comparison of different simulation approaches for cell performance evaluation.pdf

Contract Number: IST-2000-28088

Project Title: Models and Simulationsfor Network Planningand Control of UMTS

Project Acronym: MOMENTUM

n

Information Report Number: SIE_WP2_DR_PUB_027_WL_10_D2.2Date of Delivery: 2002-10-13

Report Title: Deliverable D2.2: Comparison of differentsimulation approaches for cell performanceevaluation

Editor: Thomas Winter, (Siemens)

Authors: Ulrich Trke (Siemens), Thomas Winter (Siemens),Ranjit Perera (UB), Eugen Lamers (UB), EllenMeijerink (KPN), Erik Fledderus (KPN), AntonioSerrador (IST)

Reviewers: Hans van den Berg (KPN), Tobias Pfender (ZIB)

Abstract: This deliverable report deals with two different simulationtechniques for UMTS network performance evaluation, a snapshot-basedapproach and a short-term dynamic simulation method. Beginning withdetails on source traffic models, traffic channels, and signalling, the userand traffic generation process is presented. For snapshot simulationsdifferent enhancements of the state-of-the-art are described. Performanceresults for different services are analysed and conclusions are drawn forimproving the static simulations in the next steps of the project. The short-term dynamic simulation method and its event triggering is introduced andconnections to the static snapshot approach are explained. A firstcomparison between the two methods is concluded with an outlook on thenext steps in the design of a suitable simulation method for UMTS.Key word list: UMTS, W-CDMA, MOMENTUM, snapshot simulations,short-term dynamic simulations, power control, traffic channels, radioresource management, user generation, source traffic models, IST, KeyAction IV, Action Line IV.4.1Key Action: IV, Essential Technologies and InfrastructuresAction line: IV.4.1, Simulation & VisualisationConfidentiality: MOMENTUM public

2/50

Document History

Date Version Comment Editor2002-06-12 0.1 Structure proposal Thomas Winter2002-06-17 0.2 Agreed structure of D2.2 Thomas Winter2002-06-19 0.3 First draft of introduction Thomas Winter2002-06-26 0.4 Draft of Chapters 2.2 and 3 Thomas Winter2002-07-04 0.5 Draft of Chapter 4 Thomas Winter2002-07-05 0.6 Draft of Chapter 5 and 6 Thomas Winter2002-07-10 0.7 Draft of Chapter 2.3 and 7 Thomas Winter2002-07-24 0.8 Update of Chapter 2.1 Thomas Winter2002-08-22 0.9 Update after review Thomas Winter2002-08-30 1.0 Final update Thomas Winter

SIE_WP2_DR_PUB_027_WL_10_D2.2 MOMENTUM PUBLIC

3/50

Contents

Contents 3

List of Figures 5

List of Tables 6

List of Notation 7

List of Symbols 8

1 Introduction 9

2 Service and Traffic Channels Modelling for Static and Short-termDynamic Simulations 11

2.1 Reference Services and Source Traffic Models............................. 11

2.2 Traffic Channels for Reference Services ....................................... 21

2.3 Call Admission in Static and Short-term Dynamic Simulations ... 23

3 Signalling Traffic 26

3.1 Uplink ............................................................................................ 26

3.2 Downlink........................................................................................ 27

4 Snapshot User Generation 29

4.1 User Density Map .......................................................................... 29

4.2 Setting User Positions .................................................................... 30

5 Static Simulation Concept 32

5.1 Introduction.................................................................................... 32

5.2 The Inner Loop .............................................................................. 33


4/50

5.3 The Outer Loop.............................................................................. 39

6 Short-term Dynamic Simulations 42

6.1 The concept.................................................................................... 42

6.2 Comparison between STD and dynamic simulation concepts....... 45

6.3 First results..................................................................................... 46

7 Summary 48

References 50


5/50

List of Figures

Figure 1-1: The deliverables of work package 2 and their interactions............. 9Figure 1-2: The simulation design process in work package 2........................ 10Figure 2-1: Multi rate services overview in time and bit rate domain............. 12Figure 2-2: Voice Source Model (extracted from [9])..................................... 13Figure 2-3: Typical WWW session (adapted from [6]). .................................. 15Figure 2-4: Classification of non-real-time IP traffic streams (extracted from

[7]). .......................................................................................................... 16Figure 3-1: Blue indicates variables that can be monitored during the

optimisation process. Red is the target variable that needs to be set.Black indicates constants or derived/intermediate variables................. 27

Figure 4-1: Conversion of polygon/vector data into pixel based user densitymap (traffic map). .................................................................................... 29

Figure 4-2: Positioning of the user based on random number drawn. ............. 30Figure 4-3: A sample snapshot. ....................................................................... 31Figure 5-1: In a snapshot simulation several independent snapshots are

considered................................................................................................ 32Figure 5-2: Static Simulation Concept Overview......................................... 32Figure 5-3: Comparison of simulation speed................................................... 34Figure 5-4: The standard way of analysing a snapshot.................................... 35Figure 5-5: Increased speed of convergence in case of PC on cell basis......... 37Figure 5-6: Instability due to underestimation of connection power............... 38Figure 5-7: Stabilised inner loop. .................................................................... 38Figure 5-8: DL noise plot for a 19 site/57 cell scenario with uniformly

distributed traffic. .................................................................................... 40Figure 5-9: Convergence speed depending on the traffic type (1/2). .............. 41Figure 5-10: Convergence speed depending on the traffic type (2/2). ............ 41Figure 6-1: Session of two users with activity change and session end as

example events. ....................................................................................... 44Figure 6-2: Two methods of event grouping and reduction of recalculation. . 45Figure 6-3: Cell load of one certain cell, single values. .................................. 46Figure 6-4: Cell load of one certain cell, mean value development. ............... 47Figure 7-1: Schematic view on the simulation design process in work package

2. .............................................................................................................. 48


6/50

List of Tables

Table 2-1: Voice Source Model Parameters (partial extracted from [9]). ....... 13Table 2-2: Distribution of Session arrival inter-arrival times.......................... 17Table 2-3: Distribution of Session Volume assumed in [7]............................. 17Table 2-4: Parameters of packet inter-arrival times......................................... 17Table 2-5: Statistical properties at connection level. ....................................... 18Table 2-6: Fractions of different packet sizes in overall traffic....................... 18Table 2-7: Service set characterisation for simulation purposes (adapted from

D1.3). ....................................................................................................... 19Table 2-8: Voice model resulting parameters for the different k states........... 19Table 2-9: Physical channel of 64/128/384 kbps packet ................................. 19Table 2.10: ETSI model based services........................................................... 20Table 3-1: The implementation of the RACH in the dynamic WP3 simulator

and the WP2 snapshot simulator.............................................................. 27Table 3-2: Channel functionalities, corresponding physical channels in UMTS

and equivalent GSM channels (regarding functionality). ........................ 28Table 6-1: Comparison between STD simulation approach and the real time

dynamic simulation approach in WP3..................................................... 45


7/50

List of Notation

BCCH Broadcast Control ChannelCAC Call Admission ControlCPICH Common Pilot ChannelDCH Dedicated ChannelDL DownlinkDSCH Downlink Shared ChannelFACH Forward Access ChannelGPRS General Packet Radio ServiceGSM Global System for Mobile CommunicationLBS Location Based ServiceMoDySim MOMENTUM Dynamic SimulatorMMS Multimedia Messaging ServiceMS Mobile StationNRT Non-real-timeP-CCPCH Primary Common Control Physical ChannelPC Power ControlPCH Paging ChannelPDF Probability density functionPICH Page Indication ChannelPRACH Primary Random Access ChannelQoS Quality-of-ServiceRACH Random Access ChannelRT Real-timeRX ReceiveS-CCPCH Secondary Common Control Physical ChannelSCH Synchronisation ChannelSF Spreading FactorSHO Soft HandoverSIR Signal-to-Interference RatioSMS Short Messaging ServiceSTD Short-Term DynamicTx TransmitUL UplinkUMTS Universal Mobile Telecommunication SystemWP Work Package


8/50

List of Symbols

1/k Weibull scale parameter Coefficient value that models the autoregressive process Weibull shape parameterDd Inter arrival time between datagrams (within a packet call)Dpc Reading time between packet callsgi(n) Gaussian random variablet TimeI Frame statek Burst state Lognormal distribution Meanmi Estimated average value of transmission speedmk Measured mean burst durationNd Number of datagrams within a packet callNpc Number of packet calls per sessionPk Measured ProbabilityQk Probability of a new state selection Lognormal distribution standard deviationSd Size of a datagramSk Packet size Burst duration


9/50

1 IntroductionWork package 2 deals with developing and evaluating different simulationapproaches for UMTS network and cell performance analysis. This is addressed indifferent stages in work package 2. In a first step, an overview of all relevantparameters in terms of traffic, mobility, user and services as well as system relatedparameters are summarised in deliverable D2.1. This report gives the basis fordesigning and adapting the simulation methods.

