13082710 cost 253 service efficient network interconnection via satellites

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FINAL REPORT

COST ACTION 253

SERVICE EFFICIENT NETWORK

INTERCONNECTION VIA SATELLITES

Contributors ............................................................................................................................................1

Chapter 1 .................................................................................................................................................4

Introduction to the COST Action 253.....................................................................................................4

1.1 Aim of the Action........................................................................................................................5

1.2 An overview of the activity.........................................................................................................5

1.3 Constraints ..................................................................................................................................7

1.4 Issues ...........................................................................................................................................8

Chapter 2 ...............................................................................................................................................11

Appropriate Traffic Generators for the simulation of services supported by non-GEO constellation..11

2.1 Source Traffic Parameters and Descriptors...............................................................................11

2.1.1 Peak Cell Rate and Cell Delay Variation Tolerance .........................................................12

2.1.2 Sustainable Cell Rate and Intrinsic Burst Tolerance.........................................................13

2.1.3 Mean Burst Period.............................................................................................................13

2.1.4 Burstiness ..........................................................................................................................13

2.2 Quality of Service Parameters...................................................................................................14

2.3 ATM Service Categories...........................................................................................................14

2.3.1 Constant Bit Rate (CBR) Service Category ......................................................................14

2.3.2 Variable Bit Rate (VBR) Service Category ......................................................................15

2.3.3 Available Bit Rate (ABR) Service Category.....................................................................15

2.3.4 Unspecified Bit Rate (UBR) Service Category.................................................................16

2.3.5 Guaranteed Frame Rate (GFR) Service Category.............................................................16

2.4 Statistical Behaviour of Traffic Sources ...................................................................................17

2.4.1 Voice .................................................................................................................................17

2.4.2 Video .................................................................................................................................17

2.4.3 Data ...................................................................................................................................19

2.4.4 Multimedia ........................................................................................................................20

2.5 Influences on traffic characteristics...........................................................................................20

2.6 Criteria for the Selection of Source Models..............................................................................22

2.7 Multi-State Markov Source Models..........................................................................................22

2.7.1 General Modulated Deterministic Process (GMDP) Model .............................................22

2.7.2 The On-Off Model ............................................................................................................22

2.7.3 The Interrupted Poisson Process (IPP)..............................................................................23

2.7.4 Interrupted Bernoulli Process (IBP) ..................................................................................24

2.7.5 The Birth-Death Process ...................................................................................................24

2.7.6 The Markov Modulated Poisson Process (MMPP)...........................................................26

2.8 Self Similar Models...................................................................................................................27

2.9 GEOGRAPHIC TRAFFIC MODELS ......................................................................................28

Chapter 3 ...............................................................................................................................................31

Transmission Schemes ..........................................................................................................................31

3.1 Modulation techniques ..............................................................................................................32

3.1.1 Overview of modulation schemes for satellite transmission.............................................32

3.1.2 Study of a particular modulation scheme; variable rate N-MSK ......................................36

3.2 Coding techniques.....................................................................................................................42

3.2.1 Overview of channel codes for satellite transmission .......................................................43

3.2.2 Study of a particular channel code; TPC...........................................................................47

3.3 Synchronisation.........................................................................................................................49

3.3.1 Overview of required synchronisation for satellite transmission ......................................50

3.3.2 Study of Doppler frequency shift compensation for mobile satellites ..............................50

3.4 Catching co-channel interference..............................................................................................61

3.4.1 Satellite system model.......................................................................................................62

3.4.2 IPhP3 with deterministic marks ........................................................................................63

3.4.3 Two first moments of cumulated interference power ....................................................64

3.5 Chapter summary and perspectives...........................................................................................66

Chapter 4 ...............................................................................................................................................71

Networking............................................................................................................................................71

4.1 LAN Interconnection ................................................................................................................72

4.1.1 Introduction .......................................................................................................................72

4.1.2 Satellite Network Architecture..........................................................................................72

4.1.3 Terrestrial/satellite Network Termination Module Characteristics...................................74

4.1.4 Satellite constellations.......................................................................................................75

4.1.5 Impact of Satellite Constellation on System Performance ................................................75

4.1.6 Satellite Payload Architecture...........................................................................................77

4.1.7 Satellite Payload Reference Functional Architecture........................................................80

4.1.8 Inter-Satellite Links (ISLs)................................................................................................81

4.2 Resource Control.......................................................................................................................82

4.2.1 Resource Allocation ..........................................................................................................82

4.2.2 Call Set-up and Routing ....................................................................................................85

4.2.3 Congestion control ..........................................................................................................114

4.2.4 Multicast..........................................................................................................................116

4.3 Reliability................................................................................................................................121

4.3.1 Background .....................................................................................................................121

4.3.2 The modeling and simulation method.............................................................................122

4.3.3 Payload reference functional architecture and model .....................................................124

4.3.4 Simulation of the cycle of life of a constellation.............................................................127

4.4 Security ...................................................................................................................................129

4.4.1 Background .....................................................................................................................129

4.4.2 Status of Current Research in Security in Communication Networks ............................129

4.4.3 Security Services Implementation Issues ........................................................................130

4.4.4 Security implementation issues in Satellite ATM Networks ..........................................133

4.4.5 Security infrastructure .....................................................................................................134

4.5 Conclusions .............................................................................................................................135

Chapter 5 .............................................................................................................................................142

Evaluation Tools .................................................................................................................................142

5.1 LeoSim: a simulator for routing ..............................................................................................144

5.1.1 Network Model as supported by LeoSim........................................................................144

5.1.2 Introducing Versatility in the Network Model ................................................................146

5.1.3 Implementation issues .....................................................................................................148

5.2 GALILEO: a framework for joint expertise............................................................................150

5.2.1 Both global and limited coverage....................................................................................151

5.2.2 The architecture...............................................................................................................152

5.2.3 Assumptions and definitions ...........................................................................................152

