FP7 ICT-SOCRATES

Sensitivity Analysis of the Optimal Parameter Settings of

an LTE Packet Scheduler

I. Fernandez Diaz (TNO)R. Litjens (TNO)

J.L van den Berg (TNO)K. Spaey (IBBT)

D. Dimitrova (UT)

May 18, 2010, VTC ’10 Spring, Taipei, Taiwan

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CONTEXT

Driven by …– technological complexities– market-oriented perspectives

… there is an on-going trend towardsself-organisation of future mobile networks

– Self-configuration– ‘Plug and play’ installation

of new base stations and features– Self-healing

– Cell outage detection– Cell outage compensation: automatic

minimisation of coverage/capacity loss– Self-optimisation

– Power/tilt optimisation– Load balancing– Self-optimisation of packet

scheduling parameters– Automatic turning off/on sites– … triggered by

incidental events

continuousloop

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CONTEXT

Driven by …– technological complexities– market-oriented perspectives

… there is an on-going trend towardsself-organisation of future mobile networks

– Self-configuration– ‘Plug and play’ installation

of new base stations and features– Self-healing

– Cell outage detection– Cell outage compensation: automatic

minimisation of coverage/capacity loss– Self-optimisation

– Power/tilt optimisation– Load balancing– Self-optimisation of packet

scheduling parameters– Automatic turning off/on sites– … triggered by

incidental events

continuousloop

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OUTLINE

ContextObjectivePacket scheduling in LTE networksReference packet schedulerApproach sensitivity analysisNumerical resultsConcluding remarks

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OBJECTIVE

THIS PAPER:

Assessment of the sensitivity of optimal downlink LTE packet scheduling parameters with respect to a variety of traffic and environment aspects

FOLLOW-UP (IF SIGNIFICANT SENSITIVITY IS FOUND):

Develop self-optimisation algorithms to observe traffic/environment changes and adapt

scheduling parameters

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PACKET SCHEDULING IN LTE NETWORKS

Task of the packet scheduling algorithm– On a TTI timescale, assign cell’s radio resources to active sessions– Resource granularity in time domain 1 TTI = 1 ms– Resource granularity in frequency domain 1 ‘subchannel’ = 180 kHz

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REFERENCE PACKET SCHEDULING ALGORITHM

Supports RT and NRT sessions– With a tuneable degree of session-based differentiation– (As opposed to class-based differentiation)

Comprises three key principles– Proportional fairness → Tuneable channel-adaptivity: efficiency vs fairness– Packet urgency → RT packets are characterised by limited delay budgets– Work-conserving → Aim to utilise all resources

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REFERENCE PACKET SCHEDULING ALGORITHM

For each session i and subchannel c,calculate the priority level

with for each session i

Assign subchannels to sessionsbased on above prioritiesSelect uniform MCS per session

( ) ( )( )

( )( )

ξ

ρ ⎟⎟⎠

⎞⎜⎜⎝

⎛−

+⋅=tWT

tW1tR̂tR

tPii

i

i

c,iservicec,i

channeladaptivity

factor

packeturgencyfactor

( ) ( ) ( ) ( )1-tR1tR̂1tR̂ iii αα +−−=

• = potential bit rate at which session i can be served on subchannel c at TTI t

• = filtered average bit rate at which session i has been served up to TTI t

• = aggregate bit rate at which session i was served in TTI t-1

• = delay of HOL packet of session i experienced up to TTI t

• = maximum allowed packet delay for session i

• = minimum desired bit rate

• = parameter which sets the relative importance of the packet urgency factor

• = filtering parameter

( )tR̂ i

( )tR ci,

( )1-tRi

( )tWi

iT

ξ

α

serviceρ

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APPROACH SENSITIVITY ANALYSIS

Sensitivity analysis of the optimum packet scheduling parameter settings with respect to

– Service mix– Average file size (data service)– Coefficient of variation of the file size (data service)– Multipath fading environment– Variability of avg signal strengths between sessions

