working on a framework for integrating network

planning, configuration and optimisation into a single

automated process.


K P j R l d SON(2)


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Key Projects Related to SON(2)

BeFemto -------- (2010 - 2012) Next generation of femtocell technologies

Self organisation and interference management in future indoor/outdoorbase stations

UniverSelf --------(2010 - 2013) Focus on the growing management complexity in future networks

Incorporate self organising functionalities in the network equipment(Intelligence Embodiment)

Unified management framework of existing and emerging systemarchitectures

IU-ATC (2010 - 2012)

Investigation of system performance bounds in cellular multi-hop wirelessnetworks.

Development and analysis of distributed dynamic spectrum andinterference management algorithms.

Development and analysis of cross-layer algorithms for QoS provision andperformance optimisation.


O li


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Introduction


Open literature


Characterization





Self Healing


EnablingSON


Designtools

and

Challenges



A brief time line of SON:


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A brief time line of SON:

From conception to realisation

Pre 2000: Conception of SO: Spilling1998: First use of SO in context of adaptive power

control algorithm in GSM

Spilling1998,Badia2004: Need for SO in cellular networksbut...

Pre-2005: Broader Visualisation of SO. Prehofer2005,Yanmaz2005: Provided principles and

paradigms in designing self organising systemsbut...

Post 2005: Realisation of SON. Yanmaz2006,Stolyar2009,Bernado2009,(Imran2009,2010,

2011,2012...): Recent works with proposed algorithms andsolutionsbut..



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Self

Configurationlogical flow


S lf C fi i


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Self Configuration

Neighbour Cell list (NCL) Configuration Parodi2007: Framework for self configuration of future LTE

networks.

3GPP R3-071239 Mitsubishi2008: Initial configuration of NCL based

on geographical coordinates only.

Li2010: NCL based on geographical coordinates, antenna pattern andtransmission power

Kim2010: NCL based in SINR measurements from adjacent cells

(desired threshold?)

Best Scheme?


S lf O ti i ti


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Self Optimization

[1]O.Aliu,A.Imran,M.Imran,andB.Evans,Asurveyofselforganizationinfuturecellularnetworks,CommunicationsSurveys Tutorials,IEEE,vol.PP,no.99,pp.126,2012.

58

S lf H li


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Self Healing

Mueller2008: Cell outage detection algorithmbased on Neighbour Cell List (NCL)

Khanafer2008: Cell outage detection algorithmusing bayesian network.

Amirijoo2009: Described concepts and ideas forcell outage compensation but no results were

demonstrated.

Literature lacking on cell outage compensation

for cellular [email protected]; [email protected] 59

StartAlgorithm continuously

monitors system for


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Update NCL and begin

compensation action(s)

Performance analysis(faulty cell and

neighbouring cells)

Update Neighbour Cell

List (NCL) and begin

compensation action via

specific neighbouring

cells

Ensure compensatingactions are relaxed when

a faulty node has been

restored.

Monitor, Repair and

control compensating

actions

Take measurements

and NCL data Analyse all alarms, alarm

correlation and determine

specific compensating

action(s) from

neighbouring cell(s)

Analysis and Diagnosis

Y False

detection?

Store alarm

information for

future analysis

N

Monitor TCoSH

Clustering algorithmclassifies alarms to

determine type of fault and

identify necessary

compensating action

N

Y

y

alarms that Trigger

Conditions of Self Healing

(TCoSH)

TCoSH

reached?

Trigger self healing

process

Alarm

cleared?

COMPENSATION

ALARM

DIAGNOSIS

MONITORING


O tli


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Introduction


Open literature


Characterization





Self Healing


EnablingSON


Design

tools

and

Challenges



I d t t t SON (1)


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Motorola

Launched its first O&M agent that features SON in

2007. LTE will use release 3 of this agent. Key features

Automation (elimination of tasks, operations

efficiencies, and increased value of existing staff).

Reduced expertise requirement.

Reduced high-level oversight.

