3G Network Dimension Ing

ikom - ComNets

Treffen/Workshop der ITG Fachgruppe 5.2.1

Radio Access Network Dimensioning for 3G UMTS Xi Li xili@comnets.uni-bremen.de 

November 13, 2009

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Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

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ikom - ComNets

Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

3

Universal Mobile Telecommunication System (UMTS) ikom - ComNets UE UE

Node B

Circuit Switched Domain

Iub

PSTN ...

UE

RNC UE

UE

Node B

Iub

Packet Switched Domain

Internet X.25 ...

UE UE

UE UE Node B RNC

UTRAN User Equipment Base Station Radio Network Controller

Core Network UTRAN PSTN

External Networks

UMTS Terrestrial Radio Access Network Public Switched Telephone Network

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Motivation of UMTS Network Dimensioning ikom - ComNets 



Dimensioning: determine appropriate bandwidths for transport links  maximizing utilization of transport resources  guarantee QoS (Quality of Service) requirements The transport resource within the UTRAN is considerably costly

UTRAN

Iub Interface

 

 

Costly Costlyinterface interface Strict Strictdelay delayQoS QoS

Dimensioning of Iub is important to design a high cost- efficient UMTS network

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Goal of This Thesis ikom - ComNets 

UMTS network is developing fast 

Evolutions of UMTS    

 

Radio Access Network (RAN) evolution: Rel99, HSDPA, HSUPA, HSPA+, LTE Evolved UMTS terminals and emerging new services Significant increase of the traffic volume Remarkable changes in traffic pattern and characteristics

Transport Technologies for UTRAN, e.g. migration from ATM to IP Quality of Service Schemes, e.g. QoS differentiation and prioritization

Goal Goalof ofthis thisThesis Thesis    

Investigate Investigateimportant importantaspects aspectsrelated relatedtotothe theIub Iubdimensioning dimensioning Develop Developdimensioning dimensioningapproaches approachesfor fordifferent differentUMTS UMTSNetworks Networks  simulation models  simulation models analytical analyticalmodels models Derive Deriveimportant importantdimensioning dimensioningguidelines guidelinesand andrules rules  

 

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Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

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Objectives of UMTS Network Dimensioning ikom - ComNets 

Network Costs: the costs correlated with the expenditures necessary for leasing transport link bandwidths



Quality of Service 

user-relevant QoS: refers to the QoS related to the individual users 



Application delay or throughput, connection reject ratio due to admission control function

network-relevant QoS: network-specific QoS to evaluate the quality of a network, measured on the packet level 

Packet delay, packet loss ratio

The The goal goal of of network network dimensioning dimensioning is is to to minimize minimize costs costs while while maximizing maximizing QoS QoS

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Framework of UMTS Network Dimensioning ikom - ComNets Simulation Approach

Analytical Approach

Input

Output

Traffic Demand traffic class traffic load traffic distribution

Dimensioning Process

QoS Targets

Network Cost minimum required link capacities (Mbit/s)

user-relevant QoS network-relevant QoS

Network Configurations

QoS

network topology traffic control functions resource control functions transport technology

Bandwidth

QoS mechanisms 9

ikom - ComNets

Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

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Simulation Models    

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Model a complete UMTS system following 3GPP specifications Focused on a detailed modeling of the Iub interface (i.e. protocol stack, transport network, resource and QoS management) Modeling of air interface and core network are simplified Reduce complexity and improve simulation efficiency

IP Transport

ATM Transport

ATM Transport

IP Transport 11

ikom - ComNets

Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

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Services and QoS Measures ikom - ComNets Voice

Video Conferencing

Web

FTP

Real Time (RT)

Non Real Time (NRT)

low delay low loss require Admission Control

carried by TCP/IP delay tolerant

Circuit-Switched Traffic

Applications/Services

Traffic Classes

Elastic Traffic

Blocking probability (CAC reject ratio)

Application Throughput (Application Delay)

QoS Measures at flow/call level

Userrelevant QoS

Packet Delay Packet Loss ratio

Packet Delay Packet Loss ratio

QoS Measures at packet level over the Iub

Networkrelevant QoS

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Overview of Analytical Models ikom - ComNets

Dimensioning Tool

Analytical Models

User-Relevant QoS

Traffic Scenario

Circuit-Switched Traffic

QoS Measure

blocking QoS

Proposed Analytical Models

Elastic Traffic

application delay or throughput

Erlang Loss Model

Processor Sharing (PS) Model

Erlang-B MD Erlang-B

M/G/R-PS queuing model

Network-Relevant QoS

Mixed Traffic

both QoS need to be met

Processor Sharing Model + Erlang Model

Traffic Policy - BW sharing - BW separation

Modeling Call or Flow Level

Circuitswitched traffic

Elastic Traffic

Mixed Traffic

packet delay, packet loss ratio over the Iub interface

Queuing Models with nonMarkovian Arrival Process

Non-preemptive priority queuing model

MMPP(2)/D/1

MMPP(2)/D/1 - Priority

or BMAP/D/1

or BMAP/D/1-Priority

Modeling Packet Level 14

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Outline     

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results  



Processor Sharing Model (Application Performance) Packet level Queuing Model (Transport Network Performance)

