Self-optimisation in future mobile access networks MNO... · Self-optimisation in future mobile access networks Remco Litjens ... • Manual configuration of sites ... • End user

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

WWW.FP7-SOCRATES.EU 2

OUTLINE

•  INTRODUCTION

•  DRIVERS •  VISION •  EXPECTED GAINS •  USE CASES •  CHALLENGES •  WHO IS WHO? •  CONCLUDING REMARKS


OUTLINE

•  INTRODUCTION



•  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


OUTLINE

•  INTRODUCTION



•  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


•  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


OUTLINE

•  INTRODUCTION



•  Minimise human involvement in planning/optimisation

•  Significant automation of network operations

•  Key components •  Self-configuration •  Self-healing •  Self-optimisation

VISION


•  Self-configuration •  Incidental, intentional events •  ‘Plug and play’ installation of

new base stations or features •  E.g. download of initial radio

network parameters, neighbour 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


•  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


OUTLINE

•  INTRODUCTION



•  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


•  … 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


•  … 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


OUTLINE

•  INTRODUCTION



•  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


•  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


•  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}


•  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


•  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


•  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



•  Self-healing use case •  Gains

USE CASES


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


OUTLINE

•  INTRODUCTION



•  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


•  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


OUTLINE

•  INTRODUCTION



•  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


•  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


•  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


•  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


•  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


•  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


•  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


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


OUTLINE

•  INTRODUCTION



•  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


Self-optimisation in future mobile access networks MNO... · Self-optimisation in future mobile access networks Remco Litjens ... • Manual configuration of sites ... • End user

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