There are two basic approaches used as starting points for simulations in workpackage 2. On the one hand, state-of-the-art static simulations are considered. Onthe other hand, coming from dynamic analysis methods, a short-term dynamicsimulation method has been designed. Both methods will be improved during thedifferent phases of Momentum and work package 2 leading to a final design of onerecommended simulation and analysis method for fast UMTS networkperformance evaluation.

Figure 1-1: The deliverables of work package 2 and their interactions.

The development phases in work package 2 can be summarised as follows:

D2.2: Comparison of different simulation methods for cell performanceevaluation

D2.5: Evaluation of the influence of user mobility on the interference situation D2.4: Simulation scenarios including quality of service aspects D2.6: Final design of simulation methods for network performance evaluationBeginning with this deliverable report (D2.2), the two simulation approachesmentioned above are conceptually designed, implemented in a prototype, validated

D2.3Cell

Dimensioning

D2.4QoS

D2.2Compare concepts

D2.1Clarification of

environment

D2.6 Final design

Sim

ulat

ion

D2.5Mobility

D2.7Final Report

Dim

ensioning

WP3

WP5WP4

WP1

WP6


10/50

and evaluated. The evaluation starts with small homogeneous scenarios and will beextended to the reference scenarios defined in work package 1 and work package 5.

The deliverable report focuses on the conceptual design of static simulations andshort-term dynamic simulations for UMTS. In this phase, circuit-switched andpacket-switched services will be included. However, no detailed quality of servicerequirements are taken into consideration. The same holds for the users mobility,which will be included into the conceptual design but will not be implemented andevaluated in detail. These issues will be dealt with within D2.4 and D2.5.

The following figure shows how the design process is carried out.

Figure 1-2: The simulation design process in work package 2.

The deliverable report is divided into seven chapters. Chapter 2 and Chapter 3 areconcerned with the fundamental modelling of services, traffic channels, andsignalling traffic for the two simulation approaches. Chapter 4 is devoted to thegeneration of user traffic and mobility for both the static simulation approach andthe short-term dynamic simulation methods. In Chapter 5, the static simulationconcept is described in detail including power control, resource management, andthe snapshot analysis. Chapter 6 deals with the short-term dynamic approachbeginning with event and trigger generation. Additionally, it is described how theshort-term dynamic concept uses the core of the static analysis. Chapter 7summarises the results of Chapter 5 and 6 and gives a first recommendation of acombined simulation approach. In Chapter 7 open points and topics for furtherwork in work package 2 (in particular for D2.4 and D2.5) are listed.

Snapshot

Intermediate

Intermediate

Final Design

Dynamic

Time

Complexity

Modelling

Project duration

Static Monte CarloShort-term dynamic

D2.2

D2.4

D2.5

D2.6


11/50

2 Service and Traffic Channels Modelling forStatic and Short-term Dynamic Simulations

This chapter is concerned with assumptions on modelling of traffic for thesimulation purposes in work package 2. These assumptions are based on the workdone in [1]. The assumptions made in [1] are extended if necessary. Firstly, thereference services for UMTS defined by MOMENTUM and respective sourcemodels for traffic generation are presented. Secondly, the available traffic channelsfor the reference services are introduced. Finally, the corresponding level of detailin call admission for the services as it is considered in work package 2 anddifferences in these approaches to the detailed dynamic simulation approach ofwork package 3 are described.

2.1 Reference Services and Source Traffic Models

In MOMENTUM, the following nine reference services have been defined.

Speech telephony Video-telephony Streaming multimedia Web browsing (e.g. WWW) Location based service (LBS) Short messaging service (SMS) Multimedia messaging service (MMS) E-mail File download (e.g. FTP)In order to generate the UMTS multi service traffic, different source models mustbe used to characterise each service mains characteristics. In Figure 2-1 severalexamples of different services generated by traffic source models are shown.


12/50

T im e

b p s

V o ice

b p s

V id e o

T im e

b p s

W W W

T im e

b p s

L B S

T im eb p s

S M S

T im eb p s

M M S

b p s

e -m a il

T im e

T im e

b p s

F T P

T im e

Figure 2-1: Multi rate services overview in time and bit rate domain.

In the following sub sections, each model is described in detail, as well as theirmain parameters for simulation proposals.

2.1.1 Mobile Communications Voice Model

In [9], a new model for voice is described, based on measurements that include notonly the ON-OFF behaviour but also the effects of the voice encoder, compressiondevice and air interface (AI) characteristics in mobile communications; therefore,this model is expected to produce accurate results. Most encoders used in CDMAsystems produce voice packets periodically, the packet sizes ranging between 20-40 bytes.


13/50

The model described in [9] and [8] uses a four-state model: when the source is instate k it generates packets of size Sk each 10 ms, for a burst duration of k, k =1,,4. In the long time average, the probabilities that a packet is of size Sk aremeasured to be Pk. The burst duration k is modeled as a random variable, withmean value mk, with the Weibull probability density function (PDF) as follows[10]:

( ) ( ) ( ) kkk xkkk exxf = 1 (2.1)where 1/k is the scale parameter and k is the shape parameter, both taking thevalues defined in Table 2-1.

Statek

Packetsize Sk[Bytes]

MeasuredProbability

Pk

Measured meanburst duration mk

[packet]

Weibullparameter k

Weibullparameter k

1 2 0.5978 29.8 0.03 0.752 3 0.0723 2.5 0.45 0.803 10 0.0388 1.8 0.80 0.704 22 0.2911 38.8 0.05 0.90

Table 2-1: Voice Source Model Parameters (partial extracted from [9]).

After a k state, a new state is selected with probability Qk where [8]:

=

= 41j j

j

k

k

k

mP

mP

Q (2.2)

Figure 2-1 summarises the voice source model, and shows also that all voicesources have the same repetition rate, but, depending on AI settings, they mighthave different phase offsets.

Figure 2-1: Voice Source Model (extracted from [9]).

In [9], it is also referenced that with the proper selection of k and k parameters,this distribution function is a more accurate model of packet based encoded voice,than exponential burst length approximations.

The voice calls generation process should follow a Poisson process [11]:


14/50

( )!

)(n

ettPtn

n

= (2.3)where Pn(t) is the n message probability, is the mean arrival rate (calls persecond) and t is the time interval. For the total call duration distribution, theexponential Probability Density Function (PDF) is as follows [11]:

( ) 0,1 = tetf t (2.4)where is the average voice call duration, and = 1/, is the service rate.

2.1.2 Video traffic Model

In [4], a source model for Variable Bit Rate (VBR) video traffic is presented, basedon a finite-state Markov chain. It is demonstrated that it accurately models a one-and two layers video. This model assumes two types of video frames generation,the I and P frames. I is driven by scene changes, depending on the video source,and as such can be considered independent of the encoder dynamics. However, inthe time period characterisation by a sequence of P frames, one may expect thatthere are no significant changes in the information in successive frames. Therefore,it can be assumed that the bit rate characterisation of successive P frames arecorrelated, or more generally clustered around an average value. This fact allows atechnique that uses correlation between successive frame bit rates to identify the Iand P frames in the data. The frame rate ranges from 24 to 30 frames per second.

The I frame statistic is modelled by a Gaussian distribution function, the averageand variance of which is adjusted as a function of measured data. P frames may bequite different. In order to model these frames, a mechanism of K states wascreated, each state with its own average and variance.A Markov chain with K states is used to model the transitions between the states.The corresponding K state groups related to frame P, together with the only statethat characterises frame I, typify the K+1 Markov states that are represented by aprobabilistic transitions matrix PK. In this matrix, 90 % of the total of probabilistictransitions from one state to another are concentrated in {Pii-1, Pii, Pii+1} i =1,,K. Given i = n as an arbitrary state in the Markov chain, j = n-1 and n thestate in the Markov chain, the transmission speed of a frame is given by [8] and[4]:

( ) ( ) ( ) ( )( ) ( )

+=

++=++ ngmnR

ngnRmnR

kkk

ijiiii

11

11

( )( )framesI

framesPkji

= ,...,1,(2.1)

where mi is the estimated average value of transmission speed in state i, i is acoefficient value that models the autoregressive process, gi(n) is a Gaussian randomvariable, different for each state (average 0 and variance given by measurementdata in i state).


