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FP7 ICT-SOCRATES
Self-organisation in future mobile cellular
networks
Remco Litjens TNO ICT, Delft, The Netherlands
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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INTRODUCTION
WikipediaSelf‐organisa2onisaprocessofa4rac2onandrepulsioninwhichthe
internalorganiza2onofasystem,normallyanopensystem,increasesincomplexitywithoutbeingguidedormanagedbyanoutsidesource.
Anothera4empt(inthespecificcontextoftelecommunica2onnetworks)
Self‐organisa2onistheautomated(withouthumaninterven2on)adapta2onorconfigura2onofnetworkparameters(inabroadsense),in
responsetoobservedchangesinthenetwork,traffic,environmentcondi2onsand/orexperiencedperformance.
Someexamplesmayhelp…
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SELF-ORGANISATION IN EXISTING NETWORKS
Example 1: ‘Transmission Control Protocol’ – Operates end-to-end on the transport layer – Automatically adapts source transfer rate to end-to-end congestion level – Limits amount of data in transit – Slow start phase is followed by congestion avoidance phase
• AIMR Additive Increase, Multiplicative Decrease
PHY
MAC/RLC
IP
TCP
PHY
MAC/RLC
IP
PHY
MAC/RLC
IP
PHY
MAC/RLC
IP
TCP
SOURCENODE
DESTINATIONNODE
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SELF-ORGANISATION IN EXISTING NETWORKS
Example 1: ‘Transmission Control Protocol’ – Operates end-to-end on the transport layer – Automatically adapts source transfer rate to end-to-end congestion level – Limits amount of data in transit – Slow start phase is followed by congestion avoidance phase
• AIMR Additive Increase, Multiplicative Decrease
PACKETTIMEOUT:CONGESTIONWINDOW/2
NOPACKETTIMEOUT:CONGESTIONWINDOW+1
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SELF-ORGANISATION IN EXISTING NETWORKS
Example 2: ‘Routing in ad hoc networks’ – Automatic detection of connectivity – Automatic establishment of routes – Automatic rerouting upon node failure
DESTINATIONNODE
SOURCENODE
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SELF-ORGANISATION IN EXISTING NETWORKS
Example 2: ‘Routing in ad hoc networks’ – Automatic detection of connectivity – Automatic establishment of routes – Automatic rerouting upon node failure
DESTINATIONNODE
SOURCENODE
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innerlooppowercontrol
outerlooppowercontrol
SELF-ORGANISATION IN EXISTING NETWORKS
SINRBLER
UE
Example 3: ‘Uplink transmit power control in UMTS networks’ – Default case
• Fixed transmit power Near-far effect! Battery drainage! – 1st Self-optimisation loop
• Adjust transmit power to meet SINR target • Responds to multipath fading variations
– 2nd Self-optimisation loop • Adjust SINR target to meet BLER target • Adapts to user velocity
– 3rd Self-optimisation loop • Adjust BLER target to meet
end-to-end packet loss target? • Adjust power control steps? • Adjust power control heartbeat?
NodeB RNC
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SELF-ORGANISATION IN EXISTING NETWORKS
Example 3: ‘Uplink transmit power control in UMTS networks’
transmitpower
pathgain
outerlooppowercontrolrespondstoavelocityincrease
innerlooppowercontrolfollowsmul2pathfading
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INTRODUCTION
Context of this presentation – Mobile cellular communications networks – LTE access technology
• Long Term Evolution (E-UTRAN) • Currently under standardisation • Focus on radio access network
1989
OBLB 1980
NMT 900
1985
NMT 450
2006
2003
UMTS
+ HSPA
2001
GSM
1994
+ GPRS
LTE
2011
?
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INTRODUCTION
Current networks are largely manually operated – Separation of network planning and optimisation – (Non-)automated planning tools used to select sites, radio parameters
• ‘Over-abstraction’ of applied technology models – Manual configuration of sites – Radio (resource management) parameters updated weekly/monthly
• Performance indicators with limited relevance • Time-intensive experiments with limited operational scope
– Delayed, manual and poor handling of cell/site failures
Future wireless access networks will exhibit a significant degree of self-organisation
– Self-configuration, self-optimisation, self-healing, …
Broad attention – 3GPP, NGMN, SOCRATES, …
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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DRIVERS
Technogical perspective – Complexity of future/contemporary wireless access networks
• Multitude of tuneable parameters with intricate dependencies • Multitude of radio resource management mechanisms on different time scales • Complexity is needed to maximise potential of wireless access networks
– Higher operational frequencies • Multitude of cells to be managed
– Growing suite of services with distinct char’tics, QoS req’ments – Heterogeneous access networks to be cooperatively managed – Common practice in network planning and optimisation → labour-intensive operations delivering suboptimal solutions!