5.2.4 Logical behaviour............................................................................................................153

5.2.5 The connection setup.......................................................................................................156

5.2.6 Traffic over a connection ................................................................................................158

5.2.7 The topology ...................................................................................................................158

5.2.8 Routing............................................................................................................................160

5.2.9 Fault management ...........................................................................................................161

5.2.10 Some implementation aspects .........................................................................................161

5.2.11 GaliLEO methodology and project management ............................................................163

5.3 CONSIM: a complementary tool for reliability ..................................................................164

5.3.1 A communicating process approach ...............................................................................166

5.3.2 Customisation of the model and the control scheme......................................................167

5.3.3 Development Status ........................................................................................................168

5.4 AristoteLEO............................................................................................................................168

5.4.1 The user interface............................................................................................................168

5.4.2 The architecture...............................................................................................................169

5.4.3 The performance .............................................................................................................171

5.5 SEESAWS: an ambitious concept...........................................................................................171

5.5.1 Traffic Simulator .............................................................................................................173

5.5.2 Satellite Simulator ...........................................................................................................174

5.5.3 Ground / Space Dynamics Simulator ..............................................................................175

5.5.4 Inter-Plane Dynamics Simulator .....................................................................................175

5.5.5 Statistics Collector and Processor ...................................................................................175

Annex A ..............................................................................................................................................179

Annex B ..............................................................................................................................................197

Annex C ..............................................................................................................................................201

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Contributors

This report has been drafted by member of the Management Committee based on originalcontributions by the following authors:

CHAPTER AUTHOR INSTITUTEChapter 1: COST Action 253:Introduction

Gerard Maral

Erina Ferro

Ecole Nationale Superieure desTelecommunications-Site de Toulouse10, Avenue E.Belin, BP 400431028 TOULOUSE Cedex 4, FranceTel: 33 (0) 5 62 17 83 64Fax: 33 (0) 5 62 17 83 75e-mail: [email protected]

CNUCE/C.N.RPisa C.N.R.Research Area,Via V. Alfieri 1,56010 Ghezzano (Pisa), ItalyTel: +39-050-315-3070Fax: +39-050-313-8091/8092e-mail: [email protected]

Chapter 2 : Appropriate trafficgenerators for the simulation ofservices supported by non-GEOconstellations

F. Niovi Pavlidou

Tolga Ors

Javier Aracil

Aristotle University of Thessaloniki,Department of Electrical & ComputerEngineering,Telecommunications Division54006 Thessaloniki, PO Box 1641, GreeceTel:+30 31 996380Fax:+30 31 996285e-mail: [email protected]

Nortel NetworksLondon RoadHarlow, Essex CM17 9NATel: +44 (0) 1279 405867Fax: +44 (0) 1279 403206e-mail: [email protected]

Dpto. Automatica y ComputacionUniversidad Publica de NavarraCampus Arrosadia s/n31006 PAMPLONATel: +34 48 16 97 33Fax: +34 48 16 92 81e-mail: [email protected]

Chapter 3: Transmission schemes Vendela Maria Paxal

Tomaz Javornik

Telenor R&D, Satellite and RadioCommunicationsInstituttveien 232027 KjellerNorwayTel: +47 63 84 86 12Fax: +47 63 81 98 10e-mail: [email protected]

Institut Jozef StefanJamova 391000 LjubljanaSloveniaTel: +386 1 477 3669Fax:+386 1 426 2102e-mail : [email protected]

2

Gorazd Kandus

Marie-Ange Remiche

Institut Jozef StefanJamova 391000 LjubljanaSloveniaTel: +386 1 477 3608Fax: +386 1 426 2102e-mail: [email protected]

Universite Libre de BruxellesFaculte des Sciences- CP212Department dInformatique,Blvd du Triomphe,B-1050-Bruxelles, Belgiume-mail: [email protected]

Chapter 4: Networking Y. Fun Hu

Laurent Franck

Evangelos Papapetrou

Ioannis Gragopoulos

Mihael Mohorcic

Denis Trcek

Faculty of Information and EngineeringSystemsSchool of ComputingLeeds Metropolitan UniversityThe GrangeBeckett Park CampusLeeds LS6 3QSUnited KingdomTel: +44 113 2837596Fax: +44 113 2833182e-mail: [email protected]

TELECOM Paris/TSA17 bis Rue Riquet F-31000Toulouse FRANCETel: +33 5 61 58 80 01Fax: +33 5 61 58 80 14e-mail: Laurent.Franck@{enst.fr,tesa.prd.fr}

Aristotle University of Thessaloniki,Department of Electrical & ComputerEngineering,Telecommunications Division54006 Thessaloniki, GreeceTel:+30 31 996380Fax:+30 31 996285e-mail: [email protected]

Aristotle University of Thessaloniki,Department of Electrical & ComputerEngineering,Telecommunications Division54006 Thessaloniki, GreeceTel:+3031 996380Fax:+30 31 996285



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Ales Svigelj

Haitham Cruickshank

Lloyd Wood

Fairouz Dabbarh

Marco Annoni

Gerard MaralF. Niovi PavlidouTolga OrsGorazd Kandus


Centre for Communication System ResearchUniversity of SurreyGuildford GU2 5XHSurrey U.K.Tel:+44 1483259844 (Ext. 3420)Fax:+44 1483259504e-mail: [email protected]

Centre for Communication System ResearchUniversity of SurreyGuildford GU2 5XHSurrey U.K.Tel: +44 1483259489Fax +44 1483259504e-mail: [email protected]

Department of Computer ScienceBrussels University CP 212B-1050 Brussels BelgiumTel:+32 2 6505592Fax:+32 2 6505609e-mail: [email protected]

Centro Studi e Laboratori TelecomunicazioniS.p.A., 10148 Torino(Italy)-Via G. ReissRomoli, 274Tel:+39 011 2285042Fax:+39 011 2285520e-mail: [email protected]