15/50

2.1.3 General WWW or Interactive Traffic Model

In order to model the World Wide Web (WWW) browsing sessions, one may startby modelling a typical WWW browsing session, which consists of a sequence ofpacket calls [6] (see Figure 2-1).

Figure 2-1: Typical WWW session (adapted from [6]).

Typically packet oriented services are classified as Long Constrained Delay (LCD)services because most of WWW based services are tolerant to delay. The session isunidirectional, i.e., in downlink. In a WWW session, a packet call corresponds tothe downloading of a WWW document (e.g., web page with images, text, applets,etc.). After the document is downloaded, a reading time is also modelled. During apacket call, several packets may be generated; a packet service session containsone or several packet calls, depending on the application.After the document is entirely retrieved to the terminal, the user is consuming acertain amount of time for studying the information, this time interval being calledthe reading time. It is also possible that the session contains only one packet call. Infact this is the case for a file transfer protocol (FTP). Hence, the followingparameters must be modelled in order to catch the typical behaviour described inFigure 2-1. Note that the number of events during the session models the sessionlength implicitly.

The geometrical distribution is used (discrete representation of the exponentialdistribution), since simulations use discrete time scale.

Session arrival process: The arrival of session set-ups to the network ismodelled as a Poisson process. For each service, there is a separate process. Itis important to note that this process for each service only generates the timeinstants when service calls begin, and it has nothing to do with calltermination.

The number of packet call requests per session, Npc: This is a geometricallydistributed random variable with a mean Npc.

The reading time between two consecutive packet call requests in asession, Dpc: This is modelled in discrete model time steps by a geometrically


16/50

distributed random variable with a mean Dp. Note that the reading time startswhen the last packet of the packet call is completely received by the user. Thereading time ends when the user makes a request for the next packet call.

The number of packets in a packet call, Nd: The traffic model should be ableto catch the various characteristic features possible in the future UMTS traffic.For this reason, different statistical distributions can be used to generate thenumber of packets. For example, Nd can be a geometrically distributed randomvariable with a mean Nd. It must be possible to select the statisticaldistributions that describe best the traffic case under study. An extreme casewould be that the packet call contains a single large packet.

The time interval between two consecutive packets inside a packet call,Dd: This is modelled in discrete model time steps by a geometricallydistributed random variable with a mean Dd. Naturally, if there is only onepacket in a packet call, this is not needed.

Packet size, Sd: The packet size distribution model is based on a Paretodistribution that is best suited for the traffic case under study. A Paretodistribution with cut-off is used. Here the cut-off corresponds to themaximum packet size.

2.1.4 Specific WWW Traffic Model

A user who runs (non real-time) NRT applications (e.g., HTTP, Napster, e-mail,etc.), follows a characteristic usage pattern [7]. A single user can run differentapplications that may be concurrently active, e.g., WWW browsing whiledownloading Napster music files.Each application is completely described by its statistical properties. Thesestatistical properties comprise an alternating process of ON- and OFF-periods withsome application specific length or data volume distribution, respectively, Figure2-1. Moreover, within each ON-period the packet arrival process is completelycaptured by the packet inter-arrival times and the corresponding packet sizes.Please note that UDP is not considered in this report as a part of the referenceservices.

Figure 2-1: Classification of non-real-time IP traffic streams (extracted from [7]).


17/50

The single user traffic model characterises the traffic that an individual usergenerates [7]. In UMTS, one has to distinguish between RT users and NRT users.Considering just NRT users, the single user traffic model is employed on threedifferent levels:

Session-level: it describes the dial-in behaviour of the individual users,characterised by the session inter-arrival time distribution and the session data-volume distribution.

Connection-level: it describes for each individual application thecorresponding distribution of connection inter-arrival times and connectiondata volume, respectively.

Packet-level: it characterises the packet inter-arrival time distribution and thepacket size distribution within the application specific connections.

Tables 2-2 to 2-6 show results presented in [7], where main WWW services arecharacterised individually at different bit rates.

Distribution CommentsLognormal (; 2) 0.6061; 7.5330 Similar to ISP

Table 2-1: Distribution of Session arrival inter-arrival times.

Distribution 64 kbps 144 kbps 384 kbpsUMTS users 50% 30% 20%Lognormal(; 2) 5.1613;13.8210 5.2943;14.1839 5.4197;14.4979

Table 2-2: Distribution of Session Volume assumed in [7].

Distribution 64 kbps 144 kbps 384 kbpsHTTP Lognormal(;2) -0.7597;3.5448 -0.2588;3.8720 -3.9599;4.3342e-mail Lognormal(;2) -0.3678;2.4220 -3.7152;2.6410 -4.1058;2.8902Napster Lognormal(;2) -0.1757;1.6076 -3.2357;1.3796 -3.2649;1.1853FTP Lognormal(;2) -0.0691;3.8055 -3.2656;3.9347 -3.4160;4.0337UDP Lognormal(;2) -0.9629;4.6606 -3.3120;4.7290 -3.6787;4.8532

Table 2-1: Parameters of packet inter-arrival times.

Distribution 64 kbps 144 kbps 384 kbpsInter-arrival

time Lognormal(;2) -0.3278;4.4807 -0.6907;4.8483 -1.1997;5.3259HTTP

Volume Lognormal(;2) 2.8651;12.6098 2.8072;13.0871 2.7888;13.4888Inter-arrival

time Pareto(k; ) 14.4360;2.1345 15.1334;2.1254 16.0229;2.1223e-mailVolume Lognormal(;2) 3.2677;13.2369 3.2799;13.5579 3.3084;13.8518

Inter-arrivaltime Not availableNapster

Volume Lognormal(;2) 5.8875;14.3687 5.9213;14.4278 6.0113;14.5865


18/50

Inter-arrivaltime Not available

1FTP

Volume Lognormal(;2) 3.3433;13.9698 3.3020;14.7826 3.3435;15.3229Table 2-2: Statistical properties at connection level.

Fractions of packets in overall traffic [%]Packet size 40byte

Packet size576 byte

Packet size1500 byte

Other packetsizes

HTTP 46.77 27.96 8,10 17.17Napster 34.98 45.54 4.18 15.30e-mail 38.25 25.98 9.51 26.26FTP 40.43 18,08 9.33 32.16UDP Lognormal(;2) (1.8821; 5.4139)

Table 2-3: Fractions of different packet sizes in overall traffic.

2.1.5 Service Set and Source Models Main Parameters

MOMENTUM simulators (from WP2, WP3 and WP4 work packages) are systemlevel simulators. These simulators will work at the radio interface level, not makingany signal processing (channel coding, rate matching, interleaving, framemultiplexing, etc) [2]. This processing has a huge impact on radio bit rate. In thisway, for each service, the important rate to be specified will be the service bit rateat the radio interface after all this signal processing, and not simply aftercompression. Since this bit rate is directly related with the SF, this parameter isused to characterise each service.

In order to generate traffic using source models, one has to define main parameters,which will be required to generate multiple services. In Table 2-1 some of them arepresented, like Busy Hour Call Attempt (BHCA) for each user type and the average(call or connection) duration time for each service. These parameters may be foundin deliverable D1.3. Table 2-1 presents additional parameters that characteriseservices with QoS constrains, type of channel mainly used, SF values in UL andDL and Eb/No targets.

SF QoSconstraintsEb/No[dB]

ServiceSource

model

Type ofChannel

[CCH/ DCH] UL DL Max. Delay[s] BER UL DL

Speechtelephony

VoiceModel DCH 64 128 0.150 10

-4 6.1 7.7

Video-telephony VideoTraffic DCH 8 16 0.150 10-3 3.5 2.5

Streamingmultimedia

ETSIWWW DCH 16 32 10 10

-6 2.4 1.9

1 Note that in [7] no information on suitable values for FTP are presented. The respective values are for furtherstudy.


19/50

Web browsing ETSIWWW DCH 32 64


20/50

For the streaming multimedia source model the data ETSI model is assumed,which consists of a sequence of session and packet calls. Table 2.4 gives defaultmean values for the distributions of typical multimedia streaming services.

Packet basedinformation

Averagenumber ofpacketcallswithin asession

Averagereadingtimebetweenpacketcalls [s]

Averageamount ofpacketswithin apacket call

Averageinterarrivaltimebetweenpackets [s]

Parametersfor packetsizedistribution

StreamingUDD 64kbit/s

1 0 3280 0.0625 k = 81.5 = 1.1WebBrowsingUDD 32kbit/s

15 20 156 0.125 k = 81.5 = 1.1LocationBased UDD8 kbit/s

6 30 8 0.5 k = 81.5 = 1.1Table 2.4: ETSI model based services.

