-
Contract Number: IST-2000-28088
Project Title: Models and Simulations for Network Planning and
Control of UMTS
Project Acronym: MOMENTUM
n
Information Report Number: IST-TUL_WP1_DR_PUB_200_WL_05_D1.4
Date of Delivery: 27.05.2003 Report Title: Deliverable D1.4: Final
report on traffic estimation and
services characterisation Editor: Lcio Ferreira (IST-TUL)
Authors: Lcio Ferreira (IST-TUL), Luis M. Correia (IST-TUL),
David Xavier (IST-TUL), llen Vasconcelos (IST-TUL), Erik
Fledderus (TNO).
Reviewers Carlos Caseiro (Telecel/Vodafone),
Erik Fledderus (TNO)
Abstract: This final report addresses the main results achieved
in WP1 on procedures to generate mobility and traffic scenarios, to
be used in the deployment of UMTS radio networks and on service
characterisation. After discussing the challenges concerning the
generation of multi-service traffic, a service set is chosen and
described in detail, classified and characterised, and the users
profiles are established. A traffic forecast of static users is
built for the city of Lisbon, as an example, based on an
operational environment with users spread over it generating calls
according to certain services usage patterns. Key parameters,
necessary data, and interdependencies among data are identified and
described in detail. A mobility scenario is defined, characterised
by different mobility types and a mobility model that controls the
movement of users on a motion grid. Traffic demand scenarios are
then defined for dynamic, static and short-term dynamic
simulations, where in particular average load grids are presented.
User generation is addressed as well. Key word list: UMTS,
Scenarios, Services, Users Profile, Traffic Estimation, Mobility,
Average Load, IST, Key Action IV, Action Line IV.4.1 Key Action:
IV, Essential Technologies and Infrastructures Action line: IV.4.1,
Simulation & Visualisation Confidentiality: MOMENTUM PUBLIC
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Document History
Date Version Comment Editor 17.03. 2003
1 First version. Lcio Ferreira (IST-TUL)
11.04. 2003
2 Second version updated with Carlos Caseiro (Telecel/Vodafone)
Erik Fledderus (TNO), Alexander Martin and Oliver Wengel (TUD),
Andreas Eisenblaetter (Atesio) and Ranjit Perera (UB) review
comments.
Lcio Ferreira (IST-TUL)
09.05. 2003
3 Third version with updated BHCA tables, population
distribution and resulting BHCA and load grids, and updated with
second review comments by Erik Fledderus (TNO).
Lcio Ferreira (IST-TUL)
18.05. 2003
4 Fourth version with the inclusion of dynamic load simulator
results.
Lcio Ferreira (IST-TUL)
27.05. 2003
5 Final version ready to be delivered.
Lcio Ferreira (IST-TUL)
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Contents
Contents....................................................................................................................3
List of Figures
..........................................................................................................5
List of
Tables............................................................................................................6
List of
Tables............................................................................................................6
List of
Notation........................................................................................................7
1
Introduction......................................................................................................9
2 UMTS Demand
..............................................................................................11
2.1 Key Drivers and Barriers for UMTS Demand
.............................11 2.2 Forecasting Demand
....................................................................12
3 Service
Set.......................................................................................................14
4 Traffic
Estimation..........................................................................................19
4.1 Initial Considerations
...................................................................19
4.2 User Profile
..................................................................................20
4.3 Operational Environment
.............................................................22 4.4
Population
distribution.................................................................24
4.5 Subscribers
distributions.............................................................27
4.6 BHCA
grids..................................................................................29
5 Mobility
Scenario...........................................................................................33
5.1
Introduction..................................................................................33
5.2 Mobility Model
............................................................................34
5.3 Penetration of Mobility Types
.....................................................37 5.4
Implementation of mobility
.........................................................41
6 Traffic Scenarios for Simulation
..................................................................46
6.1 Average Load Grids
.....................................................................46
6.2 Simulation Approaches
................................................................48
6.3 Generation of Users
.....................................................................49
7
Conclusions.....................................................................................................52
A Bearer
specifications......................................................................................54
A.1 Uplink bearers
..............................................................................54
A.2 Downlink bearers
.........................................................................56
B Updated Transition
Tables............................................................................59
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References
..............................................................................................................62
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List of Figures
Figure 2-1: Percentage of residential respondents willing to use
each of the services
[7]........................................................................................12
Figure 2-2: Percentage of business respondents willing to use
each of the services
[7]........................................................................................12
Figure 2-3: Example of UMTS segment market share evolution.
..........................13 Figure 3-1: Service set bit rate range
and DL session volume................................15 Figure 3-2:
Service set traffic flow characterisation during a session (time
and
bit rate domain) [2].
..........................................................................17
Figure 4-1: General process for the construction of a traffic
scenario....................20 Figure 4-2: Lisbon land use data
thematic map [22]. ..............................................23
Figure 4-3: Lisbon vector data thematic map
[22]...................................................23 Figure
4-4: Lisbon Operational
Environment.........................................................24
Figure 4-5: Lisbon population distribution during the day.
....................................25 Figure 4-6: Calculation of
persons per pixel in the different vector
operational environment classes.
......................................................26 Figure
4-7: Lisbon UMTS penetration, per customer segment.
.............................28 Figure 4-8: Lisbon UMTS
subscribers, per customer segment. .............................29
Figure 4-9: Video-telephony BHCA grids [calls / hour / 400 m2
pixel]. ................30 Figure 4-10:
Mass-Market/Speech-telephony BHCA grid [calls / hour / 400
m2
pixel]............................................................................................30
Figure 4-11: Service set BHCA grids.
....................................................................32
Figure 4-12: Location Based BHCA grid [calls/hour/400 m2 pixel],
for an
equal range scale representation.
......................................................32 Figure
5-1: Mobility scenario, identifying different mobility types
associated
to the operational environment classes.
............................................33 Figure 5-2: Velocity
probability density function [27].
..........................................35 Figure 5-3: Possible
pixel transition
directions.......................................................37
Figure 5-4: Mobility grids for the area of Lisbon under study.
..............................42 Figure 5-5: Conversion of vector
to pixel data.
......................................................42 Figure
5-6: Pixel crossed by a street.
......................................................................43
Figure 5-7: Example of conversion from vector to raster format.
..........................43 Figure 6-1: Load Grids.
..........................................................................................47
Figure 6-2: Speech average load grids for different times of
simulation,
considering a simulation step of 1 second.
.......................................48 Figure 6-3: New BHCA
grids, considering the restrictions of unavailable
services in certain operational
environments....................................49 Figure 6-4:
Generation process of active
users.......................................................50
Figure A-1: The EbNo !!!! BLER relations for the uplinklink
bearers....................55 Figure A-2: The EbNo !!!! BLER
relations for the downlink bearers. ....................58
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List of Tables
Table 2-1: Customer segmentation.
........................................................................13
Table 3-1: Service set parameters [2].
....................................................................18
Table 4-1: Number of calls per day per customer segment.
...................................21 Table 4-2: Busy hour usage
per segment.
...............................................................21
Table 4-3: Average number of calls in the busy hour (BHCA) per
service and
customer segment subscriber.
...........................................................22 Table
4-4: Momentum operational environment classes.
.......................................22 Table 4-5: Persons per
pixel in vector operational environment classes (for a
grid of 20 m x 20 m pixel size
resolution)........................................25 Table 4-6:
Weights per non-vector operational environment class, to be
applied in estimated population per pixel in vector operational
environment classes where population in that pixel is 0.
.................26
Table 4-7: Operational environment share between customer
segments (in %).....27 Table 4-8: UMTS subscribers penetration, per
segment and for a specific
operator.
............................................................................................27
Table 5-1: Mobility types average velocity and velocity
variation.........................36 Table 5-2: Mobility types PDF
parameters.............................................................36
Table 5-3: Probability of changing direction values, for each
mobility type..........36 Table 5-4: Mobility types penetration
table per operational environment class. ....37 Table 5-5: Possible
mobility types for each service.
..............................................38 Table 5-6:
Available services per operational environment
class...........................38 Table 5-7: Mobility type
penetration table per operational environment class
for Speech-telephony, Location based, MMS and E-Mail services.
............................................................................................39
Table 5-8: Mobility type penetration table per operational
environment class for Web browsing and File Download
services................................40
Table 5-9: Mobility type penetration table per operational
environment class for Video-telephony and Streaming multimedia
services.................40
Table 5-10: Transition array reference table, combining the
possible array of sides with the user entrance side to the pixel.
..................................44
Table 5-11: Pixel oriented direction probabilities, for all
mobility types. ..............44 Table 5-12: Specific loss per
service and mobility type.
