umts ran dimensioning
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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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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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Universal Mobile Telecommunication System (UMTS)
UE User Equipment
Node B Base Station
RNC Radio Network Controller
UTRAN UMTS Terrestr ial Radio Access Network
PSTN Public Switched Telephone Network
UE
PSTN ...
Core Network
Node B
UE
UE
UE
InternetX.25 ...
UTRAN External Networks
Iub
Node B
UE
UE
UE
UE
Iub
RNC
Circuit Switched
Domain
Packet Switched
Domain
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Motivation of UMTS Network Dimensioning
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 Costly interface
Strict delay QoS
Costly interface
Strict delay QoS
Iub Interface
Dimensioning of Iubis important to design a high
cost- efficient UMTS network
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Goal of This Thesis
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 of this Thesis
Investigate important aspects related to the Iub dimensioning
Develop dimensioning approaches for different UMTS Networks
simulation models
analytical models
Derive important dimensioning guidelines and rules
Goal of this Thesis
Investigate important aspects related to the Iub dimensioning
Develop dimensioning approaches for different UMTS Networks
simulation models
analytical models
Derive important dimensioning guidelines and 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
Network Costs: the costs correlated with the expenditures necessary forleasing 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 goal of network dimensioning is to minimize costs while maximizing QoSThe goal of network dimensioning is to minimize costs while maximizing QoS
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Framework of UMTS Network Dimensioning
Bandwidth
QoS
AnalyticalApproach
Input
Traffic Demandtraffic classtraffic load
traffic distribution
QoS Targetsuser-relevant QoSnetwork-relevant QoS
Dimensioning
Process
Network
Configurations
network topology
traffic control functions
resource control functions
transport technology
QoS mechanisms
Output
Network Cost
minimum required linkcapacities (Mbit/s)
SimulationApproach
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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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Simulation Models
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
ATM TransportATM TransportIP Transport IP Transport
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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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Services and QoS Measures
VoiceVideo
Conferencing Applications/Services
Circuit-Switched Traffic
Traffic Classes
Web FTP
Elastic Traffic
Real Time (RT)low delaylow loss
require Admission Control
Non Real Time (NRT)
carried by TCP/IPdelay tolerant
QoS Measures
at flow/call levelBlocking probability
(CAC reject ratio)
Application Throughput
(Application Delay)
Packet Delay
Packet Loss ratio
QoS Measures
at packet level
over the Iub
Packet Delay
Packet Loss ratio
Network-
relevant QoS
User-
relevant QoS
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Overview of Analytical Models
Queuing Models with non-
Markovian Arrival Process
Non-preemptive priority
queuing model
Modeling Packet Level
MMPP(2)/D/1
or BMAP/D/1
MMPP(2)/D/1 - Priority
or BMAP/D/1-Priority
Erlang Loss
Model
Processor
Sharing (PS)
Model
Processor
Sharing Model
+ Erlang ModelProposed
AnalyticalModels
Modeling Call or Flow Level
Erlang-B
MD Erlang-B
M/G/R-PS
queuing model
Traffic Policy
- BW sharing
- BW separation
Dimensioning Tool Analytical Models
User-Relevant QoS Network-Relevant QoS
Circuit-Switched
TrafficMixed TrafficElastic Traffic Elastic Traffic Mixed Traffic
Circuit-
switched traffic
blocking QoS application delay
or throughput
packet delay, packet loss ratio
over the Iub interface
both QoS need
to be met
Traffic
Scenario
QoS
Measure
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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)
Iub (C)
UE
UE
UE
UE
UE
NodeB RNC
R = C / rpeak
Radio Network Cont
Radio Access Bearer (RAB) rpeak
rpeak
rpeak
rpeak
rpeak
rpeak Iub (C)
UEUE
UEUE
UEUE
UEUE
UEUE
NodeB RNCRNC
R = C / rpeak
Radio Network Cont
Radio Access Bearer (RAB) rpeak
rpeak
rpeak
rpeak
rpeak
rpeak
Flow arrival follows
Poisson Process General file length
distribution
Assumptions
M/G/R-PS Model
K. Lindberger (1999)
Peak data rate
File length
{ } Rpeakpeak
RGM frx
RRRE
rxxTE =
+=
)1(),(1)( 2//
Link utilization Delay factor
Number of servers
R = C / rpeak
Expected Sojourn Time (average transfer delay)
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Proposed Extensions on M/G/R-PS Model
Case Extensions Analytical Realizations
2. Single RAB
With CAC
3. Multiple RABs
No CAC
