Self-organisation in future mobile cellular networks Remco Litjens - - PowerPoint PPT Presentation
FP7 ICT-SOCRATES Self-organisation in future mobile cellular networks Remco Litjens TNO ICT, Delft, The Netherlands OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– 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
SOURCE NODE DESTINATION NODE
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– 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
PACKET TIMEOUT: CONGESTION WINDOW / 2 NO PACKET TIMEOUT: CONGESTION WINDOW + 1
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– Automatic detection of connectivity – Automatic establishment of routes – Automatic rerouting upon node failure
DESTINATION NODE SOURCE NODE
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– Automatic detection of connectivity – Automatic establishment of routes – Automatic rerouting upon node failure
DESTINATION NODE SOURCE NODE
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inner loop power control
power control
SINR BLER
– Default case
– 1st Self-optimisation loop
– 2nd Self-optimisation loop
– 3rd Self-optimisation loop
end-to-end packet loss target?
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– Mobile cellular communications networks – LTE access technology
1989
1980
1985
2006 2003
2001
1994
2011?
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– Separation of network planning and optimisation – (Non-)automated planning tools used to select sites, radio parameters
– Manual configuration of sites – Radio (resource management) parameters updated weekly/monthly
– Delayed, manual and poor handling of cell/site failures
– Self-configuration, self-optimisation, self-healing, …
– 3GPP, NGMN, SOCRATES, …
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Complexity of future/contemporary wireless access networks
– Higher operational frequencies
– 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!
– The multitude and technical capabilities of base stations and terminals to
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– Increasing demand for services – Increasing diversity of services
– Need to reduce time-to-market of innovative services
– Pressure to remain competitive
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Self-configuration – Self-healing – Self-optimisation
triggered by incidental events con2nuous loop
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– Incidental, intentional events – ‘Plug and play’ installation
network parameters, neigh- bour list generation, trans- port network discovery and configuration, …
– Incidental, non-intentional events – Cell outage detection
– Cell outage compensation
triggered by incidental events con2nuous loop
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– Measurements
propagation conditions
format depends on parameter/ mechanism that is optimised – Automatic tuning
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
capacity expansion is unavoidable
triggered by incidental events con2nuous loop
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Primary objective! – Less human involvement in
– About 25% of OPEX is related to network operations
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– Via delayed capacity expansions – Smart eNodeBs may however be more expensive
– Enhanced service availability (robustness/resilience), service quality
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– Via delayed capacity expansions – Smart eNodeBs may however be more expensive
– Enhanced service availability, service quality
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– To guide development of solutions
– To help determine requirements
– Performance – Complexity – Stability/robustness – Timing – Interaction – Architecture/scalability – Required measurements
– Faster roll-out of LTE networks – Simplified operational processes – Easy deployment of new services – End user quality/cost benefits
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– Self-optimisation
– Interference coordination – Self-optimisation of physical channels – RACH optimisation – Self-optimisation of Home eNodeB
– AC/CC/PS optimisation – Link level retx scheme optimisation – Coverage hole detection/compensation
– Handover parameter optimisation – Load balancing – Neighbour cell list
– Reduction of energy consumption – TDD UL/DL switching point – Management of relays and repeaters – Spectrum sharing – MIMO
– Self-configuration
parameters for NE insertion
– Self-healing
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– NCL indicates potential handover target cells – Typically limited to 32 cells – Missing neighbours induces call
– Undesired neighbours cause
– UE’s signal strength reports – eNB scans of neighbours – Call drops, handover failures – Handover stats: used neighbours
– Site/cell addition – Poor performance – Periodic optimisation
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Key radio resource management mechanism – Objective is to admit as many calls as possible such
– Typical admission control rule
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– How to determine cell capacity = non-trivial!! – How to determine the new call load = non-trivial!! – How to set the margins?
