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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

    WikipediaSelforganisa2onisaprocessofa4rac2onandrepulsioninwhichthe

    internalorganiza2onofasystem,normallyanopensystem,increasesincomplexitywithoutbeingguidedormanagedbyanoutsidesource.

    Anothera4empt(inthespecificcontextoftelecommunica2onnetworks)

    Selforganisa2onistheautomated(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 chartics, QoS reqments 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 eNode

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