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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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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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SELF-ORGANISATION IN HANOI TRAFFIC

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QUESTIONS?