Does not add to current processes

Provides a review mode for each SON operation


Industrys status on SON (1)

62

I d t t t SON (2)


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ComparedtoMotorola,NECsSONsolutionaresupportedwithasimulationtool

Compared

to

Motorola,

NECs

SON

solution

are

supported

with

a

simulation

tool

63

I d t t t SON (3)


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NSN: iSON

Automatically balances the traffic loads and

optimizes the transmission capacity distributionin the core networks

autonomously optimize and repair

Huawei: SingleSON:

SC: Semi- SC of RF parameter, Inter-RAT ANR,

SO: ICIC, Multi-RAT MLB, MDT

SH: CODC,



SC:SelfConfiguration

ANR:AutomaticNeighborRelationshipSO:

Self

Optimization

SH:SelfHealing

MLB:MobilityLoadBalancing

MDT:MinimizingDeriveTestCODC:

Cell

Outage

Detect

and

CompensationICIC:

Inter

cell

Interference

Cancellation

64

I d t t t SON (4)


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Optimi

Multi RAT SON, Self Healing, Autonomous Tx planning

Started in 2003, Acquired by Ericson 2010

Celcite:

Multi RAT, Multi vendor, Automatic Intelligent Correlation (AIC)

AIC engine automatically collects a variety of data types from the

network, such as performance counters, configuration data,

faults/alarms, trouble tickets, mobile measurements, and geo location

data. AIC then automatically correlates all data points to provide triagebetween RF and operational issues, provides root cause analysis, audits,

recommendations, and executable solutions along with mml or xml

scripts



65

Ind str s stat s on SON (5)


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RADCOM: The QiSolve

Gathers, correlates and analyzes network intelligencefrom the RAN and PCN to provide real-time insightsinto network performance and how it affects thesubscribers QoE.

Intucell (Aquired by CISCO Jan-2013)

ANR,SO of RF parameters, MULTI-RAT.

Arieso: (Acquired by JDSU, Mar-2013) Their main solution expands on autonomous coverage

and performance estimation based on user

measurements [email protected]; [email protected]


66

Summary of SoA


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Standardization Activity Started with focus on LTE only, expanded to include Multi-RAT

to reflect sustained dominancy of legacy systems

Major aspects of SON covered, current focus is filling the gapsto ensure self coordination compatibility among SON functions.

Academic Activity: Open literature more focused on self optimization, and relatively

less attention is channeled to self configuration, with self healingand self coordination being almost neglected.

While myriad of parameter adaptation solutions has beenproposed in only a few feature characteristics scalability , agilityand stability to harness full potential of SON.

Industrial Activity Some industrial vendors have managed to launch SON products

that are though effective but are not necessarily inline with

standardization activities in general.


Summary of SoA

67

Outline


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Introduction


Open literature


Characterization





Self Healing


EnablingSON


Design

tools

and

Challenges



C1: Tilt optimization through BSOF for spectral efficiency


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optimization



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State of the art on antenna Tilt optimisation

1) Tilt adaptation for interference reduction[Wille2005, Siomina2005,Siomina2006, Calcev2006]

Do not cope with hotspots/non homogeneous user distribution

2) Tilt adaptation for coverage control for load balancing/hotspot relief [Wu1998, Abou-Jaoude2009, Islam2010, Viering2009] Necessitate handovers

Central control/ heavy signalling

Low scalability and agility Our Solution : TO-BSOF

System-wide self organisation of antenna tilts in distributedmanner

Spectral efficiency at the hotspots increases withoutsacrificing the average user.

