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Self-optimisation in future mobile access networks
Remco Litjens Senior scientist TNO Information and Communication Technology Delft, The Netherlands
Mobile Network Optimisation 2008 November 4, 2008
Hôtel Palais Stéphanie, Cannes, France
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• Current networks are largely manually operated • Separation of network planning and optimisation • (Non-)automated planning tools applied
• Site selection, optimisation of 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, FP7, …
INTRODUCTION
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• Technogical perspective • Complexity of future/contemporary wireless access networks
• Multitude of tuneable parameters with intricate dependencies • Multitude of RRM 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
DRIVERS
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• 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
DRIVERS
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• Minimise human involvement in planning/optimisation
• Significant automation of network operations
• Key components • Self-configuration • Self-healing • Self-optimisation
VISION
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• Self-configuration • Incidental, intentional events • ‘Plug and play’ installation of
new base stations or features • E.g. download of initial radio
network parameters, neigh- bour list generation, trans- port network discovery and configuration, …
• Self-healing • Incidental, non-intentional events
• Automatic fault detection • Automatic minimisation of
coverage/capacity loss in case of cell/site failures
• Enhanced robustness/resilience • Alarm bells
VISION
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• Self-optimisation • Measurements
• Gathering via terminals, eNBs, probes • Propagation, traffic, mobility aspects • Performance indicators
• Continuous self-optimisation of radio parameters
• In response to observed changes in conditions and/or performance
• In order to provide service availability and quality targets most efficiently
• Smart on-line algorithms • E.g. tilt, azimuth, power,
RRM thresholds, scheduling weights, neighbour cell lists
• Triggers/suggestions in case capacity expansion is unavoidable
VISION
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• 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
EXPECTED GAINS
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• … and/or CAPEX reductions … • Via delayed capacity expansions • Smart eNodeBs may however be more expensive
• … and/or performance enhancements • Enhanced service availability, service quality
EXPECTED GAINS
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• … and/or CAPEX reductions … • Via delayed capacity expansions • Smart eNodeBs may however be more expensive
• … and/or performance enhancements • Enhanced service availability, service quality
EXPECTED GAINS
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• 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
USE CASES
• 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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• 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
USE CASES
• 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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• 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
USE CASES
AUTOMATIC NEIGHBOUR CELL LIST GENERATION EXAMPLE
A: {B,C,D}
C: {A,D}
D: {A,B,C,E}
E: {B,D}
B: {A,D,E}
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• Self-optimisation use case • Key radio resource management mechanism in LTE • All traffic all multiplexed over shared channels
• Distinct QoS requirements • Rate requirements, latency tolerance, elasticity
• A typical scheduler integrates proportional fairness and deadline-based principles
• With various tunable parameters, e.g. • Capacity sharing between services • Degree of proportional fairness • Subscription-based priorities
• Self-optimisation based on • Observed performance or efficiency issues • Observed ‘environmental’ changes
• Traffic characteristics, traffic mix, spatial distribution • User mobility • Propagation effects
USE CASES
PACKET SCHEDULING OPTIMISATION EXAMPLE
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• Self-healing use case • Cell outage detection
• Automatic detection of failures • eNodeB failure, cell failure, physical signal/channel failure • 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, …
USE CASES
CELL OUTAGE MANAGEMENT EXAMPLE
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• Self-healing use case • Cell outage compensation
• Automatic compensation of failures • Optimise ‘regional’ coverage, capacity and/or quality
• Control parameters • Power settings • Downtilt, azimuth(?) • Intra/inter-RAT handover parameters, load balancing • Neighbour cell lists
USE CASES
CELL OUTAGE MANAGEMENT EXAMPLE
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• Self-healing use case • Gains
USE CASES
CELL OUTAGE MANAGEMENT EXAMPLE
local revenue
time
manual detection
time
eNodeB dies
eNodeB revived
repair time
CASE WITHOUT SELF-HEALING
local revenue
time
repair time
CASE WITH SELF-HEALING regained revenue due to
cell outage compensation
regained revenue due to cell outage detection
otherwise missed revenue
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• Development of effective self-organisation methods imposes quite a few challenges
• Measurements • What data? What frequency? • 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
CHALLENGES
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• 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
CHALLENGES
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• 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
• 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
WNO IS WHO?
NGMN
now finished follow-up project
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• Standardisation of E-UTRAN (LTE) • Self-Optimising Networks
• Introduced in Q2 ‘07 • Primary objective is to reduce OPEX • Self-configuration, self-optimisation, cell outage compensation • Considered in SA2/5, RAN1/2/3
• Releases • R8 (finalised Q1/2009)
• Some auto-configuration concepts included, incl. ‘automatic neighbour relation’, automatic physical cell ID discovery
• No new use case, just closing holes … • R9/10 (R9 tentatively finalised 12/2009)
• No concrete plans yet, but expected to consider load balancing, handover optimisation coverage hole/cell outage management, interference reduction, capacity/coverage optimisation
WHO IS WHO?
3GPP
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• 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
WHO IS WHO?
SOCRATES
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• 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
WHO IS WHO?
SOCRATES
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• 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
WHO IS WHO? SOCRATES
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• 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
WHO IS WHO?
SOCRATES
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• Work packages • WP1 ‘Project management’ • WP2 ‘Use cases and framework for self-organisation’
• Use cases, requirements • Assessment criteria • Development framework
parameter groups and relations, relations between SO-SC-SH • WP3 ‘Self-optimisation’
• Development and assessment of algorithms • Impact on measurements, architecture and interfaces
• WP4 ‘Self-configuration and self-healing’ • Development and assessment of algorithms • Impact on measurements, architecture and interfaces
• WP5 ‘Integration, demonstration and dissemination’ • Integration, demonstration, implications,
exploitation, liaisons to 3GPP/NGMN, dissemination
FIN
ISH
ED
ON
-GO
ING
WHO IS WHO?
SOCRATES
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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
WHO IS WHO?
SOCRATES
*pending approval
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OUTLINE
• INTRODUCTION
• DRIVERS • VISION • EXPECTED GAINS • USE CASES • CHALLENGES • WHO IS WHO? • CONCLUDING REMARKS
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• Self-organisation is gaining attention … • … for
• O/CAPEX reduction • enhancement of capacity, coverage, quality
• … by • 3GPP: standardisation of protocols, architecture,
interfaces, measurements • NGMN: operators’ vision based on use cases;
discussion of vendor proposals • FP7: development, assessment, demonstration of algorithms
derivation of impact on standards and network operations
CONCLUDING REMARKS