Self-Optimising Call Admission Control for LTE Downlink K. Spaey, - - PowerPoint PPT Presentation

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Self-Optimising Call Admission Control for LTE Downlink K. Spaey, - - PowerPoint PPT Presentation

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FP7 ICT-SOCRATES Self-Optimising Call Admission Control for LTE Downlink K. Spaey, B. Sas, C. Blondia IBBT / University of Antwerp Joint COST 2100 / SOCRATES workshop February 5, 2010 Work performed within the context of the SOCRATES

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FP7 ICT-SOCRATES

Self-Optimising Call Admission Control for LTE Downlink

Joint COST 2100 / SOCRATES workshop February 5, 2010 Work performed within the context of the SOCRATES project ~ www.fp7-socrates.eu

  • K. Spaey, B. Sas, C. Blondia

IBBT / University of Antwerp

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  • (Self-optimising) call admission control
  • Reference admission control algorithm
  • Metrics
  • Self-optimising algorithm for ThHO
  • Evaluation methodology
  • Simulation results
  • Conclusions and future work

Outline

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Kathleen Spaey, IBBT / University of Antwerp

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  • Call admission control (AC) algorithm

– Decides if a call request will be admitted or rejected – Bases its decisions on:

  • Enough resources available to guarantee QoS new call?
  • If call is accepted, QoS of already accepted calls will be

maintained?

  • Self-optimising call admission control

– Self-optimise / auto-tune parameters of the AC algorithm – In response to changes

(Self-optimising) call admission control

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Kathleen Spaey, IBBT / University of Antwerp

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  • Development of a simple self-optimising AC algorithm
  • Evaluation under sudden overload
  • Reference AC algorithm (static algorithm) needed

– AC algorithms are vendor specific – Literature:

  • Prioritisation of acceptance of handover over fresh calls
  • Recognise diverse QoS requirements for delay-sensitive (RT) and

delay-tolerant (NRT) applications

(Self-optimising) call admission control

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Kathleen Spaey, IBBT / University of Antwerp

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  • Typical AC rule: admit call if

Reference admission control algorithm

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Kathleen Spaey, IBBT / University of Antwerp

required capacity already accepted calls required capacity new call cell capacity Distinct margins for

  • fresh / handover calls
  • RT / NRT calls

Cell capacity depends on → packet scheduler decisions → channel conditions users → location users → varies over time → estimate of time-varying cell capacity needed *

c *(t) + creq ≤ margin × C(k)

* Based on “Adaptive connection admission control scheme for high data rate mobile networks”, S.S. Jeong, J.A. Han, W.S. Jeon, VTC Fall 2005

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Reference admission control algorithm

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  • t: time of call arrival
  • C(k): most recent estimate of

cell capacity

  • creq: required capacity of arriving call
  • c*(t): required capacity of already

accepted active calls

  • c*

RT(t): required capacity of already

accepted active RT calls fresh calls are blocked priority is given to HO calls avoid that cell capacity is entirely filled with RT calls

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  • GoS measurements

– HO failure ratio

= (# HO calls rejected by the AC algorithm) / (# generated HO calls)

– Call blocking ratio

= (# fresh calls rejected by the AC algorithm) / (# generated fresh calls)

  • QoS measurements (only for admitted traffic)

– Traffic loss ratio = (# lost traffic) / (# generated traffic)

  • measured for RT traffic (voice / video)

– Call throughput = (# bits of call) / (call transfer time)

  • measured for NRT traffic (web)
  • we focus on the fraction of web calls with a call throughput smaller

than the minimum call throughput requested to the packet scheduler

Metrics to assess performance

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Kathleen Spaey, IBBT / University of Antwerp

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  • Simulations in which the 4 performance metrics are obtained

– for various ThHO values – in scenarios with varying call arrival rate or varying %HO calls

  • Changes in the measured performance might require opposite

adaptations of ThHO, depending on which performance measure is considered

– QoS degradation of ongoing calls – Increasing HO failure ratio – Increasing call blocking ratio → increase of ThHO