No central control/global signalling

High scalability and agility


C1: Problem statement


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System Model and Assumptions:

Multi cell, sectorized cellular system

Realistic user geographical distribution Realistic antenna patterns

Interference limited

C1:TO [email protected]; [email protected] 71


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Self Organisation in Nature: A Sase StudyFlock of Common Cranes

Analysis of case study of flock of Common Cranes

How?:BSOF

A flock of common cranes enhances its flight efficiency by 70% through self organisation of its

flight attributes [2]

[2]P.B.S.LissamanandC.A.Shollenberger,Formationflightofbirds,

Science,vol.168,no.3934,pp.10031005,[email protected]; [email protected] 72


73/133

Self Organisation in Nature: A Sase Study

Deduced design and operational principle of self organisationAnalysis of case study of flock of Common Cranes

How?:BSOF

A flock of common cranes enhances its flight efficiency by 70% through self organisation of its

flight attributes [2]

[2]P.B.S.LissamanandC.A.Shollenberger,Formationflightofbirds,

Science,vol.168,no.3934,pp.10031005,[email protected]; [email protected] 73


74/133

A General Framework for Designing Self Organisation: BSOF

Biomimmetic Self Organisation Framework (BSOF)

How?:C0:BSOF

Nature

Nurture


A General Biomimetic Framework for Designing Self


75/133

Organisation: BSOF

Biomimmetic Self Organisation Framework (BSOF)

Reproducedfrom[1]

[1]A.

Glenn,

A.

Imran,

Muhammad.

A.

Imran,

R.

Tafazolli,

A

Survey

of

Self

OrganisationinFutureCellularNetworks,inpressinIEEEJournalofSurveys

andTutorials

Tools for designing self organisation

How?:C0:[email protected]; [email protected] 75

C1: Problem statement


76/133

System Model and Assumptions:

Multi cell, sectorized cellular system

Realistic user geographical distribution Realistic antenna patterns

Interference limited

C1:TO BSOF

Flock System

Common

cranes

Base

Stations

Flight

attributes

Antenna

Tilts

fly operate

Flight

efficiency

Spectral

efficiency

Air drag interference

A flock of common cranes

wants to self organise its

flight attributes and fly

such that the average flightefficiency in the whole flock

is maximised by minimising

the average air drag.

system base stations

antenna tilts

system

operate

interference.

spectral

Mapping Table

Problem statement (using analogy of common cranes)



77/133

C1: Problem formulation: Identifying SO-Objective



78/133


Maximise spectral efficiency in hotspots by optimizing tilt angles of all theN BS in the system

Average user throughput should not decrease

Subject to:



79/133


Maximise spectral efficiency in hotspots by optimizing tilt angles of all theN BS in the system

SIR at kth location from nth BS

Vector of optimization variables

Subject to:

where

and

Average SIR in sth hotspot


C1: Achieving SO solution via BSOF


80/133

C1: Achieving SO solution via BSOFSO-Objective SO-Goal SO-Function

C1:TO [email protected]; [email protected]



81/133


Analytically

SO-Objective SO-Goal SO-Function

C1:TO BSOF

Proposition: Tilt optimization with respect to Centre of Gravity (CG)

can boost the average SINR in a cell

Simplification

viaCGconcept

>Determining (CG) of user geographic distribution




82/133


Analytically Analytically

=

SO-Objective SO-Goal SO-Function

C1:TO BSOF

Proposition: Tilt optimization with respect to Centre of Gravity (CG)

can boost the average SINR in a cell

Simplification

viaCGconcept

Decomposition

viatripletconcept

Proposition: Tilt optimization in individual triplets can almost

optimize tilts system wide>Determining (CG) of user geographic distribution


C1 E bli th ti f SO F ti


83/133

C1: Enabling the execution of SO-Function

Subject to:




84/133


Subject to:

C1:TO BSOF

And




85/133


Subject to:


S stem Le el Sim lation Parameters


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System Level Simulation Parameters


Simulation Scenario


87/133


Simulation Scenario

87

C1: Numerical results: 1 Triplet scenario


88/133

C1: Numerical results: 1-Triplet scenario


C1: System level simulation results (1)


89/133





90/133





91/133





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ExpectedormeanspectralefficiencyforallusersinthewholeareaofWCSandusersinthehotspotsC1:TO BSOF

45%

gain

20%gain


Case

1:50%user

Hotpot

Case2:

80%users

inHotspot

92

Summary of TO-BSOF


93/133

Summary of TO BSOF

30% gain in spectral efficiency in

face of non homogenous

geographical user distributions with

No Central controlNegligible signalling

overhead

Agile for short term

dynamics



94/133

Imran, A.; Imran, M.A.; Tafazolli, R., Relay Station Access Link Spectral Efficiency Optimization through SO of Macro BS Tilts. IEEE Communication Letters, Vol. 99, page 1-3, Nov-2011

94

SO Solution for Load Balancing


95/133

SO Solution for Load Balancing

C2:LB [email protected]; [email protected] 95

LB-BSOF


96/133

LB BSOF

State of the art in LB: [1]

1) Resource Adaptation based LB

2) Traffic Shaping (TS) based LB

3) RS based LB

4) Coverage Adaptation (LB) based LB

a) LB through Antenna Adaptationb) Power Adaptation based LB

[Das03, Koutsopoulos07,Son 07,Son09]: centralised -> heavy signalling

[Siomina04 ]: long term traffic statistics -> off line, very less agile

central control/ heavy signalling/low agility

Load balancing through BSOF (LB-BSOF)

Do not require central control/global signalling

Agile for medium term dynamics

Multiple SO-Functions : Coverage Adaptation

Resource Adaptation

Beam switching

A.Glenn,A.Imran,Muhammad.A.Imran,R.Tafazolli,ASurveyofSelfOrganisationinFutureCellularNetworks,to

appearinIEEE JournalofSurveysandTutorialsC2:LB [email protected]; [email protected]

96

Achieving SO solution through BSOF


97/133

Achieving SO solution through BSOF

C2:LB BSOF

SO-Functions:

1)Coverage Adaptation at BS (BCA)

2)Coverage Adaptation at RS (RCA)

3)Access Link Adaptation (ALA)

4)Beam Switching of RS (BSR)

Operational use cases of LB-BSOF:

1) Micro level (SO-Function 1,2)

Intra super cell

2) Macro level (SO-Function 3,4)

Inter-super cell

3) Global level (SO-Function 1,2,3,4)

Inter Operators

Inter Infrastructures

Self Healing


Super cell concept in LB-BSOF


98/133

p p


A heuristic algorithm for LB-BSOF


99/133

A heuristic algorithm for LB BSOF

C2:LB [email protected]; [email protected]

Simulation scenarios and parameters


100/133

Simulation scenarios and parameters

1. User distribution

a) Uniform

b) Non unirom

2. Sectorization

a) 3 sector

b) 6 Sector

3. RS

a) No RS

b) In band RS

c) Out of bandRS (Pico cell)


Call rejection Log


101/133

j g


Call rejection Log


102/133

j g

>2timesincreaseusercapacity

forsame

GoS


LB-BSOF Gain


103/133

279,85%

78,78%

7,12%

68,41%

0,0%

50,0%

100,0%

150,0%

200,0%

250,0%

300,0%

Non Uniform Uniform

Blockingas%o

fabsoluteminimumbl

ocking

No LB

LB-BSOF


C3: Long term Multiobjective optimization through BSOF


104/133

g j p g

C3:[email protected]; [email protected] 104

Some Important consideration over long term


105/133

p g

Most of the SoA applicable to (very) short term dynamics

Short term dynamics become irrelevant

Optimization can not be for short term objectives like Throughput,

short term energy efficiency

rate fairness etc

Operators policys become important

Only long term parameters of WCS can be optimized

Frequency Reuse (F)

Number of sectors per site (S)

Number of RS per site (R)