........................................45 Table A-1:
Characterisation of the uplink bearers
..................................................54 Table A-2:
Characterisation of the downlink bearers
.............................................56
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List of Notation
3G 3rd Generation 3GPP 3rd Generation Partnership Project Asy
Asymmetric B Back direction BER Bit Error Rate BHCA Busy Hour Call
Attempt Bid Bi-directional CBD Central Business District COST
European Co-Operation in the Field of Scientific and Technical
Research CRC Cyclic Redundancy Check CS Circuit Switched DL
Downlink E East EDGE Enhanced Data rates for GSM Evolution ETSI
European Telecommunications Standards Institute F Forward direction
FER Frame Erasure Ratio / Frame Error Rate GIS Geographic
Information Systems GPRS General Packet Radio Service GSM Global
System for Mobile Communications HSCSD High Speed Circuit Switched
Data IST Information Society Technologies ITU International
Telecommunications Union MM Multimedia MMS Multimedia Messaging
Service MOMENTUM
Models and Simulations for Network Planning and Control of
UMTS
N North NRT Non-Real Time NTB Non-Time Based O-M One to Many
parties O-O One to One party PDF Probability Density Function PS
Packet Switched RT Real Time S South
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SF Spreading Factor SOHO Small Office/Home Office Sym Symmetric
T Turn direction TB Time Based UL Uplink UMTS Universal Mobile
Telecommunications System Uni Unidirectional W West WAP Wireless
Application Protocol WP Work package WP1 Work package 1 Traffic
Estimation & Service Characterisation WP2 Work package 2
Traffic Modelling and Simulations for
Interference Estimation WP3 Work package 3 Dynamic Simulations
for Radio Resource
Management WP4 Work package 4 Automatic Planning of Large-Scale
Radio
Networks WP5 Work package 5 Assessment and Evaluation WWW World
Wide Web XML eXtensible Markup Language
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1 Introduction
UMTS is intended to be a system providing a multiple choice of
services and applications to users, enabling the mixed use of
voice, video and data, partly at the will of the user, and partly
depending on the availability of the network. This makes a huge
difference from existing 2nd generation cellular systems, e.g.,
GSM, which were never foreseen for this purpose, although they have
recently started to provide services other than voice or simple
messaging. This poses a real challenge to those involved in the
design and dimensioning of UMTS networks, coming from the fact that
there is no real data available that can be used for the estimation
of the traffic offered to the system. The foreseen variety of
services, the enormous set of possibilities of their use, combined
with the lack of solid marketing information, makes the task of
traffic estimation a very difficult and challenging one. MOMENTUM
[1] is devoted to the study of UMTS radio network planning,
presenting a complete approach to the challenge of producing a
realistic estimate of a location-variant demand distribution for
mobile users. This is essential to generate and optimise a
realistic network configuration that satisfies this demand.
Services are characterised, usage profiles are built, and traffic
and mobility scenarios are generated to model the future demand in
the most realistic way while keeping at the same time the necessary
flexibility to incorporate future insights. An optimised radio
network configuration is achieved with a developed automatic
planning tool, using heuristic rules for faster evaluation. To
evaluate the performance of the obtained configuration, a powerful
newly developed dynamic real-time system-level simulator is used,
taking most dynamic aspects of UMTS into account. For every-day
planning purposes, a fast and simple snapshot simulator will also
be tuned to fit the results of the dynamic simulator the best way
possible. A library of UMTS scenarios will be built and published,
with test cases to be used as a benchmark in the development of
planning tools. MOMENTUM deals with the dimensioning of UMTS radio
networks in an optimum way, taking into account the relationships
between services demand, traffic capacity and network performance.
Thus, it is of key importance to establish mobility and traffic
demand scenarios as accurately as possible, so that results coming
from developed and/or used simulators, and from developed
optimisation algorithms, make sense and can be used to really
conclude on them. This report presents the final report of the work
developed in WP1 [2], which tries to answer to the following
question: Which time- and location-variant service demand
distribution for mobile users is to be expected? It presents an
approach to the problem of demand estimation, by presenting a clear
characterisation of the foreseen services, and establishing a
procedure for estimation of realistic traffic demand scenarios,
based on actual population data and its characteristics, together
with various assumptions on the use of services and on market
forecasts. Given the fact that much of the data is related to
geographical aspects, e.g., population distribution and clutter, a
Geographic Information System (GIS) tool (MapInfo [3]) is used to
visualise this information. MapBasic and C programming languages
were combined to process data. A machinery to generate traffic
scenarios is presented,
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and all processing steps are identified. Key parameters
controlling the generation of these scenarios, necessary data, and
interdependencies among the various parameters are identified and
described in detail. The procedure is a general one, i.e., it can
be applied to any geographical area. Mobility scenarios are also
addressed. They specify completely the motion of pedestrian users
and vehicular ones on roads and streets. This allows the realistic
simulation of a scenario with moving users generating traffic.
Mobility will have an impact in the spread of the average load over
the scenario. As an example of the needed data and processing for
generation of these scenarios, the centre of Lisbon is illustrated.
Besides this chapter, this document encompasses six others: Chapter
2 regards the demand of UMTS. In Chapter 3 the chosen UMTS service
set used in the project is described. Chapter 4 is dedicated to
traffic estimation generation. Chapter 5 addresses the mobility
scenarios. In Chapter 6 the different traffic scenarios for
simulation are presented. Conclusions are drawn in Chapter 7.
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2 UMTS Demand
2.1 Key Drivers and Barriers for UMTS Demand
The standardisation of UMTS started already in the early 90s;
the motto was to design a system that could deliver multimedia
services anytime, anywhere. Especially during the late 90s, when
the mobile industry boomed, the call for UMTS was strong: it would
solve capacity problems, and it would bring the vision of
ubiquitous computing close by. We know that since then, the
Information and Communications Technologies [4] world changed
considerably; after a deep dive down, most operators started to
realise that customers are not willing to pay for just mobile
Internet. It is clearer than ever that rolling out new wireless
systems should go hand in hand with stimulating demand by actively
investing in mobile data services. In this new era where realism
and caution are the keywords, UMTS must try to regain its position.
A key number of drivers and barriers [5], [6], [7] strongly
influence the demand of UMTS: The realisation of the new technical
possibilities of UMTS (high data rates,
symmetrical and asymmetrical connection, circuit- and
packet-switched mode, support of simultaneous calls, etc) is of
paramount importance for the success of this system.
The extreme fast development of fixed multimedia is a good
indicator to assess the demand for UMTS. Nevertheless, the high
cost gap between fixed and mobile may discourage the uptake.
The fast development of e-commerce is expected to have a good
impact on the demand of UMTS.
With the increase in peoples mobility, nomadic workers appear as
key UMTS customers, willing to pay for a continuity of broadband
services outside the office while on the move.
The operators battle for UMTS customers deals with pricing,
subsidies for terminals, and interesting applications. For
customers, this will have a positive impact.
Network technologies such as HSCSD, GPRS and EDGE and the
arrival of services such as WAP and despite its teething troubles
i-mode will educate future UMTS customers with regard to data
communications, and at the same time will give operators time to
change from a circuit to a packet world.
The multitude of UMTS standards and the various options that are
left open result in a limited availability of terminals have a
negative impact on the uptake of UMTS.
Regulatory aspects and standards have enabled economies of scale
and large visibility. Nevertheless, a number of operators did large
investments on spectrum and licenses, when possibly the market
share will be insufficient for all operators to do business.
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2.2 Forecasting Demand
There are a multitude of studies into the likely take up of new
mobile data services, carried out by the UMTS Forum [8], by leading
analyst companies [9], [10], and others commissioned by operators
[11] and vendors [12], [13]. It does appear from much of the
research that there is a strong possibility that take up will be
stronger than many pundits think. As an example, results of an
inquiry to residential and business persons on their will to use
certain mobile services are presented, Figure 2-1 and Figure 2-2,
[7]. With an eye on recent developments, these figures may be
interpreted in a relative sense, that is, the actual use will be
very much influenced by the tariffs for each service.
Figure 2-1: Percentage of residential respondents willing to use
each of the services [7].
Figure 2-2: Percentage of business respondents willing to use
each of the services [7].
The definition of customer segments to identify typical user
profiles is important for characterisation of UMTS demand. Three
customer segments within MOMENTUM are considered Business, SOHO
(Small Office /Home Office) and Mass-Market users and described in
Table 2-1. Residential or Mass-market groups and a Business group
are sensible choices for user groups: They are easily identified in
terms of work, age, and income. They have particular patterns of
using mobile services that are different enough
to treat separately.
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They have different take-up times and rates, i.e., Business
users are easily associated with the early adopters while the
Mass-market falls more in the followers category.
