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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
alii@qmic.com; mischa.dohler@cttc.es
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
Outlinealii@qmic.com; mischa.dohler@cttc.com 2
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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
Self Configuration
Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 3
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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
Self Configuration
Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 4
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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
alii@qmic.com; mischa.dohler@cttc.com
An alternative definition
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Classifying SON Solutions:
Possible Taxonomies
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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
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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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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
Self Configuration
Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
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Why we want SO?
Moneymatters!
Growingnumberofnodes/Heterogeneity
Increasingoperational
complexityalii@qmic.com; mischa.dohler@cttc.com
SemistaticdesignofWCSbut
highlydynamicecosystem 16
Ok d t i i i th it 't th
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alii@qmic.com; mischa.dohler@cttc.com
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.
alii@qmic.com; mischa.dohler@cttc.com
Will LTE solve the dilemma?
Longtermsolution2025
http://www.3gpp.org/FutureRadioin3GPP300attend
Onlyin
terms
of
temporary
Needforspeed,
Maybeyes!
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alii@qmic.com; mischa.dohler@cttc.com
ARPB dilemma, a seriously Growing Concern for Operators!
TotalCost
of
Ownership(TCO)
Vs
revenue
not
adding
up!
Tsunamiofsmartphonessome notreallysmartapplicationsiswashingawaytheAverageRevenuePerBit(ARPB)!
OperatorsMAY chargefordata,butnotforcontrolsignaling
Energybillsaregoinghigherandhigher!
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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)
alii@qmic.com; mischa.dohler@cttc.com
Not Convinced, Need more Evidence?
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Why we want SO?
Moneymatters!
Growingnumberofnodes/Heterogeneity
Increasingoperational
complexityalii@qmic.com; mischa.dohler@cttc.com
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
Increasingoperational
complexityalii@qmic.com; mischa.dohler@cttc.com
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
alii@qmic.com; mischa.dohler@cttc.com
Which of the Operators processes SON can Eliminate?
LifeCycle
of
Cellular
System
CAPEX70%ofTCO
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alii@qmic.com; mischa.dohler@cttc.com
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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alii@qmic.com; mischa.dohler@cttc.com
How SON will benefit?
Database
Measurements
User Reports
based
Drive
test
based
Parameter update
Remote
In field
Performanceanalysis Optimalparameter
calculation
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alii@qmic.com; mischa.dohler@cttc.com
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?
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Quantifying SONs Value
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iGR forecasts a saving of:
2.34 billion in LTE Capex
4.5 billion in LTE Opex.alii@qmic.com; mischa.dohler@cttc.com
Any estimates on savings SON can provide?
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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
Self Configuration
Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research 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
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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
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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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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
Self Configuration
Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
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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.
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li
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
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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.
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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.
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O li
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Designtools
and
Challenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 54
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
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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?
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S lf O ti i ti
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alii@qmic.com; mischa.dohler@cttc.com
Self Optimization
[1]O.Aliu,A.Imran,M.Imran,andB.Evans,Asurveyofselforganizationinfuturecellularnetworks,CommunicationsSurveys Tutorials,IEEE,vol.PP,no.99,pp.126,2012.
58
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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 networks.alii@qmic.com; mischa.dohler@cttc.com 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
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O tli
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Design
tools
and
Challenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 61
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
alii@qmic.com; mischa.dohler@cttc.com
Industrys status on SON (1)
62
I d t t t SON (2)
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alii@qmic.com; mischa.dohler@cttc.com
Industrys status on SON (2)
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,
alii@qmic.com; mischa.dohler@cttc.com
Industrys status on SON (3)
SC:SelfConfiguration
ANR:AutomaticNeighborRelationshipSO:
Self
Optimization
SH:SelfHealing
MLB:MobilityLoadBalancing
MDT:MinimizingDeriveTestCODC:
Cell
Outage
Detect
and
CompensationICIC:
Inter
cell
Interference
Cancellation
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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
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Industrys status on SON (4)
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 reports.alii@qmic.com; mischa.dohler@cttc.com
Industrys status on SON (5)
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.