Performance characterisation Framework (PCF) for WCS over long term


106/133

( ) g

Capacity

QoS

Economy

WCS KPI

C3A:[email protected]; [email protected] 106

Spectral efficiency of given heterogeneous network


107/133


p y g g

107

Capacity of FD


108/133


p y

108


109/133


Visual illustration


110/133

0

MCEL

l

l

l t

A

user 2 user MCE = log (1+SINR )user user(dB)MCE = [SINR ]

f

Theoretical Maximum Spectral EfficiencyAchievable

Spectral Efficiency

MCESINRNo

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

5.554719.83

5.115217.81

4.523415.88

3.902314.17

3.322312.29

2.730510.36

2.40638.57

1.91416.52

1.47664.69

1.17582.67

0.87700.76

0.6016-1.25

0.3770-3.18

0.2344-5.14

0.1523-6.94

MCESINRNo

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

5.554719.83

5.115217.81

4.523415.88

3.902314.17

3.322312.29

2.730510.36

2.40638.57

1.91416.52

1.47664.69

1.17582.67

0.87700.76

0.6016-1.25

0.3770-3.18

0.2344-5.14

0.1523-6.94

0

AL

t l

l

A

MCE= Modulation and Coding Efficiency

L= Total number of modulation coding schemes

= Expected mean spectrum efficiency

At = Total Area (Number of bins)

Al = Selected Area(bins) with same MCE)


EC of FD


111/133


Performance characterisation Framework (PCF) for WCS over long term


112/133

Capacity

QoS

Economy

WCS KPI

EffectiveSpectralEfficiency(ESE)

ServiceAreaFairness(SAF)

EnergyConsumption

(EC)

C3A:PCF

PCF

Imran,A.;Imran,M.A.;Tafazolli,R.;,"AnewperformancecharacterizationframeworkforDeploymentArchitecturesofnextgenerationdistributedcellularnetworks,"(PIMRC),IEEE; vol.,

no.,pp.20462051,2630Sept.2010

Imran,A.;Imran,M.A.;Tafazolli,R; "AnovelSelfOrganizingframeworkforadaptiveFrequencyReuseandDeploymentinfuturecellularnetworks,"21st (PIMRC),IEEE; vol.,no.,pp.2354

2359,[email protected]; [email protected]

Numerical results for PCF


113/133

0 1 2 3 4

13. S= 2,F=1,R=1

14. S=2,F=2,R=1

15.S=3,F=1,R=1

16. S=3,F=3,R=117. S=4,F=1,R=1

18. S=4,F=2,S=1

19. S=4,F=4,R=1

20. S=4,F=1,R=4

21. S=4,F=2,R=4

22. S=4,F=4,R=4

23. S=6,F=1,R=3

24.S=6,F=2,R=325. S=6,F=3,R=3

26. S=6,F=6,R=3

SAF

0,00 2,00 4,00 6,00 8,00 10,00

13. S= 2,F=1,R=1

14. S=2,F=2,R=1

15.S=3,F=1,R=1

16. S=3,F=3,R=117. S=4,F=1,R=1

18. S=4,F=2,S=1

19. S=4,F=4,R=1

20. S=4,F=1,R=4

21. S=4,F=2,R=4

22. S=4,F=4,R=4

23. S=6,F=1,R=3

24.S=6,F=2,R=3

25. S=6,F=3,R=3

26. S=6,F=6,R=3ESE(bps/Hz/site)

ESE based on Theoratical Shannon Bound ESE based on Practical MCS's


SOFD


114/133

SO-Objective: Multiobjective optimizationproblem

where

F: Frequency reuse ; S: number of sectors per site and R: Number of RS per site

SO-Goal: A single utility function

SO-Functions: A set of simple actuators

1. Adapt number of projected sectors (S)

2. Adapt frequency reuse scheme (F)

3. Switch on or off remote RS (R)

switch on or off the circuitry associated to each sector and RS within BS

Weights to reflect priority of objectives

according to operators policies

ESE :Effective Spectral Efficiency SAF (Service Area Fairness) EC: Energy Consumption

C3B:[email protected]; [email protected] 114

Solution space for SO-GoalsOptimal FD in terms of SAF


115/133

FD Feasible S, F, R

combinations

S 1 2 3 4 6

F 1 1,2

1,3

1,2,

4

1,2,

3,

6

R 0,

1

0,

1

1,

3

0,

1,4

0,

3

-1-0,8

-0,6

-0,4

-0,2

0

0,2

0,40,6

0,8

1

1.