A group that falls in between Business and Mass-market is the
SOHO user group, also known as the small and medium-sized
businesses; the spatial locations usually provide enough
information to pinpoint this user group.
Segment Description
Business Early adapters, with intensive and almost entirely
professional use, primarily during office hours.
SOHO Followers, with both professional and private use, during
the day and in the evening. Mass-market With low use, with flat
traffic levels.
Table 2-1: Customer segmentation.
Based also on the evolution of the GSM market in European
countries, it is assumed that UMTS will first attract the high-end
mobiles customers, mainly professionals who will require wideband
capabilities while away from the office. SOHO users and the
Mass-market segment will also be drawn to UMTS, not only because of
the new services, but essentially because voice will be migrated
onto this system. When UMTS is introduced, the mobile market will
be quite close to saturation, and UMTS subscriptions will mainly
replace existing ones. An example of segment market share evolution
is given in Figure 2-3, identifying the evolution of the usage
share of UMTS among segments. Note that this does not represent the
true figures used in the scenarios, but merely illustrates the type
of information needed.
Figure 2-3: Example of UMTS segment market share evolution.
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3 Service Set
UMTS offers the technical possibility to provide a broad set of
services and applications with different characteristics and target
users. Data transfer, video-telephony, and multiple applications
for E-commerce are foreseen, among many others, for deployment
within UMTS, which constitutes an absolute novelty in mobile
communications. In deliverable D1.1 [14], various perspectives into
services classification proposed in the literature by different
bodies (ITU-T, 3GPP, ETSI, and UMTS Forum) are presented. The 3GPP
approach is taken for future work. In deliverable D1.2 [15], a
detailed description and characterisation of UMTS services and
applications is presented. First, service classes and taxonomy of
parameters used for characterisation are presented. 25 foreseen
services and 54 applications are then identified and described into
detail. All services are classified according to 3GPP classes,
applications are associated to each service and characterised, for
the identified parameters. Most people now agree that there will
not be a service that will conquer the market. Some claim it will
be a number of small killer applications, or that it will be
personalisation of services that are tailored to individuals needs.
A set of 8 services is proposed in MOMENTUM for simulation [15], as
a killer cocktail: Speech-telephony: Traditional speech-telephony.
Video-telephony: Communication for the transfer of voice and video
between
two locations. Streaming Multimedia: Service that allows the
visualisation of multimedia
documents on a streaming basis, e.g., video, music, or slide
show. Web Browsing: This is an interactive exchange of data between
a user and a
web server. It allows the access to web pages. This information
may contain text, extensive graphics, video and audio
sequences.
Location Based Service: Interactive service that enables users
to find location-based information, such as the location of the
nearest gas stations, hotels, restaurants, and so on.
Multimedia Messaging Service (MMS): A messaging service that
allows the transfer of text, image and video.
E-mail: A process of sending messages in electronic form. These
messages are usually in text form, but can also include images and
video clips.
File Download: Download of a file from a database. This killer
cocktail is heterogeneous enough to meet the foreseen demands of
future UMTS customers and to translate in simulations the diversity
of services and traffic patterns UMTS bears. Several considerations
have been taken into account for the choice of this set of
services. This set is quite representative in terms of the foreseen
services by several fora [8], [9], [10], [11], [12], [13]; as
shown, e.g., in the market evaluation study presented in Figure 2-1
and Figure 2-2.
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The four 3GPP service classes, grouping services according to
specific characteristics and performance requirements, are well
represented in the service set as described next [14]: From the
conversational class (characterised by symmetric and real-time
conversational pattern services, with low emphasis on signal
quality), Speech-telephony and Video-telephony services are chosen.
The bit rate and session volume strongly differs between these two
services, being important to handle this diversity in
simulations.
From the streaming class (characterised by real-time almost
unidirectional data flow applications with low delay variation,
which can be processed as a steady continuous stream) Streaming
Multimedia is chosen. This service covers both audio and video
streaming.
From the interactive class (characterised by request-response
pattern services, highly asymmetric, with low round trip delay and
high signal quality) Web Browsing and Location Based services are
chosen. The average DL session volume differentiates these two
services;
From the background class (non real-time asymmetric services,
with high signal quality), File Download, E-Mail and MMS services
are chosen. File Download is a bi-directional service but highly
asymmetric, most of the traffic being DL. The remaining services
are differentiated by their average bit rate and DL session
volume.
Detailed characterisation of services is presented in Table 3-1
following the taxonomy of parameters proposed in [15]. In
particular, these services are very dissimilar in terms of Downlink
(DL) session volume and indicative bit rate range, as shown in
Figure 3-1. The traffic flow also results very diverse, as
illustrated in Figure 3-2. A description of the services and source
models for simulation purposes, in the XML MOMENTUM format, is
presented in deliverable D5.2 [16].
Dat
aR
ate
[kbp
s]
400
0
80
160
240
320
40
120
200
280
360
0.1 1 10 100 1000
Data Volume [kByte]
VideoTlphny
StreamMM
SpeechLocationbased
MMS
Email FileDwnld W W W
Conversational
Streaming
Interactive
Background
3GPP Classes:
Figure 3-1: Service set bit rate range and DL session
volume.
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The description of some characterisation parameters used in
Table 3-1 is presented below: Information type: sound, video, text,
data, still image. Intrinsic time dependency: time-based (TB, where
data blocks must be
displayed consecutively at predetermined time instants), or
non-time-based (NTB).
Delivery requirements: real-time (RT, for immediate
consumption), or non-real-time (NRT, stored for later
consumption).
Directionality of Connection: unidirectional (Uni), or
bidirectional (Bid). Symmetry of Connection (for Bid connections):
symmetric (Sym), or
asymmetric (Asy). Number of Parties: one-to-one (O-O), or
one-to-many (O-M). Switching mode: Packet Switched (PS), or Circuit
Switched (CS). Source model: Final description of source models
will be found in D2.7 [17].
These models will give more precise values or a full stochastic
for the following parameters: "#the source bit rate and the average
bit rate "#DL session volume
The bearers that are used to transport the information; when
more than one possibility exists, the probability that a certain
bearer is chosen is indicated, based on an eduacated guess.
Average Duration: average duration and DL session volume are
directly related by the DL average source bit rate.
Maximum transfer delay: This is the maximum time used to
transmit information through the air interface and the UMTS
network.
Burstiness: ratio between peak and average bit rates. Block
Error Ratio (BLER) target. Other parameters related with the
mobility type are presented in chapter 6.
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Figure 3-2: Service set traffic flow characterisation during a
session (time and bit rate domain) [2].
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Bit rate [kbps] Bearer
1 BLER
Class Service
Info.Type
TB
/NT
B
RT
/NR
T
Uni/B
id
Sym/A
sy
Parties
CS/PS
Source Models
Source Bit rate
range [kbps] UL DL UL DL
DL session volume
[kB]
Average Duration
[s]
Max. Transf. Delay
[s]
Burst- iness
UL DL
Speech- telephony Sound TB RT Bid Sym O-O CS
Speech Telephony 4 - 25 12,2 12,2 Speech Speech 91,5
2 120 0,15 1 - 5 0.010 0.010
C
o
n
v
e
r
s
.
Video- telephony
Sound Video TB RT Bid Sym O-O CS
Video- telephony 32 - 384 100 100 CS64 CS64 1500 120 0,15 1 - 5
0.002 0.002
S
t
r
e
a
m
.
Stream. MM MM
TB/ NTB RT Bid Asy O-O PS
3 Stream.
MM 32 - 384 3 60 - PS128 (10%) PS64 (90%) 2250 300 10 1 0.000
0.002
Web- browsing MM TB RT Bid Asy O-O PS
Web- browsing < 2000 1 30 -
PS384 (1%) PS64 (90%) PS32 (9%)
1125 300 4/ page 1 - 20 0.010 0.010
I
n
t
e
r
a
c
t
v
e
Location Based MM
TB/ NTB RT Bid Asy O-O PS
Location Based < 64 1 10 -
PS128 (1%) PS64 (90%) PS32 (9%)
22,5 180 0,2 1 - 20 0.010 0.010
MMS MM TB NRT Uni4 Asy O-O PS MMS < 128 30 30 PS64 (90%) PS32
(10%)
PS128 (1%) PS64 (90%) PS32 (9%)
60 16,2 300 1 - 20 0.010 0.010
E-Mail Data NTB NRT Uni Asy O-O PS E-Mail < 128 30 30 PS64
(90%) PS32 (10%)
PS128 (1%) PS64 (90%) PS32 (9%)
10 2,4 45 1 0.010 0.010
B
a
c
k
g
r
o
u
n
d
File Dwnld. Data NTB NRT Bid Asy O-M PS File Dwnld. 64 - 400 1
60 - PS128 (1%) PS64 (90%) PS32 (9%)
1000 132 0,5 1 - 50 0.010 0.010
Table 3-1: Service set parameters [2].