The average volume information assumed in D1.3 for web browsing service is1125 kByte, therefore, the source model parameters were adapted in order toachieve this value (assuming average packet size of 480 Byte).

For location-based services, the ETSI model is again assumed, because theseservices are expected to be used like WWW, but at a lower level (informationvolume). Table 2.4 gives default mean values for this service.

One SMS hardly increases the network load, but as SMS are sent massively it doesgenerate quite a lot of traffic. SMS is a quite simple service. Therefore a simpleON-OFF model, where a Poisson distribution models the service generation, can beused.

The MMS source model is similar to the SMS model, except for the informationvolume (60 kBytes) and used type of channel. For the e-mail service, the selectedmodel is the WWW model, which includes e-mail service. Values for this servicemay be found in the model description. For the file download, the selected model isagain the WWW model, which includes FTP service. Values for this service maybe found in the model description.

2.1.6 Conclusions

Basically these models are practical to simulate the UMTS user behaviour in termsof service usage and impact on the network. Nevertheless, in order to build arealistic simulator, using the real bit rate that will be used by the UMTS radiointerface it is necessary to map the traffic source models bit rate onto the UMTSframe structure. Differences between source generators and radio interface are


21/50

quite huge (due to channel coding, signalling and frame matching), therefore, it isrecommended to consider specific values in order to perform realistic simulationsat radio level.Another important aspect to be considered in this context is the influence of upperlayer protocols like TCP and UDP on the traffic source models on UMTS networklevel. The respective impact on the traffic source models cannot be neglected and isone topic for further study.

2.2 Traffic Channels for Reference Services

In a snapshot generator power is assigned to each mobile station through aniterative algorithm using SIRtarget and spreading factor (SF) as an input2. Therequired power depends on the service, but also on the channel that is used by thatparticular service. This power is determined in a power-loop, see chapter 4.Furthermore, recall that this power is assigned on chip level. In this chapter wedetermine the required power depending on the traffic channel used.

Note:All variables have linear units unless stated otherwise. Per traffic channel theallowed spreading factors are described in deliverable D2.1 [1].

2.2.1 Dedicated Channel (DCH)

2.2.1.1 Active in UL

UplinkIn UL, both user and control data is sent, they both have their own SF (SFservice andSFcontrol = 256 respectively) and their own SIR target (SIRservice,UL and SIRcontrol,ULresp.). The power for the user data is based on the power needed for the controldata multiplied with a factor, fservice, which depends on SFservice,UL and SIRservice,UL:

controlserviceservicecontrolULtotal

controlserviceservice

ULcontrolcontrol

PfPPPPfPGain

ISIRP

)1(

256

,

,

+=+==

=

(2.6)

with

ULserviceULcontrol

ULservice

ULserviceULcontrol

ULcontrolULserviceservice SFSIR

SIRSFSIRSFSIR

f,,

,

,,

,, 256

== . (2.7)

Here Gain consists of whatever losses we want to take into account, e.g. pathloss,cable loss and antenna gain, and I is the interference experienced at the BS.

2 Instead of SF, 3840/user_bitrate can be used. The SIRtarget should then be adapted accordingly. The choicedepends also on the input data (see section 1.1).


22/50

There are 2 ways to find the powers for control and user data. Either in the iterativePC algorithm, the two powers are looked at as 2 users, one of which needs Pcontrolas power and one which needs Pservice. The other option is to only use Pcontrol in theiterative algorithm, but to include Pservice in the interference calculations. Wepropose to use the latter.

Downlink

In the downlink only control data has to be sent: SF = 256, SIRtarget = SIRcontrol.

GainISIR

P DLcontrolDLtotal =

256,

, (2.8)

2.2.1.2 Active in DL

Uplink

In the uplink only control data has to be sent: SF = 256, SIRtarget = SIRcontrol.

GainISIR

P ULcontrolULtotal =

256,

, (2.9)

Downlink

In the DL both user and control information is sent, they use the same physicalchannel and therefore have one SF, SFcontrol&service and one SIR target,SIRcontrol&service.

GainSFISIR

PULservicecontrol

ULservicecontrolDLtotal

=,&

,&, (2.10)

2.2.2 Forward Access Channel (FACH)

The number of FACHs in a cell and their powers are fixed upfront by the planner.Therefore the iterative power control does not have to assign power to a MS usinga FACH, as it is fixed. The powers of the active FACHs do have to be subtractedfrom the total available DL power.

FACHDLtotal

ULtotal

PPP

==

,

, 0 (2.11)

2.2.3 Random Access Channel (RACH)

Recall that the RACH is a.o. used to get access to the system. Hence, whengenerating users for a snapshot, parts of them are in session set-up mode. Thispercentage might be obtained from current GSM and GPRS systems by either


23/50

measuring the number of access attempts versus the actual traffic during e.g. thepeak hour, or by assessing the set-up time and the average call/session durationtime, see chapter 2.The SF of a RACH is fixed upfront by a planner. The power is not fixed anddepends on the SIR target of the service and the SF of the RACH it uses.

0,

,,

=

=

DLtotal

RACH

ULserviceULtotal

PGainSF

ISIRP

(2.12)

2.2.4 Downlink Shared Channel (DSCH)

When a MS uses the DSCH, then in both UL and DL also a DCH is needed.

Uplink

The DCH in UL has a fixed SF (256) and SIR target SIRcontrol.

GainISIR

P ULcontrolULtotal =

256,

, (2.13)

Downlink

The DCH in DL has a fixed SF (256) and SIR target SIRcontrol.

The DSCH SF is variable (but has a maximum, SFDSCH,max which is fixed by theplanner). The power needed by the DSCH part is a factor, fservice, times the power ofthe DL DCH channel. This factor is different per service and depends on the SIRtarget:

controlserviceservicecontrolDLtotal

controlserviceservice

DLcontrolcontrol

PfPPPPfPGain

ISIRP

)1(

256

,

,

+=+==

=

(2.14)

DLserviceDLcontrol

DLserviceservice SFSIR

SIRf

,,

, 256

= . (2.15)

2.3 Call Admission in Static and Short-term DynamicSimulations

In this section the previous two sections come together. In section 2.1 for eachservice is described what traffic model can be used. In section 2.2 the traffic


24/50

channels are modelled. In this section we describe how per service the choice for aspecific traffic channel type and SF (in case there are several options) is made.

2.3.1 Call admission in static simulations

The call admission in static simulations is done as follows. In each snapshot, theusers are generated randomly in accordance with the traffic load specified in eachpixel of the network. The users are sorted in a random order. No prioritisation ofusers with respect to their service is considered at the moment. In the next stages ofthe project, prioritisation will be possible. The admission of users is done on apower basis. During the power control loop those users whose power demands areacceptable get connections to the system. Hence, call admission is done inconjunction with power control. No iterative call admission is performed.Prioritisation and more detailed admission strategies are topics for further studywithin MOMENTUM.

2.3.2 Call admission in short-term dynamic simulations

The call admission machinery of the short-term dynamic simulations is based onthe method for static simulations. The users who have been connected to thesystem within the last step and who did not release their connection since then areprioritised. This means that the system tries to avoid dropping of these connections.New calls, which have been generated, in the meantime may get connected ifcapacity is available. These calls are ordered by their arrival times. Hence, the calladmission process resembles the process of the static simulations. The onlyexception is that fresh calls have less priority.

2.3.3 Differences to resource management in dynamic simulations

The main difference between the call admission process in work package 2 to theway the dynamic simulator of work package 3 deals with call admission is asfollows. The dynamic simulator treats each call separately. The load andinterference is calculated as a basis for call admission. Power control and calladmission are not that closely related as in work package 2.

The approach of work package 3 can be summarized as follows.

When power is assigned to calls 1 by 1, then per call the CAC algorithm can beapplied. Assume that per service the possible traffic channel / SF combinations areordered in terms of preference. Two options depending on how CAC is modelledcan be distinguished:

1. the CAC algorithm does not take traffic channels into account whendeciding whether to admit or reject a call: determine if call will beadmitted, if so admit it on its first preference if possible, if not try itssecond, etc.