Chapter 5: Evaluation Tools Francesco Potorti

Simone Bizzarri

Erina FerroLaurent FranckEvangelos PapapetrouI. GragopoulosMarco Annoni

CNUCE/C.N.RPisa Research Area, Via V. Alfieri 1, 56010Ghezzano (Pisa), ItalyPh. +39-050-315-3058Fax: +30-050-313-8091/8092e-mail:[email protected]

Centro Studi e Laboratori TelecomunicazioniS.p.A., 10148 Torino(Italy)-Via G. ReissRomoli, 274

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Chapter 1

Introduction to the COST Action 253

Broadband satellite networks have become an important segment of the global communicationinfrastructure, due to their wide geographical coverage, quick and cost-effective deployment andconfiguration flexibility. They provide seamless integration of applications and services whichhave traditionally been available via terrestrial networks, and, in addition, they make reachablethose remote and less developed regions where access by terrestrial fibres is not commerciallyattractive, due to the high installation costs. In such areas, economic and social integration with thewider community is a major priority. Earlier opportunities for the implementation of multimediaservices will be made available by using satellites.

Up to a few years ago, the satellite communication systems were almost all based on geostationarysatellites. The major advantage of these satellites is their unchanging position with respect to theearth surface, thus no control overhead is required to track the satellites. The drawbacks are thehigh cost in launching the satellites into the geostationary orbit, the long inter-satellite linkdistance, the high on-board power required, and the long propagation delays. In the meantime, therapid growth in the demand for personal communications has led to the need for intense researchand development efforts towards a new generation of personal communication systems. Any futuresystem will integrate different services to provide voice and data communications via a multimediaportable terminal. The challenge is to build a personal communication network extended on aworld basis which allows the transfer of multimedia information between any two users, at anypoint of the earth, in a reliable way, and either in a fixed position or travelling by aircraft, train,truck, etc.

The satellite configuration in an integrated environment has considerable possibilities for variation.Essentially, four types of satellite constellations can provide the space element in an integratedspace/terrestrial network, namely: geostationary orbit (GEO); highly elliptical orbit (HEO);medium earth orbit (MEO), and low earth orbit (LEO). Hybrid constellations incorporating severaltypes of orbits can be considered.

The low orbit satellites (LEO) systems promise to provide this global communication. A LEOcommunication system consists of a number of satellites in orbits of 500-7500 Km over the earth.The satellites are organised in a constellation which determines how large a portion of the earthsurface is covered by the system. LEO systems overcome all the disadvantages of using thegeostationary satellites, but, on the other hand, they introduce new problems, mainly due to theshort time of visibility of a satellite, and the relative movements of satellites with respect to theuser terminals. Several LEO systems are currently operational, such as Orbcomm and Globalstar,and other such as Iridium and ICO have faced financial difficulties. while others are still underplanning and development. Orbcomm, Starnet and Leosat all belong to the class of the smallersystems, which offer (or will offer) paging, tracking, messaging and vehicular monitoring services.The larger systems, such as Iridium (idem) and Globalstar, offer world-wide voice and datacommunication services, while the Teledesic system is projected to offer broadband services,including video conferencing and interactive multimedia. Annex A, presents the organisation ofnon-geo constellation.

From a users perspective, standard applications should be supported across all types of networksand hence, the requirement to transport standard network protocols across both space andterrestrial components will be required. Ideally, the user should be unaware of the type of networkused to provide the required service or application, be it terrestrial or satellite. The success inrecent years of Internet has resulted in the Transmission Control Protocol/Internet Protocol

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(TCP/IP) protocol being the most widely used network protocol. Asynchronous Transfer Mode(ATM) is a candidate both as a support for TCP/IP protocol and for dedicated applications abovesatellite Medium Access Control (MAC) layer. TCP/IP has been designed for terrestrial networks,which are characterised by low delays and error rates. This is unfortunately not true for satellitenetworks with geostationary satellites, where the round trip delay is about 255 ms and the bit errorrates can become significant in case of fading. However, investigations show that applications likefile transfer, database access, remote-login, and e-mail can be well supported on satellite links. Thesituation is more complex when high interactive or real-time applications are supported (client-server applications, video, voice, etc.). To decrease the delay, satellite networks which use LEOsatellites are more attractive.

The COST Action 226, which ended in 1995, demonstrated the feasibility of LAN interconnectionvia GEO satellites using transparent satellites and fixed low cost VSAT earth stations. The COSTAction 253 is the follow-on of that activity, continuing the work of interconnection of LANs withnon-GEO satellites. This activity is important because non-GEO systems for personalcommunications (now in process) have fostered the demand for new services, such asinterconnection of wired and wireless LANs. This demand has already generated manufacturing ofnew types of equipment. Hence, in our opinion, it would be important that the European industryfinds a forum where expertise on non-GEO satellite systems is available.

1.1 Aim of the Action

The MOU of Cost Action 253 describes the main objectives of this activity as follows Mainobjectives are to study, define, implement and test systems for LAN interconnection through non-geostationary satellites. Problems related to satellite motion and the design of gateways andtransmission systems shall be studied, suitable protocols elaborated and simulation tools developedfor system dimensioning and performance evaluation. The applicability of emerging traffic,Quality of Service and resource management schemes and mechanisms within advanced satellitecommunication systems interworking with terrestrial communication networks shall be assessedand specific functions able to cope with or benefit from the characteristics of satellite systems shallbe analysed in order to support their efficient integration..

1.2 An overview of the activity

It is evident that many research aspects of transmission had to be covered, such as traffic models,data routing on the constellation, faults, security, access techniques, handovers, etc. To fullyaddress the challenges of this action, the participants to the Action decided to divide the researchactivity into six working groups, in the following listed with the relevant research aspects whichhad to be covered.

WG 1: Traffic Characterisation

1.1 Types of services for different network architectures.

1.2 Source modelling for any service type.

1.3 Traffic in/out the satellite node for passive or regenerative satellites (switching) for various

channel access techniques.