1 All bearers are DCH; the corresponding EbNo ! BLER table is
given in the Appendix. The percentages behind the bearers indicate
the (guessed) probability that this service is mapped to this
bearer during a long simulation. 2: For the calculation of the
equivalent Speech-telephony call volume, an activity factor of 50%
was considered. 3: Streaming Multimedia can also be CS for the case
of video streaming. 4: MMS and E-Mail are unidirectional services,
existing in a session as an UL or DL transmission, but never both.
5: For the E-Mail maximum transfer delay, a server access of 4
seconds was considered.
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4 Traffic Estimation
4.1 Initial Considerations
Once it is known which services future UMTS subscribers can use
(the service set described in the previous section), a closer look
to the more specific question where is the demand for these
services? is taken. Several approaches have been found in the
literature addressing the estimation of traffic demand. In [18] a
detailed description of the mapping mechanisms leading to a traffic
and mobility characterisation is provided, for a given combination
of UMTS environments/services/QoS requirements/systems. In [19]
theoretical and practical aspects related to the dimensioning of
hybrid traffic for 3G systems are discussed, combining user
profiles and geographical distribution of users concepts. In [20],
a method for the estimation and characterisation of the expected
tele-traffic in mobile networks is presented, based on a
geographical traffic model obeying the geographical and
demographical factors for the demand for mobile communication
services. In [5], the evaluation of UMTS demand is analysed,
presenting usage hypotheses and scenarios that provide a basis for
estimating the traffic load /km to be handled by third-generation
mobile systems. In MOMENTUM a global approach is used, combining
several aspects of the ones observed in the literature. These are
referenced along the description of the current approach. It
corresponds to a simple but efficient way of estimating traffic
demand, based on the available data from operators for the
scenarios to characterise. The estimation of UMTS services usage
corresponds to observe the following reality: An operational
environment with UMTS users spread over it generating calls
according to specific services usage patterns. A complete picture
of the processing is illustrated in Figure 4-1. The three key
elements to build a traffic scenario are: An user profile,
describing how a subscriber generates calls; An operational
environment; Spatial distributions of segmented subscribers, based
on a population
distribution. The way each of these elements is built in order
to generate a traffic scenario is described in this section. Taking
into account the guidelines that were defined, scenarios are
dimensioned for a desired deployment: a reasonable or extreme/worst
case scenario in terms of service usage, a forecast for a certain
year, a specific service usage forecast (e.g., not including
speech, which could be independently supported by GSM), etc.
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The visualisation of data has been done by using MapInfo GIS
tool, and all the processing described in this section was
performed with programming tools developed in MapBasic and C
programming languages. As an example of the needed data and
processing for generation of a traffic demand scenario, the centre
of Lisbon is illustrated, an area of 4 km x 4 km. The presented
data has a 20 m x 20 m resolution. Under the scope of the MOMENTUM
project, from real data, several traffic demand scenarios for
cities of Portugal (Lisbon and Porto), Netherlands (The Hague and
Bilthoven) and Germany (Berlin, Hanover, Karlsruhe) have been
generated according to different forecasts. Berlin, Lisbon and The
Hague scenarios will be available at the MOMENTUM site as public
scenarios, for benchmark in the development of planning tools.
Customer Segments Op. Env. share [%]
Population distribution
Penetrationof UMTS
Subscribers
Subscribers grids
BHCA grids /service/segment
Daily Call Attempts
UMTS usage in the BH
BHCA table
BHCA grids /service
User Profile
Operational Environment
Traffic scenario
OperatorMarket Share
Customer Segments Op. Env. share [%]
Population distributionPopulation distribution
Penetrationof UMTS
Subscribers
Penetrationof UMTS
Subscribers
Subscribers grids
BHCA grids /service/segment
Daily Call Attempts
UMTS usage in the BH
BHCA table
BHCA grids /service
User Profile
Operational Environment
Traffic scenario
OperatorMarket Share
OperatorMarket Share
Figure 4-1: General process for the construction of a traffic
scenario.
4.2 User Profile
To characterise the diversity of service usage patterns, three
customer segments are considered Business, SOHO and Mass-Market
users, as presented in Section 2.2. Each customer segment has a
specific profile of usage, generating calls of each service
according to a specific usage pattern. A table of service set usage
is defined for each segment, characterising the call generation
pattern for each service of the set. The used parameter to
characterise each service usage by a user is the Busy Hour Call
Attempt (BHCA), which indicates the average number of calls
performed in the busy hour. In this way, the user profile is
characterised by service set BHCA tables. BHCA tables are built
based on marketing data. They are dependent on many factors such as
the country under study, specific marketing strategy of pretended
UMTS usage, etc. They can be adapted, e.g., to a general increase
of services usage by subscribers of a certain customer segment, or
an increased use of specific services. To build these tables, a
similar approach to the one used in [5] is
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followed. First the total number of calls per day a subscriber
of each customer segment performs is estimated. In Table 4-1, an
example for Lisbon is presented for the year 2005. First
simulations using the BHCA-grids derived from the numbers for the
common settings as defined in [2] shows that the offered traffic is
by far higher then the traffic. Analysis of the basic assumptions
revealed that the BHCA-assumptions included in Report No. 6 of the
UMTS Forum [21] are much lower. Taking into account that the
numbers in the UMTS Forum report are based on a market study from
1997 where the whole mobile market was much more optimistic than
today, the UMTS Forum numbers can be seen as an upper limit.
Telecel/Vodafone, taking into consideration that the figures should
not exceed the UMTS Forum values, estimated the presented values
for Lisbon.
Service Business SOHO Mass-
Market Speech-telephony 4.167 2.400 1.768 Video-telephony 0.900
0.864 0.679 Streaming multimedia 0.600 0.576 0.170 Web browsing
0.400 0.256 0.075 Location Based 0.023 0.022 0.013 MMS 0.230 0.221
0.078 E-Mail 0.138 0.110 0.087 File Download 0.180 0.115 0.068
Table 4-1: Number of calls per day per customer segment.
A busy hour usage per customer segment is also estimated [5], as
being the percentage of traffic per day taking place during the
busy hour. In Table 4-2, busy hour usage values per customer type
are presented.
Customer Segment Busy hour usage [%] Business 20 SOHO 15
Mass-market 7
Table 4-2: Busy hour usage per segment.
It can be seen from the above tables that Business users use
UMTS services mostly on specific (busy hours) times of the day,
whereas the demand from the Mass-market is evenly spread. By
multiplying Table 4-1 and Table 4-2, a BHCA table per user type can
be built, as presented in Table 4-3. According to the prediction
for the scenario, all values are specified, resulting in this final
table. The three chosen customer segments represent early adapters
(Business users), followers (SOHO users) and the Mass-market. By
changing the penetration and usage of UMTS services in each group,
we are able to assess e.g. an early, medium or mature market
situation. The characteristics of each service (average duration,
rate, etc) can be specific per customer segment, evidencing once
more the flexibility of the machinery.
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Service Business SOHO Mass-
Market Speech-telephony 0.833 0.360 0.124 Video-telephony 0.180
0.130 0.048 Streaming multimedia 0.120 0.086 0.012 Web browsing
0.080 0.038 0.005 Location Based 0.005 0.003 0.001 MMS 0.046 0.033
0.005 E-Mail 0.028 0.017 0.006 File Download 0.036 0.017 0.005
Table 4-3: Average number of calls in the busy hour (BHCA) per
service and customer segment subscriber.
4.3 Operational Environment
To build a traffic scenario for a certain city, the
identification of the different existing operational environment
classes is essential. This characterisation, that is intended to be
as realistic as possible, has to translate the diversity of the
scenario, identifying regions with similar characteristics in terms
of land use and usage. A set of classes to characterise the
operational environment is proposed and characterised by MOMENTUM
in Table 4-4. Class Description Water Sea and inland water (lakes,
rivers). Railway Railway. Highway Highway. Highway with traffic
jam
Traffic jam in a highway, corresponding to a lot of cars
stopped, or moving at a very low speed.
Road Main road of relatively high-speed users, typically
inserted in suburban and rural areas. Street Street of low-speed
users, typically inserted in an urban area.
Rural
Rural area, with low building and high vegetation density; Area
with low population density, mainly of residential and primary
sector
population; Little commerce.
Sub-urban
Sub-urban area with medium building and vegetation densities;
Area with medium population density, mainly of residential and
secondary
sector population; Little commerce.
Open Small pedestrian land area (square, open area, park, large
pedestrian areas along streets) surrounded by mean urban, dense
urban, or residential areas.