25/50

2. CAC takes traffic channels into account: apply the CAC algorithm to thetraffic channel / SF combination of its first preference, if it is rejected trythe second, etc.3

3 This is how it is done in WP3


26/50

3 Signalling TrafficAs mentioned in Deliverable 2.1, Table 6-1, the channels that carry signallinginformation not associated with user data are

the Common Pilot Channel (CPICH), the Primary Common Control Physical Channel (P-CCPCH), the Synchronisation Channel (SCH), the Page Indication Channel (PICH), and the Random Access Channel (RACH)The DL channels have in common that they are not power controlled and they allhave a SF of 256.

The UL RACH channel does not have a fixed SF and it uses open loop powercontrol.

This chapter describes how the effect of these channels is taken into account in theWP2 and WP3 simulator.

3.1 Uplink

3.1.1 Random Access Channel

Within the context of signalling not related to user data, the RACH is used duringthe call set-up procedure. The process is such that the user sends with a number ofpackets over the RACH channel, until his request is detected by the base stationand granted.

Workpackage Effect, model Decision

2

Concentrating on a particular user, he maybe in call set-up or connected mode(compare figure 6-1 in [1]); when the user isin call set-up, an average power needs to beassigned, independent of an Eb/No target.The inputs that are required are average callset-up time (Tset-up) and Pset-up. Based onTsession the probability for having a user incall set-up mode, probset-up, equals Tset-up/(Tset-up + Tsession). It is still open whether'session' stands for the time that the user is'connected' or for the time that the user is'connected' and 'active'. In the latter case, weneed Tre-activate, Ttime-out and Treading (following

Tsession, Treading from WP1 service dependent;Tset-up from GPRSmeasurements: 3-5 sec;Tre-activate from GPRSmeasurements: 0.5 sec;Ttime-out operator dependent


27/50

an exponential stochast). probset-up equals inthis case ( ) ( )

up-setsession

sessions activeout-timereadingactivate-reup-setup-setprob TT

NTTTT+

>+= 1Pr

3

A dynamic simulator could in principle takethe call set-up procedure into account bymodelling the process of transmittingpackets with increasing power. Thereforethe RACH channel itself needs to beimplemented and the access procedure.This procedure needs inputs like poweroffset, power increase, timers and EbNotarget. The overall effect of the call set-upprocedure is interference.

Due to the increase ofcomplexity of the simulator,it is decided to leave out thecall set-up procedure. Note:the RACH itself is definedin MoDySIM; this can beused to generate set-upsessions causinginterference.

Table 3-1: The implementation of the RACH in the dynamic WP3 simulator and the WP2 snapshotsimulator.

3.2 Downlink

Power settings for downlink signalling channels can be obtained in a number ofways. The first way is to describe a decision or feedback procedure by which theplanner optimises the relevant power settings. As an example, consider thefollowing feedback procedure (Figure 3-1).

Power of CommonPilot Channel

Area covered byCommon Pilot Channel

Available Powerfor Traffic Channels

Traffic/Area servedby Traffic Channels

Gap (= Traffic Area - Pilot Area)

+

+

+

-

-

-

Target Gap

Differencefrom target

-+

min RXlevel

Target Ec/Io

- -

Figure 3-1: Blue indicates variables that can be monitored during the optimisation process. Red isthe target variable that needs to be set. Black indicates constants or derived/intermediate variables.

The procedure describes how an increase of pilot power increases the pilotcoverage area. On the other hand, an increase of pilot power decreases the poweravailable for traffic channels, and hence the amount of covered traffic. The gapbetween these two areas determines whether or not the pilot power should beincreased or decreased.In general, operators and vendors design these procedures when they look fordefault settings or an optimal network design.


28/50

The second option to gain insight in actual settings for the different power levels isto identify channels in GSM and/or GPRS with a similar functionality. The powersettings of the GSM equivalents might be used as a starting value in the simulator.Caution should be paid to the fact that GSM and GPRS handle e.g. the broadcastchannel in a intrinsically different way than UMTS. In GSM the BCCH is plannedwith a higher C/I and hence with more care than the traffic channels. The UMTSequivalent of this is a certain target Ec/Io or a certain minimum received pilot level.

UMTS GSM/GPRSChannelfunctionality PhysicalChannel Allocated power (guidelines)

(Logical)Channel

Broadcast systemlocation; channelestimation

CPICH 10% of DL power for targetEc/Io = -18 dB and min RXlevel = -110 dBm

BCCH

Broadcastspecific systemparameters

P-CCPCH BCCH

Synchronisation SCH SCHPaging PICH

5% of DL power

PCHLocationupdates, cell-reselection

PRACH(UL),S-CCPCH(DL)

SDCCH

Table 3-1: Channel functionalities, corresponding physical channels in UMTS and equivalent GSMchannels (regarding functionality).

3.2.1 Common Pilot Channel


2Constant power level Constant power level; should be a result

from an optimisation procedure (WP4).Initial value 10% of DL power

3 Constant power level Constant power level; level should resultfrom an optimisation procedure (WP4).

3.2.2 Primary Common Control Physical Channel, Synchronisation Channel, PageIndication Channel


2Constant power level Constant power level; level should result

from an optimisation procedure (WP4).Initial value 5% of DL power

3 Constant power level Constant power level; level should resultfrom an optimisation procedure (WP4).


29/50

4 Snapshot User GenerationIn generation of the independent snapshots to be used in the snapshot simulator, aset of active users from each type is to be generated and randomly deployed to thepixels keeping to spatial distribution of the users in the simulation scenario. At theend of this generation, each user has a defined pixel position and capacity demand.The method of generation is described below.

4.1 User Density Map

The position of a user is determined based on user density information given inform of a traffic map. For each user type a separate traffic map exists. The trafficmaps are pixel oriented and contain the user density in each pixel. In apreprocessing stage, polygon and vector data are converted into a pixel-based map.The converter checks which pixels are covered by polygons or which pixels vectorsintersect. Each of these pixels gets allocated with the same fraction of the usercount in this polygon or vector. For example, a polygon with 20 active userscovering 45 pixels, generates a value of 20/45 for each pixel, see Figure 4-1. Pixelsthat are partially contained in the polygon/vector are treated as if they werecompletely contained in it and care is taken to guarantee, that all the valuessummed up over the set of pixels is equal to the total number of users in thepolygon/vector area.However, this converter is needed only in the development stage and later trafficmaps corresponding to test scenarios will be made available by the other workpackages.

15 User10 User

10 User

10 User

10 User

20 User

20/45 User

Figure 4-1: Conversion of polygon/vector data into pixel based user density map (traffic map).


30/50

To read the pixel based user density map from a file, we use the raster file formatdescribed in [14]. If the user density information is stored in ASCII format, a hugeamount of core memory is needed to retain this information (e.g. a 2000 by 2000pixel raster file needs about 45 MB). In addition the reading time required is alsoconsiderable. To reduce the memory usage, we have also introduced the option ofstoring the density values in binary format, so that it takes only four byte per pixel,reducing the memory requirement to 16MB for the example above.

In order to save memory, we use a further modification, where a density map witha lookup table is introduced. Here, we have to store only one byte per pixel and 256four byte values for the lookup table, leading to only 4.001 MB for the exampleabove. The disadvantage of this method is that the number of different user densityvalues is restricted to 256. However, if this number exceeds 256 for a givenscenario it is always possible to allocate more than one byte per pixel. As anexample if two bytes are allocated per pixel we can accommodate 65536 differentuser density values and the memory requirement increases to 8.28 MB.

4.2 Setting User Positions

4.2.1 Single User

To determine a user position, we have to draw a random number between zero andthe sum of the user density values. Then we have to add all the single densityvalues going through the raster, from top-left to down-right through all the rowsand compare them to the random number drawn. When the sum is larger than orequal to the random value the user has to be placed in this pixel, as illustrated inFigure 4-1.

0.0

0.0

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.0

0.4 0.5 0.6 0.7

0.1 0.2 0.3 0.4

U 0 654Figure 4-1: Positioning of the user based on random number drawn.

4.2.2 Group of Users

One advantage of the above algorithm is that a group of users can be positionedthrough a single scan of the grid. For this we draw a random number for each userto be deployed and then these numbers are sorted in ascending order. After this theusers are deployed one by one, starting from the user with smallest randomnumber, in a single scan of the grid, as described above. The scan continues fromthis position and deploy the next users.


31/50

This user distribution method is fast. To place 2000 users in a 2000 by 2000 raster,it took less then 2 seconds, including the reading time (1.2 seconds) of the trafficmap with the lookup table on a 1 GHz processor.

4.2.3 Distribution sample

Figure 4-1 is a sample of a user distribution. The black dots represent the users.There are 500 users in a 2000x2000 pixel map. The colour progression shows anoise plot and is for this example not relevant.