Enabler – The multitude and technical capabilities of base stations and terminals to
perform, store, process and act upon measurements increases sharply
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DRIVERS
Market perspective – Increasing demand for services – Increasing diversity of services
• Traffic characteristics, QoS requirements – Need to reduce time-to-market of innovative services
• Reduce operational hurdles of service introduction – Pressure to remain competitive
• Reduce costs (OPEX/CAPEX) • Enhance resource efficiency • Keep prices low
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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VISION
Minimise human involvement in planning/optimisation
Significant automation of network operations
Key components – Self-configuration – Self-healing – Self-optimisation
triggeredbyincidentalevents
con2nuousloop
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VISION
Self-configuration – Incidental, intentional events – ‘Plug and play’ installation
of new base stations and features • Download of initial radio
network parameters, neigh- bour list generation, trans- port network discovery and configuration, …
Self-healing – Incidental, non-intentional events – Cell outage detection
• Alarm bells • Triggers compensation
– Cell outage compensation • Automatic minimisation
of coverage/capacity loss
triggeredbyincidentalevents
con2nuousloop
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VISION
Self-optimisation – Measurements
• Performance indicators • Network, traffic, mobility,
propagation conditions • Gathering via UEs, eNodeBs, probes • Optimal periodicity, accuracy,
format depends on parameter/ mechanism that is optimised
– Automatic tuning • Smart algorithms process
measurements into para- meter adjustments
– E.g. tilt, azimuth, power, RRM parameters, NCLs
– In response to observed changes in conditions and/or performance
– In order to provide service avai- lability/quality most efficiently
• Triggers/suggestions in case capacity expansion is unavoidable
triggeredbyincidentalevents
con2nuousloop
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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EXPECTED GAINS
OPEX reductions … – Primary objective! – Less human involvement in
• Network planning/optimisation • Performance monitoring, drive testing • Troubleshooting
– About 25% of OPEX is related to network operations • x00 million € savings potential per network
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EXPECTED GAINS
… and/or CAPEX reductions … – Via delayed capacity expansions – Smart eNodeBs may however be more expensive
… and/or performance enhancements – Enhanced service availability (robustness/resilience), service quality
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EXPECTED GAINS
… and/or CAPEX reductions … – Via delayed capacity expansions – Smart eNodeBs may however be more expensive
… and/or performance enhancements – Enhanced service availability, service quality
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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USE CASES
Definition of use cases – To guide development of solutions
• Algorithms • Performance aspects • Impact on standards and operations
– To help determine requirements • Technical requirements
– Performance – Complexity – Stability/robustness – Timing – Interaction – Architecture/scalability – Required measurements
• Business requirements – Faster roll-out of LTE networks – Simplified operational processes – Easy deployment of new services – End user quality/cost benefits
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USE CASES
Non-exhaustive use case list – Self-optimisation
• Radio network optimisation – Interference coordination – Self-optimisation of physical channels – RACH optimisation – Self-optimisation of Home eNodeB
• GOS/QoS-related optimisations – AC/CC/PS optimisation – Link level retx scheme optimisation – Coverage hole detection/compensation
• Handover related optimisation – Handover parameter optimisation – Load balancing – Neighbour cell list
• Others – Reduction of energy consumption – TDD UL/DL switching point – Management of relays and repeaters – Spectrum sharing – MIMO
– Self-configuration • Automatic NCL generation • Intell. selecting site locations • Automatic generation of default
parameters for NE insertion • Network authentication • Hardware/capacity extension
– Self-healing • Cell outage prediction • Cell outage detection • Cell outage compensation
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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USE CASE: AUTOMATIC NEIGHBOUR CELL LIST GENERATION
Self-configuration/-optimisation use case – NCL indicates potential handover target cells – Typically limited to 32 cells – Missing neighbours induces call
dropping or excessive interference – Undesired neighbours cause
unnecessary measurements
Self-optimisation based on e.g. – UE’s signal strength reports – eNB scans of neighbours – Call drops, handover failures – Handover stats: used neighbours
Triggers – Site/cell addition – Poor performance – Periodic optimisation
A: {B,C,D}
C: {A,D}
D: {A,B,C,E}
E: {B,D}
B: {A,D,E}
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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USE CASE: SELF-OPTIMISATION OF ADMISSION CONTROL
Admission control – Key radio resource management mechanism – Objective is to admit as many calls as possible such
that service quality requirements can be satisfied – Typical admission control rule
admissionthresholdforHO/RTadmissionthresholdfor……
dis2nctmarginsfore.g.FR/RT,FR/NRT,HO/RT,HO/NRT,…
cellcapacityc(t)
2me
currentloadnewcallload
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USE CASE: SELF-OPTIMISATION OF ADMISSION CONTROL
Key challenges – How to determine cell capacity = non-trivial!! – How to determine the new call load = non-trivial!! – How to set the margins?