Urban Area with high building density and low vegetation
density; Area with high population density, mainly of tertiary
sector with some
residential population. Central Business District (CBD)
Area with very high building density, very high buildings, with
almost no vegetation.
Area with very high population density, with tertiary sector
population.
Table 4-4: Momentum operational environment classes.
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For each city, operators have provided a large set of data for
the specification of an associated operational environment. For the
Lisbon public scenario, the example being presented in this
deliverable, Telecel/Vodafone MOMENTUM partner has provided a large
set of data [22] consisting of: Raster land use data: a pixel grid
of 20 20 m2 resolution with information of
Vodafone specific land use classes (water, buildings, open
areas, etc.) of each pixel, presented in Figure 4-2;
Vector data: identifying streets (highways, main roads,
streets), railways, and coastlines configurations, illustrated in
Figure 4-3.
Figure 4-2: Lisbon land use data thematic map [22].
Figure 4-3: Lisbon vector data thematic map [22].
A mapping is made of the specific Vodafone/Telecel raster and
vector classes onto the MOMENTUM operational environment classes
[2]. The resulting operational environment grid for Lisbon is
presented in Figure 4-4.
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Figure 4-4: Lisbon Operational Environment.
4.4 Population distribution
The starting point to characterise the distribution of
subscribers is the spatial distribution of population. To obtain a
refined population distribution, some processing is needed.
Available data for Lisbon consists of resident population and
workplaces per district, as well as population pendulum movement
values [23] statistics of the number of persons entering or leaving
each day the city from surrounding districts. As a first step, the
distribution should correspond to the period of the day under
study. This is obtained weighting residential population data with
workplaces data per district, combined with pendulum movements of
population in and out the scenario under study during the day, as
described in [2]. The obtained population distribution, in the
resolution of district areas, is presented in Figure 4-5 b). The
ranges of the presented picture are determined according to an
algorithm [24] such that the difference between the data values and
the average of the data values is minimized on a per range basis.
This reduces error and enables to obtain a truer data
representation, resulting in a more refined visualisation of the
spatial characteristics of distribution of population.
A more realistic and refined population spreading over the
geographic scenario is needed for a resolution similar than the
operational environment (20 m x 20 m for Lisbon). Weighting is
applied according to the operational environment classes [2], to
account for the different relative probability that a user in a
certain district will be located at each operational environment
class. As an example, population of a certain district will be more
concentrated in CBD areas than in forests.. Users are in this way
be spread in a more refined way. For the city of Lisbon, Figure 4-5
c) presents the resulting day population distribution weighted by
the operational environment classes. It must be clear that this
processing results simply in a better distribution of population.
The total population per district and globally in the entire area
under study is kept constant.
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a) Common legend b) Original population data. c) Processed
refined population data. [persons/km2]
Figure 4-5: Lisbon population distribution during the day.
For certain areas, the estimation of population was not precise
enough. With the presented approach, it is difficult to have an
estimation of population on a highway crossing a rural area without
population (typical situation in some of the received data).
Population needs to be independently estimated in the following
situations: In highway, highway with jam, road and street pixels
without population; In all highway and road pixels crossing rural
or open areas (even if there is
population data); In all railway pixels. First is estimated that
each car contains in average 1.5 persons. Evenly distributed cars
are assumed, with a certain average distance between the cars,
depending on the type of vector environment. In this way can be
calculated how many persons are present on average on each pixel
(the resolution of the final data is per pixel). In Figure 4-6 is
presented the empirical way how, for each vector operational
environment class, the number of persons per pixel is estimated.
The accepted values by all MOMENTUM partners are presented in Table
4-5. Considering that the values presented in Table 4-5 correspond
to a vector overlapping a CBD pixel, for the other non-vector
classes (rural, suburban, open, urban) specific weights are applied
to the presented values, as presented in Table 4-6, resulting in a
final number of persons per pixel.
Class Persons/Pixel Street 2.4 Road 3.4 Highway 2.4 Highway jam
7.2 Railway 0.6
Table 4-5: Persons per pixel in vector operational environment
classes (for a grid of 20 m x 20 m pixel size resolution).
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Class Weight Water 0.2 Rural 0.2 Suburban 0.4 Open 0.6 Urban 0.8
CBD 1.0
Table 4-6: Weights per non-vector operational environment class,
to be applied in estimated population per pixel in vector
operational environment classes where population in that pixel is
0.
For different pixel sizes, a factor is applied in order to adapt
these values. As an example, for a grid of 10 m x 10 m pixel
resolution, values are divided by 2, since vector data is
considered having always the width of a pixel. For 5 m x 5 m pixels
all final values are divided by 4.
Street
Road
25 m
35 m
Highway
Highwayjam
1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
Railway
50 m
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers/car
200 pers/train
10 m
50 m
6500 m
Street
Road
25 m
35 m
Highway
Highwayjam
1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
1.5 pers 1.5 pers
Railway
50 m
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers 1.5 pers 1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers
1.5 pers/car
200 pers/train
10 m
50 m
6500 m
Figure 4-6: Calculation of persons per pixel in the different
vector operational environment classes.
In other cases, a better estimation of the population was
obtained extrapolating from GSM speech traffic data. This happened
e.g. in exposition areas, train stations, or certain areas where no
accurate population was available. From the operators GSM speech
traffic data in the busy hour the number of persons was estimated,
considering a certain fixed traffic per person (25 mErl for the
case of Vodafone) and a penetration of the GSM operator (35%
penetration for Vodafone). The combination of all these processings
result in a refined population distribution, representing a good
basis for the construction of a subscribers distribution.
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4.5 Subscribers distributions
To build subscribers distributions, population is split into
three customer segments (Business, SOHO and Mass-Market). The way
population is split is new, dependent on the operational
environment and specific to the type of available data. In [19]
e.g., the availability of residence and business demographical
databases as well as road traffic databases allows a different
approach for the construction of subscribers distributions. In
MOMENTUM, population of each customer segment is spread differently
over the operational environment, according to their
characteristics (e.g., in CBD a percentage of Business users higher
than Mass-market ones, and the opposite in a rural area). A
customer segment share table is defined per operational environment
class; this gives a spatial distribution of customer segments share
according to each class, Table 4-7. Values were defined together
with MOMENTUM operators, which have extended marketing sources and
experience on these matters. For each customer segment, this table
tells where customers spend their time during the period under
consideration. This is an important characteristic of users,
identifying the areas where they are typically present. Since users
have a specific service usage, can already be foreseen that this
effect will result in a specification of the localisation of usage
of certain services.
Operational Environment Class
Business SOHO Mass- market
Water 35 35 30 Railway 20 40 40 Highway 60 30 10 Highway with
traffic jam 60 30 10 Main road 30 40 30 Street 10 20 70 Rural 2 3
95 Sub-urban 5 15 80 Open 25 40 35 Urban 25 40 35 CBD 80 10 10
Table 4-7: Operational environment share between customer
segments (in %).
Only a certain percentage of the total population in the
scenario will be a UMTS subscriber of a certain operator. The
penetrations of UMTS per customer segment and per operator market
share estimate this percentage. For the example of Lisbon,
penetration of UMTS is presented in Table 4-8 for the different
segments, an operator market share of 45% being considered for
2005. These values are market and operator dependent, resulting
from predictions how the market will evolve.
Business SOHO Mass- Market Penetration 11.25% 6.75% 2.25%
Table 4-8: UMTS subscribers penetration, per segment and for a
specific operator.
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The combination of Table 4-7, Table 4-8 and Figure 4-4 results
in three UMTS penetration distributions, per customer segment, as
presented in Figure 4-7. These pictures illustrate clearly the
different penetrations of UMTS, depending on the operational
environment class and the customer segment. Many effects resulting
from the dimensioning of these tables are identifiable graphically,
e.g.: Higher penetration of business UMTS subscribers in CBD
(9.00%) than SOHO
(2.70%) or Mass-Market (2.13%) subscribers; Higher penetration
of Mass-Market UMTS subscribers on streets (1.57%) than
business (1.12%) subscribers.
a) Business. b) SOHO. c) Mass Market. d) Legend [% persons].
Figure 4-7: Lisbon UMTS penetration, per customer segment.
Applying these penetrations to the refined population
distribution results in three customer segments subscriber spatial
distributions, illustrated in Figure 4-8. It is interesting to
discuss some visual effects on the resulting segmented subscribers
distributions (grids): The effect of the different non-uniform UMTS
penetration distributions on the
population distribution results in a graphical distortion of the
population distribution, Figure 4-5 c). As an example, for the SOHO
subscribers, it can be seen how different are Figure 4-5 and Figure
4-8 b), where in many areas the spatial distribution has
increased/decreased relatively.