  • Operator policy to decide on this trade-off
  • SON algorithm which auto-tunes ThHO should take the chosen policy

into account Sensitivity analysis on ThHO

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Kathleen Spaey, IBBT / University of Antwerp

decrease of ThHO

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  • Desired properties

– ThHO should be adapted based on GoS / QoS measurements, rather

than on measurements on the system conditions

– Measurements should be smoothed – Follow policy to handle contradictions in required adaptations of ThHO

  • Policy considered

In order of priority:

– Aim to guarantee QoS of the accepted calls – Accept HO calls with priority over fresh calls – Aim to reduce call blocking ratio

Self-optimising algorithm for ThHO

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Kathleen Spaey, IBBT / University of Antwerp

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  • At regular time instants t = kΔ, measurements are collected
  • Measurements in [ kΔ ; (k+1) Δ [ , smoothed with parameter αSON

– QoS_RT(k): trafic loss ratio real-time traffic – QoS_NRT(k): fraction of non-real-time calls with call throughput smaller

than amount requested to scheduler

– GoS_HO(k): HO failure ratio – GoS_fresh(k): call blocking ratio

Self-optimising algorithm for ThHO

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Kathleen Spaey, IBBT / University of Antwerp

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Self-optimising algorithm for ThHO

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Kathleen Spaey, IBBT / University of Antwerp

bad QoS or bad HO failure ratio good QoS and good HO failure ratio and bad call blocking ratio decrease ThHO increase ThHO

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  • Evaluation of SON algorithm under sudden overload (unpredictable

event)

– Scenarios where there is a sudden increase in call arrival rate or/and

%HO calls

  • Comparison of performance obtained with

– Self-optimising AC algorithm (reference algorithm + auto-tuning of ThHO) – Static AC algorithm (reference algorithm, fixed ThHO)

Evaluation methodology

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Kathleen Spaey, IBBT / University of Antwerp

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  • Simulator for downlink direction developed using OPNET Modeler
  • Call generation:

– fresh / HO calls – VoIP (RT) / video streaming (RT) / web browsing (NRT)

Simulation model

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Kathleen Spaey, IBBT / University of Antwerp

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  • Set-up: 0.6 calls/s, 30% HO calls

1 call/s, 60% HO calls

  • Parameters self-optimising algorithm

– Δ = 1 minute – τQoS_RT = 1e-5, τQoS_NRT = 2%, τGoS_HO = 1%, τGoS_fresh = 5% – αSON = 0.75, 0.90

  • Static AC algorithm (no-SON): ThHO = 0.3, 0.4, …, 0.9, 1
  • SON AC algorithm (SON): ThHO is auto-tuned

Simulation results

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after 28 minutes (± 1000 calls)

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  • QoS: fraction of web calls with call throughput ≤ 250 kbit/s

Simulation results

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SON no SON

Before change: SON performs equally well After change: SON performs equally well After change: SON performs better

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  • QoS: traffic loss ratio

Simulation results

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SON no SON

Before change: SON performs equally well After change: SON performs equally well After change: SON performs better

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  • GoS: handover failure ratio

Simulation results

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Kathleen Spaey, IBBT / University of Antwerp

SON no SON

Before change: SON performs equally well After change: SON performs equally well After change: SON performs better

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  • GoS: call blocking ratio

Simulation results

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SON no SON

Before change: SON performs equally well After change: SON performs better After change: SON performs considerably worse Before change: SON performs better defined policy achieved defined policy achieved defined policy not achieved

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  • SON algorithm complies better to the defined policy, both before and

after the change, than the static algorithm with fixed ThHO

– In general:

  • “high” ThHO before change
  • “low” ThHO after change

→ SON can adapt ThHO according to the state the system is in

Conclusions and future work

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  • Future work: integration of multiple SON algorithms
  • Admission control SON combined with handover SON
  • both algorithms are triggered if handover failure

ratio is too high

  • both algorithms aim to reduce the handover failure

ratio in their own way → they influence each others input measurements