• Too low means inadequate QoS • Too high means excessive blocking
• Save resources for unpredictable/ uncontrollable variations in resource usage
– User activity – User mobility location affects resource usage – Propagation effects
• Give sufficient preference to handover calls – ‘Sufficient’ depends on degree of mobility, which may vary during the day/week
• Give sufficient preference to real-time calls – Little tolerance w.r.t. (temporary) QoS degradation – Optimal margin depends on occasional downgradability of non-real-time traffic
– Self-optimisation!!
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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USE CASE: CELL OUTAGE MANAGEMENT
33/20 33/20
MeasurementsDetec2on
Compensa2on
Operatorpolicy:Coverage,QoS
Controlparameters
Cov.mapes2ma2on
Self-healing use case
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USE CASE: CELL OUTAGE MANAGEMENT
Cell outage detection – Cell outages
• eNodeB failure, cell failure, physical signal/channel failure • May not be detected for hours or even days • May require manual analysis and unplanned site visits
– Automatic detection of failures • Generate alarms for automated compensation and manual repair • Indicate location, type
and urgency of outage • Minimise detection time,
probability of missed detection and false alarm
– Measurements • UE measurement reports:
pilots, interference levels • eNB hard/software reports,
carried load, call drops, …
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USE CASE: CELL OUTAGE MANAGEMENT
Cell outage compensation – Automatic compensation of failures
• Optimise ‘regional’ coverage, capacity and/or quality – Control parameters
• Power settings • Downtilt, azimuth(?) • Beamforming • Scheduler’s fairness parameter • Intra/inter-RAT handover
parameters, load balancing • Neighbour cell lists
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USE CASE: CELL OUTAGE MANAGEMENT
Achieved gains
local revenue
time
CASE BEFORE SITE OUTAGE
manual detection
time
eNodeB dies
eNodeB revived
repair time
CASE WITHOUT SELF-HEALING
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local revenue
time
repair time
CASE WITH SELF-HEALING
otherwise missed revenue
USE CASE: CELL OUTAGE MANAGEMENT
Achieved gains
local revenue
time
manual detection
time
eNodeB dies
eNodeB revived
repair time
CASE WITHOUT SELF-HEALING
regained revenue due to cell outage compensation
regained revenue due to cell outage detection
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USE CASE: CELL OUTAGE MANAGEMENT
Scenarios for cell outage compensation – Impact of eNodeB density and load
• More compensation potential in a dense capacity-driven network layout – Impact of service type
• More compensation potential in an area with predominantly low-bandwidth service, e.g. VoIP telephony
– Impact of outage location • More compensation potential if a
cell/site outage occurs at the inner part of an LTE island
– Also study impact of • user mobility, propagation
aspects, spatial traffic distribution, UE class
Controllability & observability Algorithm development Impact on 3GPP specifications
On‐goingwork
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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USE CASE: SELF-OPTIMISATION OF HOME eNodeBs
Home eNodeBs – Femto-cell deployed to enhance coverage/QoS in homes, offices, … – Low-cost WLAN-type base station, connected to DSL broadband modem – Operates LTE radio access technology in operator’s licensed spectrum – Used to improve coverage and/or capacity in small areas – Closed versus open access
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USE CASE: SELF-OPTIMISATION OF HOME eNodeBs
Key characteristics – Potentially large number of home eNodeBs – Small coverage areas, typically few users – May be turned on/off and moved frequently – Not physically accessible for operators – Operate on a same/separate frequency from macro-cells
self-configuration/ -optimisation needed
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USE CASE: SELF-OPTIMISATION OF HOME eNodeBs
Key challenge: interference/coverage optimisation – Objectives
• Coverage should be large enough to include e.g. attic, balcony, garden, … • Interference caused to macro-cell should be limited • Example: UE in femto-cell coverage area but not allowed access may cause/
experience excessive interference to/from femto-cell • Example: UE served by femto-cell experience severe DL interference from macro-
cell, while macro-cell may experience significant UL interference from femto-cell – Control parameters
• Up/downlink transmit power • Scheduling parameters, e.g. time-frequency domain separation • MIMO parameters
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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CHALLENGES
Development of effective self-organisation methods imposes quite a few challenges
– Measurements • What data? What frequency?