In CBD areas crossed by streets, which can be identified in
Figure 4-4, the number of Mass-Market subscribers is higher on
streets than on CBD areas, Figure 4-8 c), as dimensioned in Table
4-7, even if the population grid, Figure 4-5, specifies the
opposite (containing less people on streets than on CBD area). The
opposite happens for the Business segment, Figure 4-8 a), where
more users are present in CBD areas than on streets.
The higher Business subscriber density area does not happen on
the higher population density area, an Urban area. It happens on a
CBD area, where the effect of the operational environment share
percentage (25% in Urban versus 80% in CBD) results in a higher
subscribers density on the CBD area.
The effect of different UMTS penetration values per segment
(11.25% for Business versus 2.25% for Mass-Market) results, almost
in the entire scenario, on a higher Business users density than
Mass-Market one.
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a) Business. b) SOHO. c) Mass Market. d) Legend
[persons/km2]
Figure 4-8: Lisbon UMTS subscribers, per customer segment.
It can be concluded that the combined effects of a refined
population distribution, the operational environment and the UMTS
penetration, result in a refined way of building segmented
subscriber distributions, very dissimilar from the initial
population distribution.
4.6 BHCA grids
The combination of the spatial distributions of subscribers with
the BHCA table results in traffic forecasts for the services usage
per customer segment. These are expressed in terms of BHCA grids,
where for each unit of area (pixel), the average number of new
calls in the busy hour is specified, per service and customer
segment. 24 BHCA grids make this resulting traffic demand scenario,
one per customer segment and per service, as illustrated in Figure
4-1. In Figure 4-9 a, b and c, the Video-telephony BHCA grids for
Business, SOHO and Mass-Market users are presented, using natural
break ranges, specific for the image of each segment. Values of
BHCA are presented per pixel (in the case of Lisbon corresponding
to a 20 x 20 m2 pixel).
a) Business. b) SOHO.
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c) Mass-Market.
Figure 4-9: Video-telephony BHCA grids [calls / hour / 400 m2
pixel].
Comparing the usage of Video-telephony by Business segment
versus Mass-Market segment, it can be observed that the effect of a
higher UMTS penetration (11.25% vs 2.25%, from Table 4-8) and of a
higher usage of Video-Telephony (0.180 call/h vs 0.048 call/h, from
Table 4-3) of Business users than of Mass-Market ones, results
globally on higher BHCA values for Business users than Mass-Market
one (maximum values of 0.575 versus 0.043 call/h/pixel). Many other
effects are directly related with the ones identified on the
segmented subscriber distributions. These refined and assorted
figures result from the high number of different available screws
to create a rich traffic forecast, which can be adapted to an
expected or desired reality. Each one of these BHCA grids is
directly proportional to the corresponding segmented population
grids, illustrated in Figure 4-8 (nevertheless, the different range
system used custom ranges versus natural break ranges doesnt allow
the direct comparison). This was expected, since the processing to
obtain the BHCA grids corresponds to multiply the each customer
segment subscriber grid by the corresponding factor obtained from
Table 4-3, for each service and corresponding customer segment. If
we compare the Mass-Market BHCA grid for Video-telephony and for
Speech-telephony, Figure 4-9 a) and Figure 4-10, we observe that
the resulting BHCA values are directly proportional to each
other.
Figure 4-10: Mass-Market/Speech-telephony BHCA grid [calls /
hour / 400 m2 pixel]
For each service, three BHCA exist, one per customer segment,
allowing specific characteristics of the same service. As an
example, a Business user speech call
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might have average call duration of 3 minutes, while it can be
dimensioned for 2 minutes for the remaining segments. This
diversity can also be applied to quality parameters, bearers or
priorities. Nevertheless, in MOMENTUM, services characteristics are
considered similar among customer segments. In this way, for each
service, the three BHCA grids can be added. This results in 8 BHCA,
one per service. In Figure 4-1 the global processing to obtain
these final grids is illustrated. In Figure 4-11 the resulting BHCA
grids per service for Lisbon are represented. Common ranges allow
the direct comparison of BHCA values between services. It can be
seen how different the resulting service BHCA distributions are.
Location based service is the one with lower usage. In fact, this
service is the one having the lowest BHCA values in Table 4-3, for
all segments. Speech-telephony, Web browsing and E-Mail are
services with high usage, but with very different resulting
distributions. Even knowing that for a specific segment, all BHCA
spatial distributions are directly proportional, (e.g. Figure 4-9
and Figure 4-10), note that the resulting BHCAs per service are all
different. This is due to the fact that each BHCA/service figure
results from the combination of three uniquely weighted
BHCA/service/segment figures. This evidences the importance of
splitting in segments the calculation of BHCA grids, before being
added.
a) Speech-telephony. b) Video-telephony. c) Streaming MM
d) Web Browsing. e) Location-based. f) E-Mail.
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g) MMS. h) File Download. i) Common legend [calls/hour/400 m2
pixel].
Figure 4-11: Service set BHCA grids. Business users, relatively
to SOHO and Mass-market users, are very strongly present in all
distributions due to the high UMTS penetration Table 4-8 and high
service usage Table 4-3. This results, for almost all services, in
high BHCA values where business users predominate (e.g. CBD areas).
Nevertheless, if for a certain service, the combination of the BHCA
value and UMTS penetration would be higher for the Mass-Market
segment than for Business one, the resulting BHCA service
distribution would have high BHCA values, e.g., on streets,
something that does not happen for any service. The Location based
BHCA distribution, Figure 4-11 e), seems to be uniform from the
presented picture. Nevertheless, this is an erroneous conclusion
due to the common used scaling, Figure 4-11 i). If the same data is
represented using an equal range scaling, as illustrated in Figure
4-12, it can be seen that the distribution is in fact very diverse
in space.
Figure 4-12: Location Based BHCA grid [calls/hour/400 m2 pixel],
for an equal range scale representation.
All these effects are achieved thanks to a high number of
available intuitive screws, having a natural link with real and
measurable data/parameters. This allows, for a specific area, the
dimensioning of services traffic forecast distributions according
to an expected or desired set of characteristics.
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5 Mobility Scenario
5.1 Introduction
Mobility is one of the major characteristics of wireless
systems. With the large range of services UMTS will support, the
old anywhere, anytime wireless systems premise can be extended with
UMTS to anywhere, anytime, anything, within a certain range. It
represents a big challenge for cellular planning. In most of the
environments, the mobility characteristics of the terminals have a
direct influence on the cell radius, and in turn on the investment
cost of the network. A recent investigation [25] has quantified
this effect, and has shown that the investment cost can increase by
as much as 60% in environments where high terminal speeds prevail.
Based on these facts, it is important to characterise the diversity
of mobility types existing in the operational environments, so that
inherent mobility characteristics of each environment are properly
taken into account in simulations, Figure 5-1.
Rural
Sub-urban
Urban
Water
Railway
Highway
Major streetMajor road
100% Pedestrian100% Vehicular/Highway
50% Static50% Pedestrian
10% Static30% Pedestrian60% Vehicular/Major Road
10% Pedestrian90% Vehicular/Major Street
Rural
Sub-urban
Urban
Water
Railway
Highway
Major streetMajor road
100% Pedestrian100% Vehicular/Highway
50% Static50% Pedestrian
10% Static30% Pedestrian60% Vehicular/Major Road
10% Pedestrian90% Vehicular/Major Street
Figure 5-1: Mobility scenario, identifying different mobility
types associated to the operational environment classes.
Mobility scenarios are built in MOMENTUM for the more realistic
characterisation of scenario of users characterised by specific
mobility patterns. This is of special interest for dynamic
simulations where, during simulation, motion of users is simulated
in the most realistic way. Also for static simulations this is
important. Mobility will have also an impact on the spread of load
of average load grids, used for generating snapshots. Mobility
scenarios will be characterised by:
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A mobility type penetration table per operational environment
class, for the generation of moving active users;
A mobility grid per mobility type, as an implementation that (a)
constrains the movement of users with its mobility type to a
certain area and (b) precisely describes user movement on sample
time-level.
To generate a realistic and diversified mobility scenario,
different mobility types are then identified, according to their
speed and movement type: Static; Pedestrian; Vehicular (highway,
main road, street and railway). For each mobility type, PDFs for
speed and discrete direction of motion are presented using the
proposed generic Momentum mobility model. These mobility types are
then mapped onto the operational environment in certain
percentages, Figure 5-1. When a user is generated in a certain
pixel, a mobility type is randomly going to be attributed (and will
in general remain fixed).