1.4 An end-to end traffic model justified by simulation work.

WG 2: Definition of Network Organisation

2.1 To determine the number, size and type of terminals to be interconnected.

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2.2 To determine the number and types of gateways.

2.3 To determine the reference constellation.

2.4 Inter-Satellite Links(ISL)/On-Board Processing (OBP)/ On-Board Switching (OBS) effects on

the network performance.

2.5 To define a more detailed network and subsystem architecture.

WG3 Transmission Schemes

3.1 Broadband channel characterisation.

3.2 Access techniques.

3.3 Frequency issues.

3.4 Coding and modulation.

3.5 User terminals.

WG4 Networking.

4.1 Local Area Network (LAN) and individual earth stations -Gateway Interconnection.

4.2 Link Layer Functions.

4.3 Network Operation.

4.5 Transport Layer Function.

WG5 Security Issues

5.1 Study of threats and security requirements. Identification of suitable encryption and digital

signatures systems.

5.2 Authentication and encryption key exchange protocols.

5.3 Satellite-ATM security implementation.

5.4 Other issues such as Trusted Third Parties, network management security and billing.

WG6: Performance evaluation & Recommendations

6.1 Performance measurement of an heterogeneous network where multimedia connections are

treated as a superposition of single-media connections.

6.2 Recommendations according to the obtained best performance results, if possible.

Figure 1.1 shows the interconnection among the working groups.

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Oth

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and

Rel

ated

Wor

kSystem

Specification and Standardisation

COST 253

WG 4: NetworkingWG 4: Networking

Mobility Management

Network Architecture & FunctionalArchitecture for Local Area Network

interconnection via SatelliteAsynchronous Transfer Mode Network

Gateway-Satellite Access

Local Area Network -Gateway interconnection

WG 6: Performance Evaluation WG 6: Performance Evaluation & Recommendations& Recommendations

System Capacity

End-to-end delay Mean Throughput

Channel Occupancy

Call DroppingCongestion on Satellite Link

Cell Loss Ratio

WG 2: Definition of Network OrganisationWG 2: Definition of Network Organisation

Phase AGross Definition of Generic Network Architecture for Local

Area Network interconnection with non Geostationary Satellites with traffic from 64 kbits/s -34 Mbits/s

Phase BSubsystem Definition

WG 1: Traffic CharacterisationWG 1: Traffic Characterisation

Service Identification

Traffic Models at Local Area NetworkGateway for Fixed or Mobile Terminals

Associated Traffic

WG 5: Network Security IssuesWG 5: Network Security Issues

Security Requirements for Local Area Networks interconnections with non Geostationary Satellites

Satellite - Asynchronous Transfer Mode Security

Authentication Protocols

WG 3: Transmission SchemesWG 3: Transmission Schemes

Link Budgets

Propagation Studies

Coding / Modulation Channel Characterisation

Interference

Frequency Planning

Fade Countermeasurements

Satellite Mobility for Non Geostationary Satellites

Wireless Local Area Network via Satellite

Mean Time for Channel Set-up/Release

Service Efficient Network Interconnection Via Satellites

Figure 1.1 : Interconnections between different working groups

1.3 Constraints

As it can be noted from the previous overview of the activities, almost all aspects of transmissionsover LEO constellation had to be addressed. All the participants to the project had indeed enoughbackground in specific items, so to cover almost all the research aspects, but the most difficultpoint to face with was to have realistic data relevant to the LEO constellations. For example, WG1,involved in the characterisation and modelling of aggregated traffic at the earth station and at theon-board switch of the LEO constellation, would have benefited by contacts with satelliteorganisations in order to have a realistic modelling approach. WG2 too, which had to take intoaccount some considerations in the design of the satellite constellation, such as the key satellitepayload architecture options (ISL, OBP) and their advantages/disadvantages, would haveappreciated very much the collaboration with the industry.

For these reasons some people from the industrial world were invited in our MC meetings, in thehope of setting up a sort of collaboration with the research and the industrial environments.Unfortunately, it was clear since the beginning that, because transmission over LEO constellationsat the current time is a hot topic due to the market interests and the industrial competition, nocollaboration was possible at any level. This point merits some reflection. The researchcommunities have little possibility to influence, at any level, any industrial decision if strongmarket interests are in play. There is a hardened competition among the industries to conquer themass-market, and to awaken in them the necessity of new services. The industry has therefore littletime to look at the results on specific research aspects of the academic or research world, becauseof the time-to-market constraints

This lack of collaboration with the industry was the first difficulty we encountered.

Another difficulty was to design a simulator able to cover all the aspects of the complex satellitenetwork that was emerging as a result of our studies, thus allowing a performance evaluation of thesingle components of the network. The tools available on the market or downloadable from theweb were found to be inadequate, or too expensive, or impossible to be installed in all theinstitutes. Therefore the participants of the Action were soon aware of the necessity to develop a

8

software tool kit for the evaluation of satellite system performance. It rapidly became obvious thatthe large amount of simulation time and effort required for the simulation of real systems were notcompatible with the resources that were either available or could become available. Furthermore,due to the poor collaboration with the industry mentioned previously, there was some uncertaintyas to which parameters were actually selected for the real systems. Therefore, two approaches weredecided upon:

not trying to simulate the real systems but 'real-like' systems, evaluating the performance ofsystems with a realistic set of parameters, but still allowing for simplified simulation runs;

setting up blocks of simulation software that could serve as building blocks for more elaboratesimulators as experience was gathered. In this respect, several participants to the Action joinedin a consortium, named SEESAWS, to forward a proposal to the IST Program of the EuropeanCommission for the development and the implementation on a free access web site of a networksimulator. In this proposal, the members (most being members of this COST action) aim todesign, develop and validate a modular simulation and emulation environment, which allows toevaluate the performance of future satellite networks by providing a valuable insight into theachievable performance of future satellite systems, in terms of quality of service, mainly from anetwork perspective standpoint. By using the simulation/emulation environment, systemdesigners will be able to verify how different design solutions would impact on the systemperformance so minimising the risks from the early stage of the design phases. On the otherhand, potential satellite service providers and investors could perform comparative analysesamong different candidate system solutions in order to better understand if their expectation canactually be met.