Conclusions and Outlook

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Processor Sharing (PS) Model for Elastic Traffic - for User-Relevant QoS (Application Performance) ikom - ComNets UE

Radio Access Bearer (RAB)

rpeak

rpeak UE

Assumptions

rpeak

R = C / rpeak 

rpeak UE

Iub (C) NodeB

rpeak UE

RNC



Flow arrival follows Poisson Process General file length distribution

Radio Network Cont

rpeak

Expected Sojourn Time (average transfer delay) UE

M/G/R-PS Model File length

 E  M  / G /  R {T ( x)} =

Peak data rate

Number of servers  R = C / r  peak 

 x ⎛   E 2 ( R, R ρ ) ⎞  x ⎜⎜1 + ⎟⎟ = ⋅ f R r  peak  ⎝   R (1 −  ρ )  ⎠ r  peak 

Link utilization

K. Lindberger (1999)

Delay factor 16

Proposed Extensions on M/G/R-PS Model ikom - ComNets Seven Extensions are proposed in this thesis to incorporate UMTS networks Case

Extensions

Analytical Realizations RTTadjust = RTT ⋅ f R

1. Single RAB No CAC

New parameter UL_rtt_ratio  E {T ( x )}* =  E {T ( x )} + ( 2 + UL _ rtt _ ratio ) RTT adjust 

2. Single RAB With CAC 3. Multiple RABs No CAC

General M/G/R-PS model - R is bearer specific - consider total traffic

4. Rate Adaptation - BRA

Reuse single rate M/G/R-PS Calculate an average rate from different rpeak to derive R RAB CAC

Radio Access Bearer Call Admission Control

 Ri =

C 

∑ ρ 

 ρ  =

r i

 E  M  / G /  R {T ( xi )} =

r  peak _ avg =

i

bearers

 xi ⎛   E 2 ( Ri , Ri ρ ) ⎞  xi ⎜1 + ⎟ = f Ri r i ⎜⎝   Ri (1 −  ρ )  ⎠⎟ r i

K 

∑ r  ⋅ q  j

 j =1

j

 Ravg = C  / r  peak _ avg

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Proposed Extensions on M/G/R-PS Model (cont’) ikom - ComNets Case 5. Mixing with CS Traffic

Extensions

Analytical Realizations M/G/R-PS

(a) C  Iub = C elastic + LCS (b) C  Iub

= C elastic + C CS M/G/R-PS

Erlang

Last mile links

Node B

C ac_1

Node B

C ac_2 

6. Multi-Iub RAN

Backbone Link C bb  IP Router

RNC

C ac_n 

Node B

7. IP DiffServ

 E  M  / G /  R {T ( xk )} =

⎛   E 2 ( Rk , Rk  ρ k ) ⎞  xk  ⎜1 + ⎟= f k  r  peak _ k  ⎜⎝   Rk (1 −  ρ k )  ⎠⎟ r  peak _ k   xk 

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IP-based UTRAN with DiffServ QoS Structure ikom - ComNets

RNC

Node B

UMTS Core Network

Per Hop Behavior (PHB) EF AF PHB

Expedited Forwarding Assured Forwarding Per Hop Behavior

SP WFQ DiffServ

Strict Priority Weighted Fair Queuing Differentiated Services

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Validation of Application Delay Estimation ikom - ComNets AF21 PHB - NRT RAB 128kbps

AF11 PHB - NRT RAB 64kbps

Single Link Scenario

12

12    ) 10   s    (   y   a 8    l   e    d  .   p 6   p   a    1 4    1    F    A

M/G/R/N-PS Simulations

   )   s    (

  y   a    l   e    d  .   p   p   a    1    2    F    A

2

0 0.5

WFQ weight

10

M/G/R/N-PS Simulations

8 6 4 2

0.6

0.7 0.8 Iub link utilization

0.9

0 0.5

1

0.6

AF41 PHB - NRT RAB 384kbps

Service class

PHB

RT voice

EF

10    )

RT video

EF

8

NRT RAB 64kbps

AF11

20

NRT RAB 128kbps

AF21

30

NRT RAB 256kbps

AF31

40

NRT RAB 384kbps

AF41

50

NRT HSPA 2Mbps

BE

10

1

0.9

1

12

M/G/R/N-PS Simulations

10    )   s    (   y   a    l   e    d  .   p   p   a    E    B

6 4

M/G/R/N-PS Simulations

8 6 4 2

2 0 0.5

0.9

BE PHB HSPA

12   s    (   y   a    l   e    d  .   p   p   a    1    4    F    A

0.7 0.8 Iub link utilization

0.6

0.7 0.8 Iub link utilization

0.9

1

0 0.5

0.6

0.7 0.8 Iub link utilization

The Therelative relativeerrors errorsof ofobtained obtainedanalytical analyticalresults resultsare arewithin within the theagreed agreedlevel levelfor fornetwork networkdimensioning dimensioningof ofindustry industry

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Queuing Models for Network-Relevant QoS