S=1,F=1

2.

S=2,F=1

3.

S=2,F=2

4.

S=3,F=1

5.

S=3,F=3

6.

S=4,F=1

7.

S=4,F=2

8.

S=4,F=4

9.

S=6,F=1

10.

S=6,F=2

11.

S=6,F=3

12.

S=6,F=6

ESE SAF EC

p

Optimal FD in terms of ECOptimal FD in terms of ESE

00,10,20,30,40,50,6

0,70,8

1.

S=1

,F=1

2.

S=2

,F=1

3.

S=2

,F=2

4.

S=3

,F=1

5.

S=3

,F=3

6.

S=4

,F=1

7.

S=4

,F=2

8.

S=4

,F=4

9.

S=6

,F=1

10.

S=6

,F=2

11.

S=6

,F=3

12.

S=6

,F=6

d=1,d=1,d=0 d=.7,d=.5,d=-.2 d=.5,d=.9,d=-.9

C3B:SOFD


Outline


116/133

What ?: What is Self Organization (SO)?



Introduction

Standardization

Projects

Open literature


Characterization





Self Healing


EnablingSON


DesigntoolsandChallenges



Self healing concept


117/133

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

Distance (metres)

Distan

ce(metres)

-1.0E+001

-6.7E+000

-3.3E+000

0.0E+000

3.3E+000

6.7E+000

1.0E+001

1.3E+001

1.7E+001

2.0E+001

Arsalan Saeed,Osianoh GlennAliu,MuhammadAliImran"ControllingSelfHealingCellularNetworksusingFuzzyLogic"IEEEWireless

CommunicationsandNetworkingConference(WCNC),Paris,France,April2012.



118/133

-10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SINR

co

mmulative

distribution

function

Outage

Power Compensation

Normal

-10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SINR

co

mmulative

distribution

function

Outage

Power Compensation

Normal


Outline


119/133

What ?: What is Self Organization (SO)?



Introduction

Standardization

Projects

Open literature


Characterization





Self Healing


EnablingSON


DesigntoolsandChallenges



Attention need to be channeled to Enabling solutions for SON


120/133

Identification/Characterization/Quantification of right KPIs, incorporating

1) Times scale of SON solutionFor example for long term self optimization e.g. Planning

Parameters can not be optimized with respect conventionalmetrics as they depend heavily on short term dynamics. e.g.

Throughput-> shadowing, fast fading, interference

Fairness-> scheduling

Instantiations energy consumptions -> power allocations to carriers

2) Mutual dependencies of objectives

e.g. Spectral efficiency is coupled with energy efficiency

Solutions for automating the KPI measurement process


Need for holistic SON Framework


121/133


Use the problem generatorsto solve the problem


122/133

Time

TrafficA

TrafficB


SON as enabler of high QoE at low cost, in future CS


123/133

Infrastructure sharing has been shown to havemany benefits

With advent of small cells, it is making moresense.

Imagine, a single infra structure, shared by all theoperators, with the help of SON, to guarantee

Energy efficiency

Low cost operation

Ubiquitous coverage


OptimizationObjectives Configuration/Optimization

Parameter(s)

Minimizeinterference Transmitpower(cellsize)

RBassignment(scheduling)

Adjustbeamformingparameters


124/133

socrates

Sectorization

BSlocation

Frequencyreuse

AntennaTilts

Maximize/Optimizecoverage Transmitpower

Antennatilts

Balanceload Transmit

power

(Cell

sizes

and

BS

locations)

Antennaparameters

HOparameters

Cellreselectionparameters

Minimizeenergyconsumption Transmitpower

Antennaparameters

NumberofusedTx antennas

Maximizecellcapacity Transmitpower

Admissioncontrolthreshold

Congestion

detection

and

resolution

parametersSchedulerparameters

Linklevelretransmissionschemeparameters

Trackingareaparameters

Switchingpointconfiguration

CQIthresholdsforMCEswitching

FrequencyReuse

Sectorisation


OptimizationObjectives Configuration/Optimization

Parameter(s)

