FP7 ICT-SOCRATES

Algorithms for Self-

optimisation

Presented by Ove Linnell (Ericsson)

9th June 2009

ICT Mobile Summit 2009 Pre-conference Workshop on ‘Self-Organisation for Beyond

3G Wireless Networks’

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Ove Linnell, Ericsson Research

Outline

�Self-optimisation in SOCRATES

�Self-optimisation principles

�Selected use cases– Self-optimisation of Home eNodeB

– Packet scheduling parameter optimisation

– Admission & Congestion control

– Load Balancing

– Interference Coordination

– Handover

� Interacting Self-Optimisation algorithms

�Summary / Conclusion

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Self-Optimisation in SOCRATES

General definition:Optimise the performance of the network through automatic or semi-automatic features,

to save costs, running the networkto enhance the performance of the network

Measurements(Gathering and processing)

Self-optimisation

Settingparameters

Self-healing

Self-configuration

continuous

loop

triggered by incidental

events

Measurements(Gathering and processing)

Self-optimisation

Settingparameters

Self-healing

Self-configuration

continuous

loop

triggered by incidental

events

SOCRATES approach:

Develop new concepts, methods and algorithms for efficient self-optimisation of wireless networks that adapts to gradual changes in the radio network,

� with focus on LTE

� real-time optimisation on a time scale ranging from monthly or weekly to hours or minutes

� Optimise on a wider range of radio parameters(local optimisation � system optimisation)

� Quick response to network changes to resulting in optimisation that best suits current network situation

� Strong gains in coverage, capacity and quality of service, e.g. no need for drive tests.

Self-optimisation use cases in SOCRATES:

� Home eNodeB

� Packet scheduling

� Admission & congestion control

� Load Balancing

� Interference Coordination

� Handover

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Network

Self-Optimization

Self-HealingSelf-Configuration

Observation

Action

Anomaly detection

Install

New

HW

Eternal cycle

Policy

Self-optimisation principles

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� Aspects to consider

– How local is the problem? Single cell, regional or network-wide?

– How local is the impact of control actions?

– How isolated are the involved control parameters w.r.t. other mechanisms?

– How quick is a response needed? Within (milli)seconds, minutes?

– How much computational power is available?

– What degree of knowledge regarding the network is assumed by the algorithm?

– What is the impact on network during learning phase?

� A way forward when selecting Self-optimisation principles,

– First get it quickly roughly right off-line using

– An optimisation tool

– Measurement-tuned propagation maps are used

– Expert-based rule of thumb

– Then fine-tune control parameters

– On-line optimised real-time controller that is allowed only gradual

parameter adjustments within a limited range

Self-optimisation principles

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Self-optimisation of Home eNodeB

� Up to 70% of the traffic load is indoor

� Support open and closed subscriber groups

� Minimal human intervention

� Selected sub-use cases

– HO optimisation

– HO between macro and Home eNodeB should work smoothly and

seamless

– Probably the user wants to move to Home eNodeB as quick as

possible and stay there as long as possible

– Interference and Coverage Optimisation

– Minimize interference to macro in order not to deteriorate macro

performance

– Avoid coverage holes

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Packet scheduling parameter optimisation

� Appropriately tune the parameters of the packet scheduling algorithm in response to changes,

– in e.g., traffic load, traffic mix, traffic characteristics, interference

characteristics, etc.

– such that channel resources are optimally used

– such that a balanced QoS is offered to all users

� Reference algorithm selected

� The focus is on the sensitivity of the optimum parameter settings of the scheduler with scenarios like :

– data only

– spatially inhomogeneous traffic (hot spots)

– mobility (sudden changes in a user’s channel condition, e.g. due to

shadowing)

– mixed traffic: video/data

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Admission & Congestion control

� Self-optimise the admission & congestion control parameters in response to changes in cell capacity, traffic mix etc. so that QoS of admitted calls is meet and that call rejection is minimized

� Admission control

– Admits or rejects calls requests

– Proactive mechanism to prevent undesired/uncontrolled QoS

degradation

� Congestion control

– Reactive mechanism once QoS degradation has occurred

� Reference algorithm selected

– Focus on admission control for DL

– Priority will be made for HO calls rather than for new calls

– To ensure the QoS requirements priority will be given to real-time calls

instead of non- real time calls

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Other Self-optimisation use cases

�Load Balancing

– HO Offset modifications

– Cell coverage adjustments

� Interference Coordination

– Impact of different environments, service type, user mobility, traffic load distribution and elevated UE:s

�Handover

– Optimal setting of HO parameters

– Avoid ping-pong effects, e.g. in the stationary scenario

– Minimize HO drops

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Interacting Self-Optimisation algorithms 1(2)

� Each Self-Optimisation (SON) algorithm have one or several goals to meet

� A SON algorithm alter a set of control parameters to reach a predefined goal, e.g. KPI

� Coordination needed because,

– Algorithms have conflicting goals

– Could alter same control parameters

Goal 1 Goal 3Goal 2

SONAlgorithm 1

SONAlgorithm 2

SONAlgorithm 3

SONAlgorithm 4

…

…

Ctrl. param 1 Ctrl. param 2 Ctrl. param m…

Coverage, capacity, quality

optimizationBalance load

Optimize HO performance

Antenna

parameters

Transmit power HO parameters

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Interacting Self-Optimisation algorithms 2(2)

Goal 1

Coordinator

Goal 3Goal 2

SONAlgorithm 1

SONAlgoritm 2

SONAlgorithm 3

SONAlgorithm 4

…

…

Ctrl. param 1 Ctrl. param 2 Ctrl. param m…Operatorpolicy

� The SON algorithms may specify

– desired changes to control parameters

– desired range of the control parameters

� The coordinator may decline desired change or partially accept change by taking into account

– desired changes of all SON algorithm

– impact on goals and/or operatory policies

� Using the interface, the coordinator informs the SON algorithm regarding, e.g.,

– if desired change cannot be accomodated

– changes to other parameters that may impact the goal of the SON function

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Summary / Conlusion

� Introducing Self-optimising features to save cost, but also able to cope with the increasing complexity

� Some Self-optimisation principles presented

� Six uses cases selected for further elaboration– Self-optimisation of Home eNodeB

– Packet scheduling parameter optimisation

– Admission & Congestion control

– Load Balancing

– Interference Coordination

– Handover

� Challenging to cope with interacting (conflicting?) Self-optimisation algorithms

– Addressed within next phase of SOCRATES