Figure 4-1: A sample snapshot.


32/50

5 Static Simulation Concept

5.1 Introduction

Figure 5-1: In a snapshot simulation several independent snapshots are considered.

The general idea of static snapshot simulations is to carry out an analysis of severalindependent network configurations, i.e. traffic distributions, based on onecommon network and traffic setting. The generation and analysis of one of these iscalled run. Each run consists of several stages. At first the snapshot is generatedwith respect to a specified traffic configuration (cmp. Chapter 4). In the next stepthe snapshot is analysed, meaning that the state of the network is estimated giventhe traffic situation from the snapshot. Prior to this the snapshot is evaluated. Thismay for example include a graphical area analysis, e.g. of the noise level. Thecombination of the results of several independent runs gives statistically significantresults.

PC Iteration

ConvergenceCriterion

Snap-ShotGeneration


Snap-Shot AnalysisPC Loop

Snap-ShotLoop

PC Iteration


Snap-ShotGeneration


Snap-Shot AnalysisPC Loop

Snap-ShotLoop

PC Loop

Snap-ShotLoop

Snap-ShotLoop

Snap-ShotLoop

Figure 5-2: Static Simulation Concept Overview.

Figure 5-2 gives an overview of the static simulation. It consists of two main loops:The snapshot or outer loop and the power control (PC) or inner loop. Objective ofthe outer loop is to generate and analyse as many snapshots that the resultsconverge and significant results can be obtained. The analysis of individualsnapshots is realised in an iterative way. Aim of the inner loop is to assureconvergence of this. Both loops are in the following described in more detail.


33/50

5.2 The Inner Loop

Goal of the inner loop is to analyse the snapshot. This in particular means todetermine the fraction of the offered traffic that can be carried and to determine thepower levels of the active connections.

As the system mainly is interference limited, that analysis is based on thecalculation of the individual power levels. In the following it is assumed that alldata is transmitted on dedicated channels with perfect power control. The influenceof soft handover is not considered here, but will be discussed in detail in 5.2.5. Foreach mobile/connection a power equation can be obtained:

( ) )(arg ijInteriIntraii

etTi PfIIG

CIRP =++= (5.1)

WhereetTCIR arg : Signal to interference before despreading for this particular connection

: Orthogonality factorIntra

iI : Intracell interferenceInter

iI : Intercell interference : Thermal noise

iG : Link gain between MS i and corresponding BS

The latter equation applies for the downlink only. A similar equation for the uplinkcan be derived.

It can be noted that each individual power is a linear combination of all otherpower levels in the system. Calculating the power levels of the M connections inthe system thus basically means to solve a set of M linear equations:

kAp = (5.2)Solving a system of linear equations obviously is not a task of great complexity.However, there are two facts that significantly influence the difficulty of this taskin this case. Firstly, the dimension of the system of linear equations mightdepending on the scenario be very high. The matrix A tends to be sparse. Moreimportantly, the set of linear equations changes during calculation due toconstraints that have to be taken into account. Constraints are in particular themaximum load level in the cells for uplink and downlink and the maximum outputpower of mobiles. An intuitive way of solving the system is thus to solve ititeratively.

Iterative methods calculate the power levels in step n+1 based on the power levelsin step n:

bnCpnp +=+ )()1( (5.3)The speed of convergence depends on the complexity of the system as well as onthe spectral radius of the matrix C.


34/50

From literature several iterative methods for solving a system of linear equationsare known. However, in this case only few methods seem to be appropriate. Theseare the classical methods: namely Gauss-Seidel and Point-Jacobi. More efficientmethods like over-relaxation methods are considered not to be appropriate as theymight cause instability due to overshooting.

The matrix C and the vector b are in both methods calculated based on the strictlylower and strictly upper triangular matrices of A (F, E) as well as the diagonalmatrix D.

Point-Jacobi:

kDbFEDC -1-1 )( =+= (5.4)Gauss-Seidel:

kEDbFEDC -1-1 )()( == (5.5)A comparison of the convergence speed of both methods is exemplarity shown inFigure 5-1. The results generally suggest the use of the Gauss-Seidel algorithm.

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Gauss-Seidel

Jacobi

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Gauss-Seidel

Jacobi

Figure 5-1: Comparison of simulation speed.

Independent of the iterative method chosen, different realisations of the inner loopcan be thought of. These approaches differ regarding accuracy, simulation speedand modelling complexity (e.g. prioritisation of traffic). Different approaches andaspects of concern are described in the following sections.


35/50

5.2.1 The classical Approach

Insert active connections randomly into list

Initialize power values for each link

Best Server analysis (including SHO)

Power Control update

Drop if not feasible (Max Power Constraint)

For each link i

Until convergence reached

Snapshot Analysis

Figure 5-1: The standard way of analysing a snapshot.

As described above, the objective of the inner loop is to model the behaviour of thesystem given a particular traffic distribution. This mainly includes modelling thepower control for each connection (solving the system of linear equations) andmodelling the influence of the radio resource management and equipment limits(the constraints). The classical approach for the inner loop is depicted inFigure 5-1.Firstly, all users are inserted randomly into a list to avoid any side effects due tocorrelation. The users are then assigned to a cell based on a minimum path losscriterion. All power levels are initialised. The initial value might in the easiest casebe the minimum down- or uplink power, but a more complex method is likely to beused, as this can speed up the iterative method followed by this.In each step of the iterative algorithm the power levels of all connections areupdated based on the interference experienced if all power levels were set as in thelast step (Point-Jacobi). Alternatively updated power levels of the same step canalready be taken into account (Gauss-Seidel). After calculating the new powerlevels (for UL and DL independently), it has to be decided, if this is a feasibleconnection. If the cell load in either UL or DL exceeds the maximum allowed cellload or any other constraint is violated, the connection is regarded as non-feasibleand is dropped. Note that in case of cell reselection in each step, the connection isnot necessarily dropped or blocked for all times, but might be reselected in the nextstep.Contrary to a fixed cell assignment at the beginning of the simulation, as an optionin each step a cell reselection can take place. This might more precisely reflect thebehaviour of the real network, but first investigations proved that this likelyoverestimates the network.


36/50

The iteration loop is finished after convergence of power levels is reached.However, it might be necessary to define a maximum number of iterations asbackup criterion. For more details on the convergence see 5.2.4.

5.2.2 Introducing Priorities

A slight modification of the algorithm described above allows the introduction ofpriorities among connections of one cell. In the algorithm above exactly theconnection is dropped, that violates a constraint. As an alternative, in case of aviolation of the maximum load level, first connections of lower priority can bedropped. This might allow this connection to be maintained. For this purpose thecell keeps a priority list of all connections.

5.2.3 Power Control on Cell Basis

Given the system of linear equations described in 5.2, the power of each DLconnection in principle is a linear combination of all cell Tx powers in the system:

)( celli pfp = (5.6)With

ip : The vector of individual connection powers

cellp : The vector of cell powers

Adding the equations belonging to one cell can reduce the complexity. In this waya macroscopic system of order N can be obtained. The formula below shows theprinciple for one cell in DL direction:

)(0 cellc

ctotal

kc

k

kcc

ctotal

mi ki

cii

ktotal

mii

mi ki

iksignalling

mi

ki

ksignalling

ktotal

pfPaa

PGGCIRPCIR

GCIRP

PPP

kkk

k

=+=

+++=

+=

(5.7)

In this way the computational complexity can be significantly reduced: Not onlythe number of operations per iteration is reduced due to the decreased number ofequations, but also the convergence speed in terms of iterations is reduced asshown in Figure 5-1. The blue curve in Figure 5-1, labelled as Standard,corresponds to the Gauss-Seidel implementation on connection basis.

The formulae above correspond to the DL direction only, but a similar set ofequations can be obtained for the cell interference in UL direction.


37/50

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1S tandardP oint JacobiGaus s -S eidel

Figure 5-1: Increased speed of convergence in case of PC on cell basis.

The PC on cell basis seems to be very promising in terms of reduction ofcomplexity. Nevertheless, it has to be stated that it can only directly beimplemented in case that the network can carry all offered traffic and noblocking/dropping occur. This method is still subject to investigations.

5.2.4 Convergence of the inner loop

The iterative loop is finished based on a convergence criterion. This can simply bebased on the change of the cell load from one iteration to the other. However, thereare constellations for that convergence is not reached due to oscillations inparticular in case of cell reselection in each iteration step. This especially is causedby the fact that the connection power is underestimated when it is checked whetherthere is enough available power for a connection (Figure 5-1). This problem can beovercome by a back-up criterion, e.g. a maximum number of iterations.However, more intelligent is to estimate the required connection power with moreaccuracy. It goes without saying that in case the required power is exactly known,no oscillations occur and the system thus is stabilised (Figure 5-2).