This collaboration exactly matches the spirit of the COST activities, which aim at the co-operation in

the field of science and technology among different European countries.

1.4 Issues

A large amount of work has been done in this Action, as can be seen from the considerablenumber of papers covering specific items among those listed in the WGs activities. Of coursenot all the topics listed in the six working group activities were fully covered, mainly due tolack of time and insufficient number of people. For this reason the results of the workperformed in this Action are presented in five chapters instead of six, as some results fromdifferent working groups were aggregated together. In the following, the mapping betweenworking groups and chapters of this final technical report is presented.

Chapter 2: Appropriate traffic generators for the simulation of services supported by non-GEO

constellations.

WG1 was encharged to study the characterisation / modelling of aggregated traffic at the earthstation or at the on-board switch of the LEO constellation. An overview of source modelling andservice characterisation was provided as a basic platform, but the research mainly focused on self-similar modelling. A research on geographic traffic modelling was carried on to cope with thecharacteristic of non uniform loading of the LEO constellations.

The work done by the WG1 team is reflected in Chapter 2, where service categories andrequirements are classified, a source activity is characterised (Markovian and Regression models),and models for aggregated traffic are presented.

9

Chapter 3: Transmission schemes

The radio interface design in the broadband satellite systems is a major factor determining theefficiency of the systems in the utilisation of the satellite system resources as bandwidth andpower. Utilisation of advanced techniques in the radio interface can bring important systemenhancements. Within WG3 some existing schemes and promising options were analysed.

The results achieved by the WG3 team are reflected in Chapter 3. They are limited to an analysisof modulation schemes, coding schemes and Doppler frequency shift correction. The application issatellite transmission over mobile satellites. In each of the subjects (modulation techniques andcoding techniques) a general survey of possible schemes has been presented, and then a study ofone particular scheme in more detail has been carried out. For the Doppler shift correction, threetechniques have been studied in detail for comparison; all three techniques are based on correctionin the receiver. For all the particular studies, the results are based on Monte Carlo simulations. .The interference problems also were subject to evaluation, as the strict international regulationsmust be respected by all systems. The crowdedness of Ku-band imposes interference control. Thework presented in Chapter 3 is related to the modellisation of interference created by user signalsat the satellite, assuming a CDMA-based system.

Chapter 4: Networking

This chapter assembles the major results of theWG2, WG4 and WG5 teams. We decided to presentthem together, because sometimes, it was difficult to define clear boundary lines between thedifferent activities.

The main objective of WG2 was to define a general network architecture to interconnect LANswhich have traffic ranging from 64 kbps to several hundred Mbps, using non-geostationarysatellites. The satellite network entity options, which have to be taken into account in the design ofa satellite constellation, were analysed. Then the key satellite payload architecture options wereidentified, and their advantages/disadvantages discussed. In order to be able to choose an optimalsatellite constellation and satellite architecture to efficiently interconnect LANs, recent non-GEObroadband proposals were compared. The selected satellite network entity options, taken by theCOST253 MC, use Inter-Satellite Links (ISLs) and On-Board Switching (OBS). ISLs wereselected because they provide some independence from the terrestrial infrastructure allowing end-to-end routing over the space segment. ISLs require On-Board Switching (OBS), which wasselected because of the flexibility it provides in routing and multicast.

Work undertaken in WG4 mainly addressed routing strategies and traffic congestion controlmechanisms. Concerning the routing strategies, a simulation tool was developed, having the goalto study the effects of the different routing algorithms on the performance of the network (such asthe blocking probability during connection set-up, the load distribution in the network, theproperties of the routes, and the complexity of the routing algorithm), and to study the effects ofLink State Packet data unit (LSPdu), broadcast policies on the network load, and the blockingprobability during set up.

WG5 had to face with problems related to the user-satellite network authentication, informationprivacy and satellite-ATM security. Data encryption and digital signature techniques were studiedand elaborated, and their implementation in both hardware and software examined.

In Chapter 4 several network control issues are considered: the use of non-geostationary satellitesto provide LAN interconnection, the network resource control aspects, the routing strategies, callcontrol functions and multicasting techniques. Results from different studies are presented. Adetailed description on the reliability requirements and techniques for satellite system is alsopresented. Finally, the security requirements and their implementation issues are discussed.

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Chapter 5: Evaluation Tools

WG6 had to collect, in a certain sense, the results of the previous WGs, and had to measure theperformance of a heterogeneous network where multimedia connections are treated as a super-position of single-media connections, of which satellites are one possibility. For obvious reasons,simulation proved to be the only possibility to carry out this work, organising it in groups ofparameters which had to be tested according to both the characteristics of the satellite network(such as different satellite allocation policies, different number of satellites per orbit, differenttypes of faults in the satellites of the constellation, etc.), and other choices relevant to the terrestrialnetwork.

As different aspects of the non-geostationary satellite network had to be studied, different ad-hocsimulation tools have been used, and a new one was studied whose aim is (because itsdevelopment is still in course) to provide a test-bed for studying various aspects ofcommunications using satellite constellations.

Chapter 5 is devoted to the presentation of the used evaluation tools. At the end of the chapter theSEESAWS proposal is briefly described.

Annexes

Annex A - Satellite constellation design for network interconnection using non-geostationarysatellites

Annex B - List of Temporary Documents (TD) and External Documents (ED)

Annex C - List of the publications made by participants in the technical literature (alreadypublished, or, at the current time, submitted for publication).