Reference scenario– Service mix file downloads only– Average file size (data service) 500 kbit– Coefficient of variation of the file size (data service) 1– Multipath fading environment PedestrianA, 3 km/h– Variability of avg signal strengths between sessions spatially uniform user

distribution, σshadowing = 9.4 dB

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NUMERICAL RESULTS

Reference scenario

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

2500

3000

3500

4000

0 200 400 600 800 1000

Cell load (kbit/s)

Ave

rage

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

Alpha

Max

imum

sup

port

edce

lllo

ad(k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

2500

3000

3500

4000

0 200 400 600 800 1000

Cell load (kbit/s)

Ave

rage

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

Alpha

Max

imum

sup

port

edce

lllo

ad(k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

500 kb/starget

(FAIRNESS)

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NUMERICAL RESULTS

Reference scenario

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

2500

3000

3500

4000

0 200 400 600 800 1000

Cell load (kbit/s)

Ave

rage

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

Alpha

Max

imum

sup

port

edce

lllo

ad(k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut a

t cel

led

ge(k

bit/s

) Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

500

1000

1500

2000

2500

3000

3500

4000

0 200 400 600 800 1000

Cell load (kbit/s)

Ave

rage

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Cell load (kbit/s)

10%

Per

cent

ileof

thro

ughp

ut (k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

Reference scenario

0

200

400

600

800

1000

1200

1400

Alpha

Max

imum

sup

port

edce

lllo

ad(k

bit/s

)

Max SINRalpha=0.001alpha=0.01alpha=0.1RR

500 kb/sTarget

(FAIRNESS)

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NUMERICAL RESULTS

Sensitivity w.r.t. average file size

Larger files allow a lower α (higher spectrum efficiency; larger ‘fairness window’)

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NUMERICAL RESULTS

Sensitivity w.r.t. coefficient of variation of file size

A larger CoV means more small files for which a higher α is optimal (see previous slide)

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NUMERICAL RESULTS

Sensitivity w.r.t. multipath fading environment

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NUMERICAL RESULTS

Sensitivity w.r.t. variability of avg signal strengths between sessions

low = dense hot spot, no shadowingmedium = reference scenariohigh = uniform spatial user distribution, σshadowing = 14 dB

Under very low variability, fairness is established even with a pure channel-aware scheduler.The higher the variability, a high α is needed to lift up the cell edge sessions.

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CONCLUDING REMARKS

Summary sensitivity analysis in data only scenarios– α = 0.01 is the optimal parameter setting in most scenarios, except when

– the average file size is very large (α 0 optimal)– the coefficient of variation of file size is very high (α 1 optimal)– the differences in average signal strengths are large (α 1 optimal)

– Comparison with a self-optimised scheduling algorithm– Assume that a SON function perfectly recognises the situation and

always chooses the correspondingly optimal scheduling parameter– The gain of self-optimisation in the studied scenarios is not very large,

viz. 3.3% on average over the considered scenarios and 16.6% maximum– Considering that a SON implementation is imperfect and the scenarios with the

highest observed gains are not very likely, we conclude that the potential for self-optimisation is not very significant

Sensitivity analysis for video/data scenarios– Refer to the paper for the detailed results and discussion– Although the potential for self-optimisation is somewhat larger, it is still not

overly significant

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CONCLUDING REMARKS

Some open issues– Impact of integration of ICIC and PS on the sensitivity analysis– Possibility of other reference packet schedulers

– Less intelligent packet schedulers have more SON potential– Note that the presented reference packet scheduler already inherently

possesses some adaptivivity properties– Fairness aim, regardless of individual channel qualities– Automatic shift of resources to RT traffic if RT load increases– Automatic response to congestion– …

– Consideration of the LTE uplink– Integration of SON in the heart of the packet scheduler (at the ms timescale),

as an alternative to the addition of a slowly adaptive SON layer atop the PS layer

– Respond to instantaneous (n) rather than average (λ) load– Respond to instantaneous (nRT:nNRT) rather than average (λRT:λNRT) service mix– …

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