uncontrollable variations in resource usage
– User activity – User mobility location affects resource usage – Propagation effects
– ‘Sufficient’ depends on degree of mobility, which may vary during the day/week
– 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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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Cell outages
– Automatic detection of failures
and urgency of outage
probability of missed detection and false alarm – Measurements
pilots, interference levels
carried load, call drops, …
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– Automatic compensation of failures
– Control parameters
parameters, load balancing
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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
missed revenue
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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– Impact of eNodeB density and load
– Impact of service type
service, e.g. VoIP telephony – Impact of outage location
cell/site outage occurs at the inner part of an LTE island – Also study impact of
aspects, spatial traffic distribution, UE class
On‐going work
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– 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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– 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/
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– Objectives
experience excessive interference to/from femto-cell
cell, while macro-cell may experience significant UL interference from femto-cell – Control parameters
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– Measurements
Tuned to urgency?
vs achieved performance
determine ‘network state’
malicious reports – Effectiveness of self-organisation
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– Dealing with delayed feedback
is not immediate
– Reliability
revision of actions
when giving up direct control
– Gradual introduction
– Shape the network architecture
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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Algorithm Assessment Assessment Criteria Measurements Control Parameters Operator Policy Controllability & Observability Algorithm Specifica2on Scenarios Algorithm Development 3GPP specifica2on Implementa2on
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– In the process of algorithm development, the Real Network can be
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– Optimisation Algorithm exploits a Network Simulator for ‘what if’ analyses,
Predicted Network Sta/s/cs Proposed Parameter Se3ngs
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– Real-Time Controller rapidly responds to changes – Periodic off-line tuning of Real-Time Controller by Optimisation Algorithm
– Reliable, stable but suboptimal for complex use cases?
Updated Controller Se3ngs Controller’s Performance Sta/s/cs
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– Periodic/continuous on-line tuning of Real-Time Controller
– Reference CAC scheme with an RL-based self-optimisation layer on top,
– Integrated RL-based CAC scheme, directly optimising the mapping of system
state to admission/rejection decision
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Proposed Network Parameters
– Optimisation Algorithm exploits a Network Model for ‘what if’ analyses
planning tool in order to optimise tilts and azimuths
Predicted Network Sta/s/cs
‘Solve’ the model
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– To collect and promote operator
– Establish clear performance targets,
– SON WP is a continuation of ‘Project 12’ – Develop operator vision on self-organisation – Ensure that self-organisation capabilities become
– Push vendors to fulfil operator requirements regarding
– Concrete activities include
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– Introduced in Q2 ‘07 – Considered primarily
– R8 (frozen)
Automatic physical cell ID discovery – R9 (tentatively frozen 12/2009)
reduction, Load balancing, Coverage hole management and cell outage management, Handover optimisation
– ‘Self-configuring and self-optimizing network use cases and solutions’,
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– Self-Optimisation and self-ConfiguRATion in wirelEss networkS
– 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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– Technological focus: 3GPP E-UTRAN (LTE) – Radio (resource management) parameters, e.g. pilot power, antenna tilt,
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– Development of novel concepts, methods and algorithms for the effective
– Specification of the required information, its statistical accuracy and the
– Validation and demonstration of the developed concepts and methods for self-
– Assessment of the operational impact of the developed concepts and
– Influence on 3GPP standardisation and NGMN activities
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– Development of methods and algorithms – Quantitative assessment – Simulation of scenarios
– FP7 E3, 4WARD, EFIPSANS, EURO-NF, …. – COST 2100 – 3GPP, NGMN, WWRF
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– Automatic neighbour cell list generation – Admission/congestion control – Cell outage management – Self-optimisation of Home eNodeBs
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– … reduce O/CAPEX – … cost-effective provisioning of high-quality services – … reduce time-to-market of new features, services
– Self-configuration – Self-optimisation – Self-healing
– Effectiveness, reliability, stability – Measurements, interfaces, protocols, architectures
– NGMN, 3GPP, GANDALF, E3, SOCRATES, …, you?
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