11

Chapter 2

Appropriate Traffic Generators for thesimulation of services supported by non-

GEO constellation

Provision of the requested Quality of Service (QoS) and efficient utilisation of resources are someof the objectives of COST 253. In order to achieve these challenging tasks it is necessary toanalyse and/or simulate such a network before actually implementing it. Modeling services usingappropriate source and traffic models are therefore very important to enable simulations andanalysis to be performed with the selected network architecture.

Source modelling is used to mimic the behaviour of a source. Traffic modelling on the other handfocuses on aggregated traffic patterns. Multiplexed models will capture the effects of statisticallymultiplexing bursty sources and will predict to what extent the superposition of bursty stream issmoothed. Hence traffic models will be used for designing connection admission controlalgorithms and traffic engineering the network. The most important application of source models isin predicting the QoS that a particular application might experience under various networkconditions. In order to assess the QoS provided to individual services, source models andaggregated source models (traffic models) will be used extensively.

A number of traffic models are already known from the traditional fixed and mobile network planningand they can be classified in:a) short-range dependent models, which have a correlation structure that is significant for relatively

small lags andb) long-range dependent processes, which have a significant correlation even for large lags.

Traffic models can be based on analysis, simulation and measurements. Measurements are used toverify the accuracy of the simulation model which is usually based on analysis.

Source characterisation at the macro level is defining the source traffic characteristics and its QoSrequirements. The traffic characteristics of an application are the minimum set of parameters that auser can be expected to declare while providing the network with as much information as possibleto effectively control network traffic and achieve high resource utilisation.

After defining various parameters which can be negotiated between user and network, servicecategories supported by ATM networks are identified. Each service category can support variousservices depending on which traffic parameters can be declared and which QoS guarantees arerequired by the user. The statistical behaviour of generic traffic services such as voice, video, dataand multimedia are provided. Then the influences on traffic characteristics are discussed and thecriteria in selecting source models for traffic sources are explained. Finally a review of widelyused source models, and traffic models is provided.

2.1 Source Traffic Parameters and Descriptors

Source traffic parameters are used to describe traffic characteristics of a source. They may bequantitative or qualitative (e.g. telephone, videophone). For an ATM connection, traffic parameters

12

are grouped into a source traffic descriptor, which in turn is a component of a connection trafficdescriptor.

A source traffic descriptor is the set of traffic parameters of the ATM source. It is used duringthe connection set-up to capture the intrinsic traffic characteristics of the connection requested by aparticular source. The set of traffic parameters in a source traffic descriptor can vary fromconnection to connection. A connection traffic descriptor characterises a connection at the UserNetwork Interface (UNI). It consists of:

Source traffic descriptor Cell Delay Variation Tolerance (CDVT) Conformance definition

The connection traffic descriptor is used by the network during connection set-up to allocatenetwork resources and derive parameters for Usage Parameter Control (UPC). The conformancedefinition is used by the UPC to distinguish conforming and nonconforming cells withoutambiguity.

An important issue is the set of traffic parameters to include in the source traffic descriptor. Allparameters should be simple to be determinable by the user, interpretable for billing, useful toConnection Admission Control (CAC) for resource allocation, and enforceable by UPC. The setshould be small but sufficient for the diverse types of traffic in B-ISDN. Some proposed sourcetraffic parameters which will be explained in detail are:

Peak Cell Rate (PCR) and Cell Delay Variation Tolerance (CDVT)

Sustainable Cell Rate (SCR) and Maximum Burst Size (MBS)

Intrinsic Burst Tolerance (IBT)

Mean duration of the burst (ton)

2.1.1 Peak Cell Rate and Cell Delay Variation Tolerance

The Peak Cell Rate (PCR) of the ATM connection is the inverse of the minimum interarrival timeT between two cells on a transmission link. It specifies an upper bound on the traffic that can besubmitted on an ATM connection [22]. The ATM Forum and ITU-T define the PCR and CDVtolerance using the Generic Cell Rate Algorithm (GCRA) and equivalent terminal model [23] [22].The reason for variation in the cell delay is that ATM Layer functions (e.g. cell multiplexing) mayalter the traffic characteristics of ATM connections by introducing Cell Delay Variation (CDV).When cells from two or more ATM connections are multiplexed, cells of a given ATM connectionmay be delayed while cells of another ATM connection are being inserted at the output of themultiplexer. Similarly, some cells may be delayed while physical layer overhead or OAM cells areinserted. Consequently with reference to the peak emission interval T (i.e. the inverse of thecontracted peak rate), some randomness may affect the inter-arrival time between consecutive cellsof a connection. The upper bound on the clumping measure is the CDV Tolerance (CDVT) . TheCDVT at the public UNI, is defined in relation to the PCR according to the GCRA (T, UNI)

For the time being two extreme cases of characterising the CDVT [22] have been identified:

Loose Requirements on CDV Tolerance

A large amount of CDV can be tolerated. In this case, only the specification of the maximum valueof CDV tolerance MAX that can be allocated to a connection is envisaged. MAX is intended as themaximum amount of CDV that can be tolerated by the user data cell stream.

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Stringent Requirement on CDV Tolerance

A connection should not be denied because of the required CDV tolerance if this CDV tolerancerequirement is less than or equal to PCR which is given by:

PCR PCRPCR

T

T =

max , 1

where:

TPCR is the peak emission interval of the connection (in seconds),

is the cell transmission time (in seconds) at the interface link speed,

is a dimensionless coefficient (suggested value is 80 [22]).

2.1.2 Sustainable Cell Rate and Intrinsic Burst Tolerance

The Sustainable Cell Rate (SCR) is an upper bound on the average rate of the conforming cells ofan ATM connection, over time scales which are long relative to those for which the PCR isdefined. The Intrinsic Burst Tolerance (IBT) [22] specifies the maximum burst size at the PCR orin other words the maximum deviation from the average rate. These parameters are intended todescribe VBR sources and allow for statistical multiplexing of traffic flows from such sources .