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FP PDUs

Delay distribution

TTI

DCH 1

TTI

DCH 2

0.99 Link

DCH n

TTI

AAL2 ATM Queue Segmentation Queue Deterministic Deterministic service rate service rate

30ms

Arrivals

Departure Depatures

RT or NRT

H

Packet scheduling: Non-preemptive priority

Departure Depatures

Arrivals Server process (deterministic service rate)

(a) Single-service system 

RT

queuing delay

NRT L

Server process (deterministic service rate)

(b) Priority system

Arrival process model (shall capture bursty and self-similarity of the aggregated arrival traffic and Bulk Arrival of packets)  2-state Markov Modulated Poisson Process (MMPP) model, where the interarrival time distribution is based on 2-Phase Hyper-exponential distribution  Batch Markovian Arrival Process (BMAP) 21

MMPP Model for Estimation of the Iub delay ikom - ComNets Capture of the Characteristic of the Arrival Traffic

Traffic demand

Add network / protocol overhead

Mean traffic

Variance

Correlation

Measure arrival traffic

Capture the arrival traffic characteristics MMPP arrival process model parameters

MMPPD/1 queuing MMPP/D/1 – priority queuing

Queuing delay distribution

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Validation of the Iub Delay Estimation ikom - ComNets Scenario I: 100% voice traffic (single Iub) Traffic model: Adaptive Multi Rate (AMR) 12.2kbps Speech/silence period: exponential distribution, mean = 3 seconds Call duration: exponential distribution, mean = 120 seconds Dimension QoS target: 99% of packets experience less than 10ms Iub delay voice only scenario - Rel99 ATM-based Iub 10000    ]   s   p    b    k    [    h    t    d    i   w    d   n   a    b    b   u    I    d   e   r    i   u   q   e   r

9000 8000 7000

   l   e    d 0.35   o   m    l   a 0.3   c    i    t   y    l   a 0.25   n   a   e 0.2    h    t    f   o   r 0.15   o   r   r   e 0.1   e   v    i    t   a 0.05    l   e   r

system simulation M/D/1 H2/D/1 MMPP/D/1

6000 5000 4000 3000 2000 1000 0 0

1000 2000 3000 voice traffic demand [kbps]

voice only scenario - Rel99 ATM-based I ub 0.4

4000

0 0

M/D/1 H2/D/1 MMPP/D/1

1000 2000 3000 voice traffic demand [kbps]

4000

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Validation of the Iub Delay Estimation ikom - ComNets Dimension QoS target: 99% of voice packets experience less than 10ms Iub delay 99% of data packets experience less than 30ms Iub delay Scenario III: 90% web traffic (low priority) & 10% voice traffic (high priority)

Scenario II: 100% web traffic packet switched traffic (BRA) only

packet switched traffic (BRA) with 10% voice

8000    ]   s 7000   p    b    k    [ 6000    h    t    d    i   w    d 5000   n   a    b    b 4000   u    I

8000

System simulation Queueing simulation (Opnet) Analytical calculation

   ] 7000   s   p    b    k    [ 6000    h    t    d    i 5000   w    d   n   a 4000    b    b   u 3000    I

   d   e   r 3000    i   u   q   e 2000   r

1000 1000

System simulation Queueing simulation (Opnet) Analytical calculation

   d   e   r    i   u 2000   q   e   r

1000

2000 3000 4000 UTRAN traffic demand [kbps]

5000

0 0

1000 2000 3000 4000 UTRAN traffic demand [kbps]

5000

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ikom - ComNets

Outline      

Introduction and Motivation UMTS Network Dimensioning Framework Developed Simulation Models Developed Analytical Models Dimensioning Models and Results Conclusions and Outlook

25

Conclusions and Outlook    

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Propose a general UMTS network dimensioning framework Develop several detailed simulation models with OPNET Investigate important aspects related to Iub dimensioning Develop novel analytical models for dimensioning of the Iub interface and validated by simulations

Circuit-switched Traffic User QoS

Network QoS

Elastic traffic

Mixed traffic

Blocking QoS Erlang-B (single stream) MD-Erlang B (multi-stream)

Application QoS Processor Sharing (PS) Extensions of M/G/R-PS

Consider applied traffic policy and its Mux. gain

Packet delay & loss QoS

Packet delay & loss QoS

 

MMPP: Markov Modulated Poisson Process BMAP: Batch Markovian Arrival Process

MMPP/D/1 or BMAP/D/1 

MMPP/D/1 or BMAP/D/1

MMPP/D/1-nonpreemptive priority

Develop a dimensioning tool (in Matlab)  

Summarize all proposed analytical models Apply to derive dimensioning rules 26

Conclusions and Outlook (cont’) ikom - ComNets 



Dimensioning and Comparing of ATM- and IP-based UTRAN 

Single Iub link scenario



Multi-Iub RAN scenario

Dimensioning HSPA traffic in ATM-based UTRAN   



HSDPA HSUPA HSPA+Rel99 (Traffic Separation)

Further Work: Long Term Evolution (LTE)  

Expect a much higher demand on transport bandwidth in access networks Dimensioning for LTE transport access network  Investigating applicability of current dimensioning models  Extensions of analytical models are desired

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Thank for your Attention

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