Minimizeinterference Transmitpower(cellsize)

RBassignment(scheduling)

Adjustbeamformingparameters


125/133

socrates

Sectorization

BSlocation

Frequencyreuse

AntennaTilts

Maximize/Optimizecoverage Transmitpower

Antennatilts

Balanceload Transmit

power

(Cell

sizes

and

BS

locations)

Antennaparameters

HOparameters

Cellreselectionparameters

Minimizeenergyconsumption Transmitpower

Antennaparameters

NumberofusedTx antennas

Maximizecellcapacity Transmitpower

Admissioncontrolthreshold

Congestion

detection

and

resolution

parametersSchedulerparameters

Linklevelretransmissionschemeparameters

Trackingareaparameters

Switchingpointconfiguration

CQIthresholdsforMCEswitching

FrequencyReuse

Sectorisation

[email protected]; 125

Need to cope with sub-optimality arising from larger time

scale dynamics


126/133


Designing Ideal SON (1/3): Scalability


127/133

Scalabilitycan

be

achieved

through

Minimalcomplexity

Limitingthescopeofcooperationtolocalneighbourhoodonly

AsimplisticwayofdescribingscalabilityincontextofSOisthroughcondition

below.

Where O represents implementation complexity of an algorithm as function of

number of nodes n over which the algorithm needs control or coordination.

N is the total number of nodes in the system over which the objective of the

algorithm is to be achieved.

C is a constant that can be zero for perfectly scalable solution.


Designing Ideal SON (1/2): Stability


128/133

Stability has different precise meanings in different contexts but in this particular context of

SO we use a simplistic but generic definition of stability

An algorithm or adaptation mechanism is stable if it has finite number of states and finite

time to traverse all its states.

Mathematically we can write condition for stability as follows:

where S is set of all possible states in algorithm and is set of desired states only

such that and is arbitrary currant state such that S and is an

arbitrary future state such that 2 S .The symbol represents transition

from one state to other, P and T represent probability and times required for

these transitions. is a arbitrary bounded number.

dS

ls ms

bT


Designing Ideal SON (1/3): Agility


129/133

An adaptive system can be scalable and stable but still not perfectly self organisingas it might be sluggish in its adaptation. Agility is an other key characteristic of selforganizing systems (as observed in the case studies in above).

Agility describes how supple or acutely responsive an algorithm is in its adaptationto the changes in its operational environment, Mathematically:

Where Sis the total number of possible states to which the operational environment

of the algorithm can change, and it also represents the corresponding stages of thealgorithm. The time trepresents the duration associated to switching to a particularstate while superscripts a and e represent the algorithm and environmentrespectively.

It should noted that A=0 means system is not adaptive at all and A > 100% meanssystem is over agile i.e. it is not stable and can have oscillations.

Prefect agility is a tough condition to be met in real systems because of thefeedback, processing and decision making, and actuation delays involved in anyphysical system.


A non-exhaustive list of tools for SONS lf h li


130/133


NeuralNetworks

Fuzzylogic

Iterative

control

Unsupervised

LearningOpenloop

control

Geneticprogramming

Random

search

Perturbation

analysis

PIDcontrol

Model

predictive

control

Gradient

basedmethod

NonGradient

Based

method

Markov

Decision

process

Branch

Andbound

Clique

detectionStochastic

optimization

Chaos

theory

Gametheory

Combinatorial

optimization

Multiobjective

optimization

Combinatorial

optimization

Docitive

learning

Reinforced

Learning

Convex

optimization

Nonconvex

optimization

Prediction

theory

Interpolation

theory

Error

analysis

sensitivity

analysis

inference

analysis

EnablingSO Self

ConfigurationSelfoptimization

Selfhealing

Cellular

automata

130


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Thank you!

son- from conception to realisation

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