The SCR and IBT traffic parameters are optional traffic parameters a user may choose to declarejointly, if the user can upper bound the average cell rate of the ATM connection. To be useful tothe network provider and the customer, the value of the SCR must be less than the PCR. The SCRand the IBT (denoted as IBT) are defined by the GCRA (TSCR,IBT). SCR and IBT belong to theATM traffic descriptor [22]

Translation from the Maximum Burst Size (MBS) to IBT will use the following rule:

IBT = (MBS-1)(TSCR-TPCR) seconds

where x stands for the first value above x out of the generic list of values.

If the user has the knowledge of IBT rather than of the maximum burst size, than the following ruleapplies:

MBS = 1+ [IBT / (TSCR-TPCR)] cells

where x stands for rounding down to the nearest integer value.

2.1.3 Mean Burst Period

The mean burst period (Ton) is defined as the average time the source is transmitting cells at thepeak rate. This parameter is widely used for bursty sources.

2.1.4 Burstiness

Burstiness (), following the ITU-T definitions, corresponds to the ratio of the peak-to-averagetraffic generation rate (=PCR/SCR).

14

2.2 Quality of Service Parameters

Quality of Service (QoS) is measured by a set of parameters characterising the performance of anATM layer connection. These QoS parameters (referred to as network performance parameters byITU-T) quantify end-to-end network performance at the ATM layer.

Six QoS parameters are identified by the ITU-T and ATM-Forum which correspond to a networkperformance objective. Three of these may be negotiated between the end-systems and thenetworks. One or more values of the QoS parameters may be offered on a per connection basis,corresponding to the number of related performance objectives supported by the network. Supportof different performance objectives can be done by routing the connection to meet differentobjectives, or by implementation-specific mechanisms within individual network elements. Thefollowing QoS parameters are negotiated:

Cell Loss Ratio (CLR)

Cell Loss Ratio (CLR) is the ratio of total lost cells to total transmitted cells in a connection.

Cell Transfer Delay (CTD)

This is defined as the elapsed time between a cell exit event at the measurement point 1 and thecorresponding cell entry event at measurement point 2 for a particular connection.

Cell Delay Variation (CDV)

Two performance parameters associated with CDV are defined. The first parameter, One-PointCDV, is defined based on the observation of a sequence of consecutive cell arrivals at a singleMeasurement Point (MP). The second parameter, Two-Point CDV, is defined based on theobservation of corresponding cell arrivals at two MPs that delimit a virtual connection portion.[23] provides more details on CDV measurements.

The following QoS parameters are not negotiated:

Cell Error Ratio (CER)

Cell Error Ratio (CER) is the ratio of total errored cells to the total of successfully transferred cellsin a connection.

Severely Errored Cell Block Ratio (SECBR)

Severely Errored Cell Block Ratio (SECBR) is the ratio of total severely errored cell blocks tototal cell blocks in a connection.

Cell Misinsertion Rate (CMR)

Cell Misinsertion Rate (CMR) is the total number of misinserted cells observed during a specifiedtime interval divided by the time interval duration.

Further information on ATM layer QoS may be found in ITU-T Recommendation I.356.

2.3 ATM Service Categories

Services provided at the ATM layer, consists of different service categories which will beexplained in this Section.

2.3.1 Constant Bit Rate (CBR) Service Category

The CBR service category is used by connections that request a static amount of bandwidth that is

15

continuously available during the connection. This amount of bandwidth is characterised by thePeak Cell Rate (PCR). CBR service is intended to support real-time (rt) applications requiringtightly constraint delay variation but is not restricted to these applications. Typical examples ofCBR services include voice, video, and audio.

In the classical Synchronous Transfer Mode (STM) networks, fluctuating information rate must beconverted into a CBR, namely the rate at which this STM network is operating. For instance, 64kbit/s or 2 Mbit/s in N-ISDN.

CBR traffic is easy to manage in the network, since constant bandwidth is reserved for each CBRconnection throughout its duration, independent of whether the source is actively transmitting or ina silent state. This is, however, an inefficient use of the transmission bandwidth. In particular,since the amount of information generated by most applications varies over time it is possible toreserve less bandwidth in the network than the applications peak bit rate, thereby allowing moreconnections to be multiplexed and increasing the resource utilisation. In initial deployments, alarge portion of traffic in ATM networks is expected to be CBR voice, video and audio. As timeevolves, designers will have a better understanding of the dynamics of VBR traffic and be able todesign efficient techniques to manage VBR traffic in the network, thereby achieving high resourceutilisation.

2.3.2 Variable Bit Rate (VBR) Service Category

The traffic generated by a typical source, in general, either alternates between the active and silentperiods and/or has a varying bit rate generated continuously. Furthermore, the peak-to-average bitrate (burstiness) of a VBR source is often much greater than one. Presenting VBR traffic to thenetwork as CBR traffic means buffering, or rather artificially controlling its bit generation rate,which has the drawback of underutilization of network resources and QoS degradation. Althoughdoing so simplifies the network management task, it is more natural to provide VBR service toVBR sources and thereby provide a better service and a framework to achieve higher resourceutilisation. ATM networks offer this opportunity, thus the limitations of working at CBRdisappears. VBR connections are characterised in terms of PCR, Sustainable Cell Rate (SCR) andIntrinsic Burst Tolerance (IBT).

The VBR service category is usually divided into two categories namely: real-time Variable BitRate (rt-VBR) service category and non-real-time Variable Bit Rate (nrt-VBR) ServiceCategory.

The rt-VBR service category is intended for rt-applications which require tight constrained delayand delay variation. The nrt-VBR Service Category on the other hand is intended for nrt-applications and no delay bounds are associated with this service category.

2.3.3 Available Bit Rate (ABR) Service Category

Many applications, mainly handling data transfer, have the ability to reduce their sending rate ifthe network requires them to do so. Likewise, they may wish to increase their sending rate if thereis extra bandwidth available within the network. This kind of applications, which do not requirebounds on delay and delay variation are supported by the ABR service category.