5.2 Mobility Model
Several sources have suggested mobility models, according to
different criteria, pointing out the key parameters for model
customisation. An overview of the main existing mobility models and
key parameters for model customisation was presented in [2]
describing the following models: Random Walk Modelling [26];
Mobility Model with Triangular Velocity Distribution [27];
Simulation of a Mobile Highway Traffic [28]; Mobility Models
described in ETSI [29] for:
"#Indoor Office Scenario; "#Outdoor to Indoor and Pedestrian
Scenario; "#Vehicular Scenario; "#Mixed-cell Pedestrian/Vehicular
Scenario.
Mobility Model Described in COST 259 [30]; A model for
simulation of mobility in MOMENTUM was proposed and described. It
combines the Mobility Model with Triangular Velocity Distribution
[27] (for velocity estimation) and the COST 259 mobility model [30]
(for discrete direction of motion estimation). These models where
chosen due to their simplicity, still accounting for the main
mobility characteristics. In addition, considering that users move
in a pixel grid mobility scenario, the resulting vector describing
the probability of taking a direction is converted into a vector
describing the probability of crossing a side to a neighbouring
pixel by including the effect of speed, pixel size, sample time and
holding time.
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For the velocity PDF, the Mobility Model with Triangular
Velocity Distribution [27] is used with a specific average and
variation for each scenario (mobility type). Figure 5-2 represents
the triangular distribution and the respective parameters. For the
Direction of Motion Estimation, the mobility model described in
COST 259 [30] was chosen. This probability is defined by (5.1)
[30], where w/2, w-/2 and w are the weight factors corresponding to
probabilities, and is the standard deviation of the direction
distributions. Standard deviation is assumed to be equal for the
four variables.
( )( ) ( ) ( ) ( )
++++
+++
=
+
+
2
2
2
2
2
2
2
2
2
2
2222
2/
22
2/2
2/2/ 21
211
iiii
i
ewewewewe
wwwp i
(5.1)
A new term was added to the original equation to provide
symmetry of the direction function around rad. Both weight factors
and standard deviation will be specified for each scenario.
v [ms-1] Vmax Vav Vmin
2/(Vmax-Vmin)
f(v)
Figure 5-2: Velocity probability density function [27].
In order to generate a realistic and diversified mobility
scenario, different mobility types are proposed for simulation,
according to their type of motion and speed: Static; Pedestrian;
Main Road/vehicular; Street/vehicular; Highway/vehicular; Highway
traffic jam/vehicular;; Railway/vehicular.. For each mobility type,
PDFs for speed and discrete direction of motion are presented using
the proposed mobility model, modelling in this way the different
mobility patterns. For the average velocity and velocity variation,
some values where taken from [27] and others defined together with
MOMENTUM operators, which have a large experience on these matters.
For the MOMENTUM chosen mobility types, Table 5-1 summarises these
characteristics. Average velocity and
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variation are equal, except for Highway vehicular mobility type,
where cars never move below a minimum speed. The direction PDF is
described by (5.1) with parameters presented in Table 5-2. The
MOMENTUM mobility scenario is a pixel grid. Considering that user
motion is limited to transitions between pixels, only four possible
directions for the mobile unit, forward (0), back (180), left (90)
and right (-90) are considered possible, as illustrated in Figure
5-3. From the PDFs of each mobility type, the corresponding
direction probability value can be extracted for each of the four
possible directions of motion. Direction probability values, for
each mobility type, are presented in Table 5-3.
Mobility type Vav [ms-1] Vav [kmh-1] [ms-1] [kmh-1] Static 0 0.0
0 0.0 Pedestrian 1 3.6 1 3.6 Street/vehicular 10 36.0 10 36.0 Main
Road/vehicular 15 54.0 15 54.0 Highway/vehicular6 22.5 81.0 12.5
40.5 Highway with jam/vehicular 1 3.6 1 3.6 Railway/vehicular 22.5
81.0 22.5 81.0
Table 5-1: Mobility types average velocity and velocity
variation.
Mobility type w/2 w-/2 w Static - - - -
Pedestrian 5/8 5/8 1/4 /8 Street/vehicular 1/2 1/2 0 /8
Main Road/vehicular 3/14 3/14 0 /8
Highway/vehicular 1/8 1/8 0 /8
Highway with jam/vehicular 1/8 1/8 0 /8 Railway/vehicular 1/8
1/8 0 /8
Table 5-2: Mobility types PDF parameters.
Mobility type 0 90 180 Static 0 0 0 Pedestrian 40 25 10
Street/vehicular 50 25 0 Main Road/vehicular 70 15 0
Highway/vehicular 80 10 0 Highway with jam/vehicular 80 10 0
Railway/vehicular 80 10 0
Table 5-3: Probability of changing direction values, for each
mobility type.
6 The highway/vehicular model for Germany has a Vav of 35 ms-1
(126 kmh-1); the velocity variation remains unaltered.
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0
-90
180 IN
90
Figure 5-3: Possible pixel transition directions.
The mobility model is used in the dynamic simulators to generate
movement. More precisely, the movement of users is sampled, i.e.,
at different time-instances the position of the active user is
updated. The level of detail is defined by the granularity of the
raster, i.e., the pixel size. When these aspects are combined, we
can express the probability of changing position (or pixel) by the
generic direction vector (Table 5-3), the velocity (Table 5-1), the
pixel size, the sample time and the holding time. This last
quantity is assumed to be memory less, or exponentially
distributed.
5.3 Penetration of Mobility Types
For the generation of moving users, mobility types are mapped
onto the operational environment classes in a more or less
empirical approach. In Table 5-4, for each operational environment,
the percentages of users generated within a certain mobility type
are presented. The presented values are a rough estimate and were
defined together with MOMENTUM operators, which have large
experience and sensibility for these matters. Nevertheless, values
can be changed, expressing once more the flexibility of all the
defined machinery.
Mobility type [% of users] Operational Environment
class Static Pedestrian Street/
veh. Main road/
veh. Highway/
veh. Highway jam/ veh.
Railway/ veh.
Water
Railway 100 Highway traffic jam 100
Highway 100
Main road 5 95
Street 5 5 90
Open 10 90
Rural 10 90
Sub-urban 20 80
Urban 30 70
CBD 50 50
Table 5-4: Mobility types penetration table per operational
environment class.
When a user is generated in a certain pixel, a mobility type is
randomly allocated according to these percentages. As an example,
in a main road environment, 5% of the generated users are
pedestrian, while 95% are Main road/vehicular.
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Available services are strongly dependent on the mobility type.
Some services (e.g. video-telephony) are not supported when the
user moves with high speeds (highway). High bit rates and high
delay sensitivity of certain services restrict the possible
mobility types of active users. In Table 5-5, the possible mobility
types for each service are presented. Can be seen, e.g., that a
street/vehicular user driving at 36 km\h average speed cannot use
Video-telephony service.
Mobility type
Service Static Ped. Street/ veh. Main
road/ veh. Highway/
veh. Highway jam/
veh. Railway/
veh. Speech $ $ $ $ $ $ $ Video-tlphny $ $ $ Str. MM $ $ $ Web
brow. $ $ $ $ Loc based $ $ $ $ $ $ $ MMS $ $ $ $ $ $ $ E-Mail $ $
$ $ $ $ $ File Dwnld $ $ $ # $
Table 5-5: Possible mobility types for each service.
In this way, mobility types associated to certain operational
environment classes inhibit the availability of certain services.
In Table 5-6, the available services per operational environment
are presented. This table is obtained by combining Table 5-4 and
Table 5-5.
Operational Environment Classes
Service
Water
Rail-w
ay
Highw
ay traffic jam
Highw
ay
Main R
oad
Street
Open
Rural
Sub-urban
Urban
CB
D
Speech $ $ $ $ $ $ $ $ $ $ Video-telephony # $ $ $ $ $ $ $ $
Str. MM $ $ $ $ $ $ $ $ Web browsing $ $ $ $ $ $ $ $ Location based
$ $ $ $ $ $ $ $ $ $ MMS $ $ $ $ $ $ $ $ $ $ E-Mail $ $ $ $ $ $ $ $
$ $ File Download $ $ $ $ $ $ $ $
Table 5-6: Available services per operational environment
class.
The impact of Table 5-6 can be introduced directly on the BHCA
grids, where the non-available services in certain pixels will have
a corresponding BHCA of 0 for that service. Some considerations are
presented in what follows. Video-telephony is a service that is not
available for the Main Road/Vehicular mobility type (see Table
5-5). Nevertheless, in the Main Road environment the possible
mobility types are Main
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Road/Vehicular and Pedestrian. In this way, this service is
available on a Main Road environment (see Table 5-6), but only with
a Pedestrian mobility type. Considering a Street operational
environment, from Table 5-6 can be seen that in all services are
available. Nevertheless, for the case of Streaming Multimedia, this
service is not compatible with the Street/Vehicular mobility type,
being available for this service only two mobility types: Static or
Pedestrian. The corresponding mobility type penetrations from Table
5-4 have then to be rebalanced since Street/Vehicular mobility type
is not allowed. The values will be updated in order to keep 100%
total sum; one will then have 50% of probability that the Streaming
Multimedia user will have a Pedestrian mobility type and 50% for
the Static one. Taking into consideration the results of Table 5-6,
for each service, a rebalanced Table 5-4 is generated, as presented
in Table 5-7 to Table 5-9. These tables can be directly used in
simulations to randomly associate a mobility type to a new
generated service, in a pixel of a certain operational environment
class.