38/50

Figure 5-1: Instability due to underestimation of connection power.

Figure 5-2: Stabilised inner loop.

5.2.5 Influence of Soft Handover

In the description above the processing of soft handover (SHO) has not beenincluded. However, SHO can very easily be included: During cellselection/reselection not only one cell but also multiple cells based on the bestserver analysis might be selected. The power update then has to take this intoaccount. For the calculation of the power levels two possibilities are possible.


39/50

Firstly, maximum ratio combining can be applied, meaning that the required poweris calculated such that the addition of the individual SIR levels of all SHO branchesmatches the SIR target. This is the way SHO is considered in WP3.

The second possibility is to calculate the power level as if only the strongest serveris chosen lowered by a SHO gain. The SHO gain obviously depends on a largenumber of parameters, like the environment (clutter type), the mobiles speed,number of branches, and the difference between link gain values of the branches.

For which option to choose no general recommendation can be given. However, interms of computational complexity and especially in case of power control on cellbasis the second option is favourable.

5.3 The Outer Loop

Objective of the outer loop is to control the number of snapshots to be analysed toreach convergence. Convergence could be based on the convergence of the cellloads in UL and DL.

In order to get indications of the number of snapshots to be analysed forconvergence, in the following a simple investigation shall be considered. For the 19site/57 cell scenario depicted in Figure 5-1 with uniformly distributed traffic, thespeed of convergence is evaluated for different traffic types. The traffic types differin the required bandwidth (Spreading Factor SF). The total offered traffic in termsof bandwidth is kept approximately constant, meaning that the number of offeredconnections is reduced with decreasing SF.

For each traffic type 500 snapshots are evaluated and the mean value for the DLcell loads m500 is calculated. As a measure for the convergence of the outer loop therelative deviation of the mean value after n snapshots from the mean over all 500snapshots is evaluated:

500

500

mmm n (5.8)

In Figure 5-2 the latter is shown for an arbitrarily chosen cell and 4 service types(SF=16, 32, 64, 128). As expected the number of snapshots needed to reachconvergence, in this case a 5 percent confidence interval, heavily depends on thetraffic type.

The same investigation is carried out for a different cell of the same scenario(Figure 5-3). As an interesting result, the number of snapshots needed to reachconvergence for services other than voice (SF


40/50

Figure 5-1: DL noise plot for a 19 site/57 cell scenario with uniformly distributed traffic.

These simple investigations obviously can only give an indication of theperformance of the outer loop. However, it already becomes very clear, that forhigh bit rate services, which are of great importance for UMTS networks, thenumber of snapshots to be analysed can become extremely high. Thereby theanalysis time per snapshot obviously is decreased. However, first investigationsindicated that this decrease does not compensate the increase in simulation timedue to the higher number of snapshots. More detailed investigations will be carriedout at a later stage. Regardless of these, cutting down the snapshot number is ofmayor importance and will be one of the main tasks in the next deliverables (i.e.D2.3).


41/50

10 20 30 40 50 60 70 80 90 100-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

SF 128SF 64

SF 32

SF 16

10 20 30 40 50 60 70 80 90 100-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

SF 128SF 64

SF 32

SF 16

Figure 5-2: Convergence speed depending on the traffic type (1/2).

20 40 60 80 100 120 140 160 180 200-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

SF 128 SF 64SF 32

SF 16

Figure 5-3: Convergence speed depending on the traffic type (2/2).


42/50

6 Short-term Dynamic SimulationsShort Term Dynamic (STD) simulations play an important role in the planningstage to make investigations on the impact of mobility and different service mixesand also to ensure that QoS requirements are met. It provides much moreinformation regarding the system behaviour compared to pure snapshot simulationsby taking important dynamic effects such as time dependent bandwidthrequirements of up- and down link, up- and downgrading of non-real time datatraffic and user-mobility into consideration. However, STD simulationsdifferentiate significantly from the real time dynamic simulations because theyspan a shorter time duration, allows a comparatively low time resolution and do notinclude some of the detailed dynamic effects as described in 6.2.

6.1 The concept

The STD simulation builds upon the snapshot simulation in such a way that thesimulation starts from an independent snapshot and proceeds for a predefined shortduration of time taking a selected set of dynamic effects into consideration. At theend of this duration the dynamic simulation is terminated and a new independentsnapshot is generated. A further dynamic simulation window restarts from the newsnapshot. This cycle is repeated for a sufficient number of times and during eachshort duration of simulation all the dynamic events of interest are passed forevaluation. As each of the starting snapshots is supposed to be a state out of thestate-space of the stationary system, the dynamic simulation window need notinclude an initial transient phase and thus can be limited to a maximum of aboutone average session length. However, in the test simulations described in section5.3, much smaller simulation windows have been used. The modules used forsystem recalculations, where the interference and power control calculations areperformed, are taken over from the snapshot simulator.

Further details of the simulation concept are described below in 6.1.1 to 6.1.3.

6.1.1 User generation

User generation in snapshot simulation requires only the physical position of eachactive user and his/her activity state (e. g. data rate and SIR targets), whereas noinformation needs to be generated about the past or future behaviour. For the STDsimulations this need to be extended to include the complete future behaviour ofthe active user for the whole period of the STD simulation window or theremaining session duration, whichever happens to be the shorter duration (see alsoeffects of downgrading described in 6.1.2). First the user type, which includes theservice type as well as the mobility type, is generated keeping to the spatialdistribution of users at the location. Then the whole user session is pre-generated insuch a way, that the complete sequence of activity change instants are produced inadvance. In case of packet switched services, only the start/end of packet calls,corresponding data rates and reading times [13]] are generated. Effects due topacket retransmissions are implicitly taken into account by the packet call duration


43/50

distribution. In order to obtain the duration of each packet call a k-stage Erlang(Erlang-k) distribution is used. Here, k corresponds to the number of packets in thecall and is itself a geometrically distributed random number with a mean of 25.The parameters of the Erlang distribution are selected in such a way, that the timerequired for transmission of a packet is neg. exponentially distributed with a meanof 0.5s, which corresponds to the data rate of 8kbit/s. For higher data rates themean value is corrected accordingly.

The point in time of user intended termination of the session is also identified inadvance. This pre-generation is implemented (in C++) by attaching a user typedependent source object to the user object. The information in the source object isused in generating the session containing all the traffic related events. These arestored as event objects in an array and taken up one by one later at the schedulingstage.

As the mobility type of each user is already known, the complete set of futuremovements of the user can also be generated and stored. How much informationneeds to be stored will be evaluated during subsequent phases of work package 2.For this, the pixel oriented mobility model proposed in [12] is used, where themobility parameters are attributed to the pixel and any user entering the pixel picksup the mobility parameters belonging to the pixel and makes the next movement asdictated by these parameters. This way another sequence of events, this timemobility events, can be generated and stored in a separate event object.

In addition to the users present in the snapshot, new users emerge during the STDsimulation window. Complete generation of such users including arrival time,starting position and event objects for session activity and mobility is needed. Inorder to avoid the need of time intensive scanning of the whole distribution map foreach newly arriving user during the STD simulation window, a sufficiently largearray of new users is separately generated in advance at the beginning of the STDsimulation using the same algorithm used for snapshot generation. The advantageof this approach is that the positions of all the new users can be determined in asingle scan of the distribution map. The events of new arrivals are dynamicallygenerated using a global exponential process and for each arrival a user israndomly picked from the pre-generated array.

6.1.2 Generation and scheduling of events

The STD simulation is an event driven simulation and the set of events consists ofa large number of model events and a relatively small number of managementevents. Model events are the random events inherent to the processes beingsimulated and comprise

Arrivals of new users Terminations of active users Events of activity change in each of the session Change of a user SIR levels (dB) Down- and up-grading events Mobility events (a user displacement exceeding a lower limit).


44/50

See Figure 6-1 for an example.

Down- and up-grading of data traffic makes the simulation more complicated as itrequires rescheduling of the corresponding events that are pending and havealready being scheduled. The packet call durations, which change due to down- orup-grading, are recalculated and the relevant events are regenerated from the storedevent objects. Rescheduling comprises the deletion of old unprocessed events andfresh scheduling of the newly generated events.

Figure 6-1: Session of two users with activity change and session end as example events.