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Summary of SoA
67
Outline
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
Design
tools
and
Challenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 68
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
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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
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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.10031005,1970.alii@qmic.com; mischa.dohler@cttc.com 72
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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.10031005,1970.alii@qmic.com; mischa.dohler@cttc.com 73
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A General Framework for Designing Self Organisation: BSOF
Biomimmetic Self Organisation Framework (BSOF)
How?:C0:BSOF
Nature
Nurture
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A General Biomimetic Framework for Designing Self
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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:BSOFalii@qmic.com; mischa.dohler@cttc.com 75
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 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)
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C1: Problem formulation: Identifying SO-Objective
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C1: Problem formulation: Identifying SO-Objective
Maximise spectral efficiency in hotspots by optimizing tilt angles of all theN BS in the system
Average user throughput should not decrease
Subject to:
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C1: Problem formulation: Identifying SO-Objective
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
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C1: Achieving SO solution via BSOF
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C1: Achieving SO solution via BSOFSO-Objective SO-Goal SO-Function
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C1: Achieving SO solution via BSOF
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C1: Achieving SO solution via BSOF
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
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C1: Achieving SO solution via BSOF
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C1: Achieving SO solution via BSOF
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
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C1 E bli th ti f SO F ti
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C1: Enabling the execution of SO-Function
Subject to:
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C1 E bli th ti f SO F ti
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C1: Enabling the execution of SO-Function
Subject to:
C1:TO BSOF
And
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C1 E bli th ti f SO F ti
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C1: Enabling the execution of SO-Function
Subject to:
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S stem Le el Sim lation Parameters
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System Level Simulation Parameters
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Simulation Scenario
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Simulation Scenario
87
C1: Numerical results: 1 Triplet scenario
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C1: Numerical results: 1-Triplet scenario
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C1: System level simulation results (1)
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C1: System level simulation results (1)
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C1: System level simulation results (2)
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C1: System level simulation results (2)
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C1: System level simulation results (3)
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C1: System level simulation results (3)
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C2: System level simulation results (2)
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ExpectedormeanspectralefficiencyforallusersinthewholeareaofWCSandusersinthehotspotsC1:TO BSOF
45%
gain
20%gain
alii@qmic.com; mischa.dohler@cttc.com
Case
1:50%user
Hotpot
Case2:
80%users
inHotspot
92
Summary of TO-BSOF
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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
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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
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SO Solution for Load Balancing
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LB-BSOF
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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 BSOFalii@qmic.com; mischa.dohler@cttc.com
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Achieving SO solution through BSOF
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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
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Super cell concept in LB-BSOF
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p p
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A heuristic algorithm for LB-BSOF
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A heuristic algorithm for LB BSOF
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Simulation scenarios and parameters
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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)
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Call rejection Log
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j g
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Call rejection Log
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j g
>2timesincreaseusercapacity
forsame
GoS
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LB-BSOF Gain
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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
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C3: Long term Multiobjective optimization through BSOF
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g j p g
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Some Important consideration over long term
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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)
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Performance characterisation Framework (PCF) for WCS over long term
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( ) g
Capacity
QoS
Economy
WCS KPI
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Spectral efficiency of given heterogeneous network
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p y g g
107
Capacity of FD
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p y
108
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Visual illustration
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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)
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EC of FD
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Performance characterisation Framework (PCF) for WCS over long term
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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
2359,2630Sept.2010alii@qmic.com; mischa.dohler@cttc.com112
Numerical results for PCF
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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
alii@qmic.com; mischa.dohler@cttc.com 113
SOFD
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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:SOFDalii@qmic.com; mischa.dohler@cttc.com 114
Solution space for SO-GoalsOptimal FD in terms of SAF
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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
alii@qmic.com; mischa.dohler@cttc.com 115
Outline
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
DesigntoolsandChallenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 116
Self healing concept
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-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.
alii@qmic.com; mischa.dohler@cttc.com 117
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-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
alii@qmic.com; mischa.dohler@cttc.com 118
Outline
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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
Self Configuration Self Optimization:
SON for short term dynamics
SON for Medium term dynamics
SON for Long term dynamics
Self Healing
Some Selected Solutions
EnablingSON
NeedforSelfCoordination
DesigntoolsandChallenges
Open Research Challenges
Outlinealii@qmic.com; mischa.dohler@cttc.com 119
Attention need to be channeled to Enabling solutions for SON
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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
alii@qmic.com; mischa.dohler@cttc.com120
Need for holistic SON Framework
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alii@qmic.com; mischa.dohler@cttc.com 121
Use the problem generatorsto solve the problem
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Time
TrafficA
TrafficB
alii@qmic.com; mischa.dohler@cttc.com 122
SON as enabler of high QoE at low cost, in future CS
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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
alii@qmic.com; mischa.dohler@cttc.com 123
OptimizationObjectives Configuration/Optimization
Parameter(s)
Minimizeinterference Transmitpower(cellsize)
RBassignment(scheduling)
Adjustbeamformingparameters
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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
Antennatiltsalii@qmic.com; mischa.dohler@cttc.com 124
OptimizationObjectives Configuration/Optimization
Parameter(s)
Minimizeinterference Transmitpower(cellsize)
RBassignment(scheduling)
Adjustbeamformingparameters
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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
Antennatiltsalii@qmic.com; 125
Need to cope with sub-optimality arising from larger time
scale dynamics
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alii@qmic.com; mischa.dohler@cttc.com 126
Designing Ideal SON (1/3): Scalability
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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.
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Designing Ideal SON (1/2): Stability
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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
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Designing Ideal SON (1/3): Agility
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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.
alii@qmic.com; mischa.dohler@cttc.com 129
A non-exhaustive list of tools for SONS lf h li
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alii@qmic.com; mischa.dohler@cttc.com
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!