Tuned to urgency? • Trade-off: signalling cost
vs achieved performance • Appropriate processing to
determine ‘network state’ • Detection/handling of erroneous/
malicious reports – Effectiveness of self-organisation
• Multi-objective optimisation • Intricate parameter dependencies • Frequency of adjustments • Mutual timing → prevent oscillations • Centralised vs distributed control • Timely detection, swift response
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CHALLENGES
Development of effective self-organisation methods imposes quite a few challenges
– Dealing with delayed feedback • Feedback upon control actions
is not immediate • Effects of control decisions
or due to natural variations – Reliability
• Actions must be reliable • No human sanity checks or
revision of actions • Operator must trust the system
when giving up direct control – Gradual introduction
– Shape the network architecture • Incorporation in actual systems • Protocols, interfaces, architecture
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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SELF-OPTIMISATION APPROACHES
Generic approach
AlgorithmAssessment
AssessmentCriteria
MeasurementsControl
Parameters
OperatorPolicy
Controllability&Observability
AlgorithmSpecifica2on
Scenarios
AlgorithmDevelopment
3GPPspecifica2on
Implementa2on
methodologies
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SELF-OPTIMISATION APPROACHES
Generic approach – In the process of algorithm development, the Real Network can be
replaced by a simulator
Networkparameters
RealNetwork
NetworkSta2s2cs
Op2misa2onAlgorithm
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SELF-OPTIMISATION APPROACHES
Approach 1: ‘Simulation-based optimisation’ – Optimisation Algorithm exploits a Network Simulator for ‘what if’ analyses,
i.e. test potential parameter adjustments before application in the Real Network
• Equivalent to current approach to off-line optimisation • Specific problem imposes requirements on speed and accuracy
Networkparameters
RealNetwork
NetworkSta2s2cs
Op2misa2onAlgorithm
NetworkSimulator
PredictedNetworkSta/s/cs
ProposedParameterSe3ngs
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SELF-OPTIMISATION APPROACHES
Approach 2: ‘Off-line optimised real-time controller’ – Real-Time Controller rapidly responds to changes – Periodic off-line tuning of Real-Time Controller by Optimisation Algorithm
• Particularly suitable when quick response is needed • Real-Time Controller may largely simplify dependencies/impact
– Reliable, stable but suboptimal for complex use cases?
Networkparameters
RealNetwork
NetworkSta2s2cs
Real‐TimeController
Op2misa2onAlgorithm
UpdatedControllerSe3ngs
Controller’sPerformanceSta/s/cs
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SELF-OPTIMISATION APPROACHES
Approach 3: ‘On-line optimised real-time controller’ – Periodic/continuous on-line tuning of Real-Time Controller
• Example combining admission control and reinforcement learning – Reference CAC scheme with an RL-based self-optimisation layer on top,
optimising the CAC parameters – Integrated RL-based CAC scheme, directly optimising the mapping of system
state to admission/rejection decision
• Involves random intialisation and training phase with randomised actions • Inherent degree of ‘black box character’ limits ‘trustworthiness’
Networkparameters
RealNetwork
NetworkSta2s2cs
Adap2veReal‐TimeController
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ProposedNetworkParameters
SELF-OPTIMISATION APPROACHES
Approach 4: ‘Adaptive model-based optimisation’ – Optimisation Algorithm exploits a Network Model for ‘what if’ analyses
(assess performance impact of potential parameter adjustments) or direct derivation of the optimal parameters, before parameter application in the Real Network. The Network Model is tuned based on measurements.