The management events are the events, which control the simulation and comprise

Events of system recalculation, Termination of the STD window, and Termination of the simulation.

6.1.3 System recalculations

Proper implementation of the power control plays an important role concerning theaccuracy of the simulation. However, it is time consuming and also not necessaryto be performed at each and every event in the STD simulation run. Thus,recalculation is carried out only after a certain number of events (a group) haveoccurred or some period of time without recalculation has passed, see figure 6.2.The figure shows two methods of event grouping. In the first one, the events aresimply counted for the decision, when the next recalculation has to be performed.In the second one, the events are delayed themselves, which can make a differencein evaluation of network delays. For the grouping, the events are weightedaccording to their type, in such a way that, for example, a user arrival is treated tobe more important than a simple activity change in a CS session. This helps toheavily reduce simulation effort but leads to inaccuracies as the systemrecalculation, for example resource allocation for a new call, is not carried out atthe actual occurrence of the event but delayed by some time. As a compromise therelevant events are delayed in such a way, that they occur exactly at therecalculation time point, see Figure 6-1. Currently the first method is applied, inwhich the events are weighted.


45/50

Figure 6-1: Two methods of event grouping and reduction of recalculation.

6.2 Comparison between STD and dynamic simulation concepts

The differences between the STD simulation approach described above and the realtime dynamic simulation approach implemented in WP3 are briefly listed out in thetable below:

Characteristicsof the simulation

approach

STD simulation Dynamic simulation

Simulation run Starts from a snapshot andsimulates for a shortduration.

No transient phase Repeated simulation of

STD-duration

Starts practically fromidle state.

Transient phase Single, but long

simulation period

Power control Only at selected points of time Command by commandsimulation

Session details Signalling details notincluded

No packet-by-packetsimulation

Packet retransmission notincluded explicitly

Signalling details arepartially simulated

Packet by packetsimulation.

Packet retransmissionincluded

Mobility Mainly the pixel number givesthe user position.

Exact co-ordinates give theuser position.

Table 6-1: Comparison between STD simulation approach and the real time dynamic simulationapproach in WP3.


46/50

6.3 First results

As first results some STD simulations have been performed that can be comparedwith static simulation runs. A 19 base station scenario has been chosen, where theuser distribution has been concentrated on very few cells to guarantee measurableload for at least one cell. This cell has been chosen to compare the evaluation of astatic simulation run with some STD runs.

The current configuration of the STD simulation is to perform a recalculation afterevery event (user generation, user termination, activity change, see Figure 6-1. OneSTD simulation has been performed with 2 independent snapshots from which aSTD run of 5 seconds each has been continued. The second STD simulation startsfrom 5 snapshots followed by a 2-second STD run for each of them.

To compare simulation runtime, the number of snapshots for the static simulationhas been chosen to be the same as the total number of recalculations in the STDruns. The runtime turns out to be approximately the same.

Figure 6-1: Cell load of one certain cell, single values.

Figure 6-1 shows the value for the cell load at the chosen cell after eachrecalculation. The 5-snapshot simulation shows more variance for the load, sincemore independent snapshots have been the starting state for the STD run. As acomparison, the static run is shown as points showing the total independence of theobtained samples. This raises the impression, that the same results can be obtainedby a static simulation with the appropriate number of snapshots, but the differentgoals of the STD simulation has to be considered, since QoS parameters likenetwork delay cannot be provided by static simulations.


47/50

Figure 6-2: Cell load of one certain cell, mean value development.

The slopes, especially for the 5-snapshot simulation, correspond to the snapshots isFigure 6-1. E.g. the very low load values of the 4th snapshot between therecalculation number 1200 and 1600 leads to the steep decaying slope in Figure6-2. In both figures 6.3 and 6.4 the y-axis with the cell load has a very fine scalingin order to have the highest possible resolution for the obtained values. This means,the absolute deviation from the static simulation run is much smaller than itappears.

Figure 6-2 shows as comparison to Figure 6-1 the development of the mean valuesof the cell load. Both figures show a complete overlapping of both STD plots forabout the first 400 recalculations. This is obvious since both STD runs start fromthe same snapshot and simulate the same samples up to the time the 5-snapshotsimulation generates a new independent snapshot.


48/50

7 SummaryIn chapter 5 and chapter 6, the concepts for static simulations and short-termdynamic simulations have been introduced. The approach for short-term dynamicsimulations is based on the simulation analysis core of the static simulators. Thedynamics are mainly covered in the user generation and trigger and eventgeneration phases.

Snapshot

Short-termdynamic

Intermediate

Final Design

Dynamic

Project duration

Complexity

Modelling

Short-term dynamic Static

D2.4

D2.5

D2.2

D2.6

Static

Figure 7-1: Schematic view on the simulation design process in work package 2.

In Figure 7-1, the simulation design process as introduced in chapter 1 isillustrated. As the results of this deliverable report, a first approach to newsimulations for network performance evaluation has been designed. The short-termdynamic simulation method of chapter 6 is based on the static snapshot approach ofchapter 5. Both methods will be enhanced and extended in the remaining phases ofthe Momentum project.There are already two versions of both simulation methods available: On the onehand the simulations can be based on power control per user and on the other handon power control on a cell basis. Hence, four different versions of intermediatemethods are available where the ranking in terms of complexity from high to lowcomplexity is as follows: short-term dynamic simulations (PC on user basis), short-term dynamic simulations (PC on cell basis), static simulations (PC on user basis),and static simulations (PC on cell basis).Further evaluation and investigation into improvement and speed-up methods ofthe methods will continue in deliverable D2.5 where further mobility aspects will


49/50

be included and in deliverable D2.4 where additional QoS mechanisms will beincluded.


50/50

References

[1] L.M. Correia, E.R. Fledderus, E. Meijerink, R. Perera, A. Serrador, U.Trke, M. J.G. van Uitert, T. Winter, Identification of relevant parametersfor traffic modelling and interference estimation, MOMENTUMDeliverable D2.1, 2001.

[2] 3GPP, Multiplexing and channel coding (FDD), Technical SpecificationGroup Radio Access Network Report No 25.212 version 3.2.0, Mar. 2000.(http://www.3gpp.org).

[3] L. Ferreira, L.M. Correia, A. Serrador , G. Carvalho, R. Salles, UMTSdeployment and mobility scenarios, MOMENTUM Deliverable D1.3,2002.

[4] K. Chandra and A.R. Reibman, Modeling One and Two Layer VariableBit Rate Video, IEEE/ACM Trans. on Networking, Vol. 7, No. 3, pp. 398-413, 1998.

[5] ETSI, Universal Mobile Telecommunications System (UMTS); Channelcoding and multiplexing examples (3GPP TR 25.944 version 4.1.0),France, June 2001. (http://www.etsi.org).

[6] ETSI, Universal Mobile Telecommunications System (UMTS), Selectionprocedures for the choice of radio transmission technologies of the UMTS(UMTS 30.03 version 3.2.0), TR 101 112 v3.2.0, France, Apr. 1998.

[7] A. Klemm, C. Lindemann, M. Lohmann, Traffic Modeling andCharacterization for UMTS Networks, Proc. of the Globecom, InternetPerformance Symposium, San Antonio, TX, USA, Nov. 2001.

[8] J. Rbanos and C. Mesquida, Mobile Communication of Third Generation(In Spanish), Telefnica Mviles Espaa, Madrid, Spain, 2001.

[9] A.G Valko, A. Racz, and G. Fodor, Voice QoS in Third GenerationMobile Systems, IEEE Journal on Selected Areas in Communications,Vol. 17, No. 1, pp. 109-123, Jan.1999.

[10] http://www.weibull.com/LifeDataWeb/contents.htm[11] M.D. Yacoub, Foundations of Mobile Radio Engineering, CRC Press,

Florida, USA, 1993.[12] R. Perera, A. Eisenbltter, E.R. Fledderus, C. Grg, M. Scheutzow, S.

Verwijmeren, Pixel Oriented Mobility Modelling for UMTS NetworkSimulations, presented at IST summit 2002, Thessaloniki, 2002.

[13] A. Serrador and L. Ferreira, and L.M Correia, Traffic modelling forUMTS, Technical note, IST project MOMENTUM, January 2002.

[14] U. Kutzner, Initial interference design, Deliverable 5.1, IST projectMOMENTUM, November 2001.

comparison of different simulation approaches for cell performance evaluation.pdf

Documents

review thomas winter2002

signalling traffic

source traffic models

thomas winter siemens

traffic channels modelling

static simulations

shortterm dynamic simulations

final update thomas