Mobility type [% of users] Operational Environment
class Static Pedestrian Street/
veh. Main road/
veh. Highway/
veh. Highway jam/ veh.
Railway/ veh.
Water
Railway 100
Highway traffic jam 100
Highway 100
Main road 5 95
Street 5 5 90
Open 10 90
Rural 10 90
Sub-urban 20 80
Urban 30 70
CBD 50 50
Table 5-7: Mobility type penetration table per operational
environment class for Speech-telephony, Location based, MMS and
E-Mail services.
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Mobility type [% of users] Operational Environment
class Static Pedestrian Street/
veh. Main road/
veh. Highway/
veh. Highway jam/ veh.
Railway/ veh.
Water
Railway
Highway traffic jam 100
Highway
Main road 100
Street 5 5 90
Open 10 90
Rural 10 90
Sub-urban 20 80
Urban 30 70
CBD 50 50
Table 5-8: Mobility type penetration table per operational
environment class for Web browsing and File Download services.
Mobility type [% of users] Operational
Environment class Static Pedestrian
Street/ veh.
Main road/ veh.
Highway/ veh.
Highway jam/ veh.
Railway/ veh.
Water
Railway
Highway traffic jam 100
Highway
Main road 100
Street 50 50
Open 10 90
Rural 10 90
Sub-urban 20 80
Urban 30 70
CBD 50 50
Table 5-9: Mobility type penetration table per operational
environment class for Video-telephony and Streaming multimedia
services.
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5.4 Implementation of mobility
5.4.1 Introduction
For a complete characterisation of a mobility scenario, besides
a mobility type penetration table per operational environment class
and per service (Table 5-7-Table 5-9) for the generation of moving
active users, Table 5-4, a detailed implementation of the mobility
types is essential for the control of users movement. The result is
a mobility grid where each pixel contains enough local information
to set the user in motion. One mobility pixel grid per mobility
type, Figure 5-4, will completely specify the motion of all users
of that mobility type in the scenario. For each pixel, the possible
transition sides are specified, with associated transition
probabilities values. In this way, users of a certain mobility type
are always kept in the specific mobility grid. In particular,
motion of vehicular users will be vector oriented, driving along
streets or railways. As an example, a moving Major Road/Vehicular
user will always drive in major roads. The user may turn in
crossings, according to a certain probability. Transition between
mobility types (e.g., to a Street/Vehicular mobility type) may be
allowed under special circumstances only between certain mobility
types and in specific connecting points. Mobility types are
characterised (besides their speed) by their movement type and
corresponding mobility grid: Static users are non moving users
(once generated in a certain pixel, they
remain always there); in this way, no mobility grid is
associated to this type; Pedestrian users are walking users that
move freely in all operational
environments except Water, Railway and Highway operational
environment classes, as illustrated in Figure 5-4 d);
Vehicular users are driving users, being their motion restricted
to their corresponding operational environment class (Highway,
Major Road, Street or Railway); specific vehicular mobility grids,
Figure 5-4 a) to c), specify the allowed motion pixels for Lisbon.
The area under study has no highway.
a) Street mobility grid. b) Major Road mobility grid.
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c) Railway mobility grid. d) Pedestrian mobility grid.
Figure 5-4: Mobility grids for the area of Lisbon under
study.
5.4.2 Vector to pixel grid conversion
The implementation of the mobility types is done using pixel
grids of a certain resolution with specific information for each
pixel. The data underlying the operational environments are of
raster and vector type, hence, a conversion of vector data onto
raster data must be performed in order to extract vector
information of the linking sides of pixels. As an example, the
simulator must have enough information about the Railway path to
move users along it; information has to be clear enough in order
not to place suddenly a Railway/Vehicular user in an urban area of
buildings or into water! This leads to the need of a format that
maintains vector information in a grid of pixels. In Figure 5-5 the
conversion of vector data into raster format is illustrated, for
the main road operational environment class of a certain area of
Lisbon. To maintain the vector information in a grid of pixels, the
key issue is to keep two types of information in the pixel grid:
Identification and properly labelling of the pixels crossed by
vectors; Identification, for each pixel, of the linking sides,
North, East, South or West
(N, E, S and W respectively); more precisely, for each pixel P,
an array of sides SP will contain binary information of each side
(N,E,S,W), indicating whether the appropriate side links (1) or not
(0) to an other pixel.
a) Main road data in Vector format. b) Main road data in pixel
grid format.
Figure 5-5: Conversion of vector to pixel data.
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As an example, for the illustrated Main road pixel in Figure
5-6, the linking sides are N and S, and the corresponding array of
sides for this pixel looks as (1,0,1,0).
N
S
EW
Figure 5-6: Pixel crossed by a street.
Considering that the simulator knows from which side a Main
road/vehicular user has entered the pixel (e.g. N), this
information is enough to know that this user will move to the pixel
linked to side S.
5.4.3 Updating the Direction Transition Tables
The presented pixel oriented direction probability model,
illustrated in Figure 5-3, will be influenced by the array of
sides. As a simple illustration of the required update, consider
the Street mobility grid in Figure 5-7 (b): For a Street/Vehicular
user entering pixel K from side N(orth), with SK =
(1,0,0,1), it makes no sense the existence of the two (turning)
possibilities of 25%. In fact, the direction probability will be
100% W(est) (considering 0% probability of going back);
For a Street/Vehicular user entering pixel A from side E(ast),
and considering SA = (0,1,0,0), the direction probability
distribution can only be 100% to the E(ast) side;
For a Street/Vehicular user entering pixel G from S(outh), and
considering
GS!
= (0,1,1,1), the only possible sides are E(ast) or W(est), which
in principle will be 50% for each side (since it is not possible to
determine a principal direction). Nevertheless, if the user enters
pixel G from E(ast), W(est) (straight direction) should have a
higher probability than S(outh) (turning left).
a) Vector street. b) Street in raster format.
Figure 5-7: Example of conversion from vector to raster
format.
In this way, for each mobility grid, a reference table of
direction probabilities should be made for each of the possible
configurations of the array of sides and for
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each of the possible entering sides. First, a generic reference
table is created, consisting of a possible array of sides and
related entrance sides (Table 5-10). The following abbreviations
are used: F forward direction; T turn direction; B back
direction.
Entrance side Array of sides N E S W
(1,0,0,0) (F,0,0,0) (0,1,0,0) (0,F,0,0)
(0,0,1,0) (0,0,F,0)
(0,0,0,1) (0,0,0,F) (1,1,0,0) (B,F,0,0) (F,B,0,0)
(1,0,1,0) (B,0,F,0) (F,0,B,0)
(1,0,0,1) (B,0,0,F) (F,0,0,B) (0,1,1,0) (0,B,F,0) (0,F,B,0)
(0,1,0,1) (0,B,0,F) (0,F,0,B)
(0,0,1,1) (0,0,B,F) (0,0,F,B)
(1,1,1,0) (B,T,F,0) (T,B,T,0) (F,T,B,0) (1,1,0,1) (B,T,0,T)
(T,B,0,F) (T,F,0,B)
(1,0,1,1) (B,0,F,T) (F,0,B,T) (T,0,T,B)
(0,1,1,1) (0,B,T,F) (0,T,B,T) (0,F,T,B)
(1,1,1,1) (B,T,F,T) (T,B,T,F) (F,T,B,T) (T,F,T,B)
Table 5-10: Transition array reference table, combining the
possible array of sides with the user entrance side to the
pixel.
As an example, a user entering a pixel with array of sides
(1,1,0,1) from W side, will have, from Table 5-10, (T,F,0,B) as
resulting transition array. This transition array identifies that:
N is a turning side; E is a forward side; W is a back side.
An adaptation must be then made of the direction probabilities,
summarised in Table 5-11. When certain directions are not possible,
the probability for the respective side(s) is 0%, the remaining
probabilities being rebalanced in order to obtain 100% again.
Resulting tables are calculated for all mobility types, and
presented in Appendix B.
Mobility Type Forward Turn left Turn right Back
Static - - - -
Pedestrian 40 25 25 10
Street/Vehicular 50 25 25 0
Main Road/Vehicular 70 15 15 0
Highway/Vehicular 80 10 10 0
High