• For example, feeding estimated propagation/coverage maps into automated planning tool in order to optimise tilts and azimuths
Networkparameters
RealNetwork
NetworkSta2s2cs
Adap2veNetworkModel
Op2misa2onAlgorithm
PredictedNetworkSta/s/cs
‘Solve’themodel
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Non-exhautive list of potentially applicable optimisation techniques
SELF-ORGANISATION METHODOLOGIES
randomsearch
samplepath ordinal
op2misa2on
perturba2onanalysis
branch&bound
metamodels cliquedetec2on
gradientbasedmethods
non‐gradientbasedmethods
open‐loopcontrol
propor2onal‐integral‐deriva2vecontrol
modelpredic2vecontrol
itera2velearningcontrol
gene2cprogramming
neuralnetworks
Markovdecisionprocesses
neuralswarming
reinforcementlearning
fuzzylogic
expertsystems
automa2ccontrol
fuzzyQ‐learning
neuro‐evolu2onofaugmen2ngtopologies
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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NGMN
Cooperation among world-leading mobile network operators
General objective – To collect and promote operator
requirements and recommendations for future mobile networks
– Establish clear performance targets, fundamental recommendations and deployment scenarios
‘Operational Efficiency’ project – SON WP is a continuation of ‘Project 12’ – Develop operator vision on self-organisation – Ensure that self-organisation capabilities become
an inherent part of the initial design of future systems – Push vendors to fulfil operator requirements regarding
the implementation of particular solutions – Concrete activities include
• Industry conferences, vendor workshops • White papers, 3GPP contributions
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3GPP
Standardisation of E-UTRAN (LTE) Self-Optimising Networks
– Introduced in Q2 ‘07 – Considered primarily
in TSGs RAN2/3, SA5 Releases
– R8 (frozen) • Some auto-configuration concepts included – Automatic neighbour relation,
Automatic physical cell ID discovery – R9 (tentatively frozen 12/2009)
• Key RAN3 work items – Coverage and capacity optimisation, Interference reduction, Load balancing, Coverage hole management and cell outage management, Handover optimisation
See also – ‘Self-configuring and self-optimizing network use cases and solutions’,
3GPP Technical Report 36.902, maintained by RAN3
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SOCRATES
Overview – Self-Optimisation and self-ConfiguRATion in wirelEss networkS
• Self-configuration, self-optimisation, self-healing – 3-year duration: from 01/01/2008 until 31/12/2010 – Effort: 378 person months, € 4.980.433 – EU IST FP7-ICT-2007-1
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SOCRATES
Scope – Technological focus: 3GPP E-UTRAN (LTE) – Radio (resource management) parameters, e.g. pilot power, antenna tilt,
neighbour cell lists, SHO/CC/CAC/scheduling parameters, … Consortium
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SOCRATES
Objectives – Development of novel concepts, methods and algorithms for the effective
self-organisation of wireless access networks – Specification of the required information, its statistical accuracy and the
methods of retrieval incl. the needed protocol interfaces – Validation and demonstration of the developed concepts and methods for self-
organisation through extensive simulation experiments, assessing the established capacity/coverage/quality enhancements, and the attainable O/CAPEX reductions
– Assessment of the operational impact of the developed concepts and methods for self-organisation, with respect to the network operations, e.g. radio network planning and capacity management processes
– Influence on 3GPP standardisation and NGMN activities
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SOCRATES
Evolutionary approach Quantitative character
– Development of methods and algorithms – Quantitative assessment – Simulation of scenarios
Contacts and cooperation – FP7 E3, 4WARD, EFIPSANS, EURO-NF, …. – COST 2100 – 3GPP, NGMN, WWRF
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Come and see us at the joint workshop* on
‘Self-organisation for beyond 3G wireless networks’
at ICT Mobile Summit ’09 in Santander, Spain
SOCRATES
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OUTLINE
Introduction Drivers Vision Expected gains Use cases
– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
Challenges Approaches Who is who? Concluding remarks
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CONCLUDING REMARKS
Self-organisation key approach to … – … reduce O/CAPEX – … cost-effective provisioning of high-quality services – … reduce time-to-market of new features, services
Key components – Self-configuration – Self-optimisation – Self-healing
Exciting challenges – Effectiveness, reliability, stability – Measurements, interfaces, protocols, architectures
Involved parties/projects – NGMN, 3GPP, GANDALF, E3, SOCRATES, …, you?
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