General M/G/R-PS model - R is bearer specific - consider total traffic
i
ir
CR = =
bearers
i
{ } Rii
i
i
ii
i
iiRGM f
r
x
R
RRE
r
xxTE =
+=
)1(
),(1)( 2//
4. Rate Adaptation
- BRA j
K
j
javgpeak qrr ==1
_ avgpeakavg rCR _/=
Reuse single rate M/G/R-PSCalculate an average ratefrom different r
peakto derive R
1. Single RABNo CAC
Radjust
fRTTRTT =
{ } { } adjustRTTratiorttULxTExTE )__2()(*)( ++=
New parameterUL_rtt_ratio
Seven Extensions are proposed in this thesis to incorporate UMTS networks
RAB Radio Access Bearer CAC Call Admission Control
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Proposed Extensions on M/G/R-PS Model (cont)
5. Mixing with
CS Traffic
CSelasticIub LCC +=
M/G/R-PS
CSelasticIub CCC +=
M/G/R-PS Erlang
(a)
(b)
Case Extensions Analytical Realizations
7. IP DiffServ { } k
kpeak
k
kk
kkk
kpeak
kkRGM f
r
x
R
RRE
r
xxTE
_
2
_
//)1(
),(1)( =
+=
6. Multi-Iub RANC
bb
Node B
Node B
Node B
IP Router
Cac_1
RNC
Cac_n
Cac_2
Backbone Link
Last mile links
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IP-based UTRAN with DiffServ QoS Structure
EF Expedited Forwarding
AF Assured Forwarding
PHB Per Hop Behavior
UMTS Core
Network
UMTS Core
NetworkNode B RNC
SP Strict Priority
WFQ Weighted Fair Queuing
DiffServ Differentiated Services
Per Hop Behavior (PHB)
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Validation of Application Delay Estimation
BE
AF41
AF31
AF21
AF11
EF
EF
PHB
10NRT HSPA 2Mbps
50NRT RAB 384kbps
40NRT RAB 256kbps
30NRT RAB 128kbps
20NRT RAB 64kbps
RT video
RT voice
WFQ
weightService class
Single Link Scenario
The relative errors of obtained analytical results are within
the agreed level for network dimensioning of industry
The relative errors of obtained analytical results are within
the agreed level for network dimensioning of industry
0.5 0.6 0.7 0.8 0.9 10
2
4
6
8
10
12
Iub link utilization
AF11
app.
delay(s)
AF11 PHB - NRT RAB 64kbps
M/G/R/N-PS
Simulations
0.5 0.6 0.7 0.8 0.9 10
2
4
6
8
10
12
Iub link utilization
AF
41app.
delay(s)
AF41 PHB - NRT RAB 384kbps
M/G/R/N-PS
Simulations
0.5 0.6 0.7 0.8 0.9 10
2
4
6
8
10
12
Iub link utilization
AF21app.
delay(s)
AF21 PHB - NRT RAB 128kbps
M/G/R/N-PS
Simulations
0.5 0.6 0.7 0.8 0.9 10
2
4
6
8
10
12
Iub link utilization
BEapp.
de
lay
(s)
BE PHB HSPA
M/G/R/N-PS
Simulations
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Queuing Models for Network-Relevant QoS
Arrival process model (shall capture bursty and self-similarity of the aggregatedarrival traffic and Bulk Arrival of packets)
2-state Markov Modulated Poisson Process (MMPP) model, where the inter-arrival time distribution is based on 2-Phase Hyper-exponential distr ibution
Batch Markovian Arrival Process (BMAP)
TTI
TTI
TTI
AAL2
Queue
ATM
Queue
Deterministic
service rate
Deterministic
service rate
Link
DCH 1
DCH 2
DCH n
Segmentation
FP PDUs
RT or NRTArrivals
Server process(deterministic service rate)
Depatures
(a) Single-service system
DepartureRT
NRT
H
L
Packet scheduling:
Non-preemptive priority
Server process(deterministic service rate)
DepaturesArrivals
(b) Priority system
Departure
queuing delay
Delay distribution
0.99
30ms
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MMPP Model for Estimation of the Iub delay
Capture of the Characteristic of the Arrival Traffic
Traffic demand
Mean traffic Variance Correlation
Add network / protocol overhead
Measure arrival traffic
MMPP arrival process
model parameters
Capture the arrival traffic characteristics
MMPPD/1 queuing
MMPP/D/1 priority queuing
Queuing delay
distribution
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Validation of the Iub Delay Estimation
0 1000 2000 3000 40000
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
voice traffic demand [kbps]
re
quiredIubbandwid
th[kbps]
voice only scenario - Rel99 ATM-based Iub
system simulation
M/D/1
H2/D/1
MMPP/D/1
0 1000 2000 3000 40000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
voice traffic demand [kbps]
relativeerroroftheanaly
ticalmodel
voice only scenario - Rel99 ATM-based Iub
M/D/1
H2/D/1
MMPP/D/1
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
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Validation of the Iub Delay Estimation
1000 2000 3000 4000 50001000
2000
3000
4000
5000
6000
7000
8000
UTRAN traffic demand [kbps]
req
uiredIubbandwidth[kbps]
packet switched traffic (BRA) only
System simulation
Queueing simulation (Opnet)
Analytical calculation
0 1000 2000 3000 4000 50000
1000
2000
3000
4000
5000
6000
7000
8000
UTRAN traffic demand [kbps]
req
uiredIubbandwidth[kbps]
packet switched traffic (BRA) with 10% voice
System simulation
Queueing simulation (Opnet)
Analytical calculation
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
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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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Conclusions and Outlook (cont)
Dimensioning and Comparing ofATM- 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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