A rate-based flow control was specified [22] which supports several types of feedback to controlsource rate in response to changing ATM layer transfer characteristics. This feedback is conveyedto the source through specific control cells called Resource Management (RM) cells. It is expectedthat an end-system that adapts its traffic in accordance with the feedback will experience a low cellloss ratio and obtain a fair share of the available bandwidth according to a network specificallocation policy.

On the establishment of an ABR connection, the end system specifies both a maximum and

16

minimum required bandwidth. These are called Peak Cell Rate (PCR) and Minimum Cell Rate(MCR), respectively. The bandwidth available from the network may vary, but is guaranteed not tobecome less than the MCR.

2.3.4 Unspecified Bit Rate (UBR) Service Category

The UBR service category is intended for nrt-applications like traditional computercommunication applications, such as file transfer and e-mail.

UBR service does not specify traffic related service guarantees. No numerical commitments aremade with respect to the CLR experienced by a UBR connection, or as to the Cell Transfer Delay(CTD) experienced by cells on the connection. Congestion control for UBR may be performed at ahigher layer on an end-to-end basis. The UBR service is indicated by use of the best effortindicator in the ATM user cell rate information element. Even if the PCR is not enforced it is stillrecommended to have the PCR negotiated, so that the source can discover the bandwidth limitationof the connection.

Recently there have been proposals to guarantee a minimum bandwidth to UBR sources. Thisservice class has been proposed to be able to support applications like TCP/IP which are losssensitive and has been called UBR+ or Guaranteed Frame Rate (GFR) [35]. The UBR+ serviceclass is similar to ABR without feedback control.

This Section has defined the ATM service categories. Table 2.1 provides a list of ATM QoS andtraffic parameters and identifies whether and how these are supported for each service category.Then Table 2.2 provide the guaranteed network performance objectives of the network for aspecific traffic class as recommended by the ITU-T Rec.I.356.

2.3.5 Guaranteed Frame Rate (GFR) Service Category

The Guaranteed Frame Rate (GFR) service is UBR with some level of service guarantees. GFRrequires minimal interactions between users and ATM networks, but the simplicity of the servicespecification for users does come at a cost in terms of requirements imposed on the network inorder to efficiently support GFR. However the cost of these requirements is far outweighed by thepotential benefits of making ATM technology more attractive to a broad range of users (inparticular Internet users).

Source Parameters CBR rt-Vt-VBR UBR ABRPCR and CDVT specified specified specified

SCR, MBS, CDVT n/a specified n/a n/aMCR n/a n/a specifiedCDV specified specified unspecified unspecified unspecified

Maximum CTD specified specified unspecified unspecified unspecifiedCLR specified unspecified specified

Table 2.1 : List of ATM Service Traffic Categories Parameters

17

CTD 2-pt. CDV CLR CER CMR SECBRDefault

Objectives

no default no default no default 410-7 1/day 10-4

Class 1

(stringent)

400 msec 3 msec 310-7 default default default

Class 2

(tolerant)

U U 10-5 default default default

Table 2.2 : Provisional QoS Network Performance Objectives

U means unbounded. When the objective of a parameter is specified as being U performancewith respect to the parameter may, at times, be arbitrarily poor.

2.4 Statistical Behaviour of Traffic Sources

2.4.1 Voice

The statistics of a single voice source are composed of two phases and they normally depend onthe technique of voice coding that is being used. The two periods are the active period and thesilent period.

The POTS (Plain Old Telephony Service) has been using a fixed bandwidth digital channel at64kbit/s. Modulation techniques such as adaptive differential pulse code modulation can be used tocompress voice information to a constant bit rate with lower bandwidth requirements.

CBR voice in ATM networks is transmitted with AAL type 1 using the pulse code modulationtechnique. Recommendation G.711 [24] specifies 64 kbit/s CBR voice.

When voice signals are coded with a variable bit rate an active period of a voice sourcecorresponds to a talk spurt, whereas a silent period corresponds to speech silence duration. Thesilent periods constitutes 60-65% of the transmission time of voice calls in each direction. Morespecifically, the average active and silent periods are measured to be respectively equal to 352 msand 650 ms [25]. Furthermore, in a normal conversation the active period fits the exponentialdistribution reasonably well while the duration of the silent periods is less well approximated bythe exponential distribution [26]. Nevertheless, the most frequently used models of voice sourcesin the literature assume that the duration of both active and silent periods are exponentiallydistributed.

A single voice source can be modelled by an Interrupted Poisson Process (IPP) or by the on-offmodel. Multiplexed voice sources are best modelled by a Markov Modulated Poisson Process(MMPP).

2.4.2 Video

A promising service of ATM networks is video communication. It can be divided into still pictureand motion picture video traffic. The investigation of video statistics started in the 1970s, but stilllittle is known about the statistics for the arrival process of cells containing video informationcoded at high bit rates. Video is quite different than voice or data in that its bit streams exhibitvarious types of correlations between consecutive frames. Video images have the followingstatistical components (which are dependent on the type of codec):

18

Line Correlation: occurs when data at one part of the image is highly correlated with data on thesame part of the next line (spatial correlation).

Frame Correlation: data at one part of the image is highly correlated with data on the same partof the next image (temporal correlation).

Scene Correlation: occurs because sequences of scenes may, to a greater or less extent, becoincidentally correlated with each other.

White Noise: is a memoryless process and is uncorrelated.

Non-frame buffered video codecs have all four of the correlations, whilst frame buffered videocodecs (frames all always buffered before being sent) only have scene and white noise correlation[27]. Scene correlations can be reduced by multi-frame buffering.

Due to the various correlations that video traffic exhibits it is inadequate just to measure theburstiness of video traffic. The following list summarises some desirable qualities for newmeasures:

The measure should not yield just statistical values, but values that capture the characteristics ofthe rate variation over time.

The measures must be capable of evaluating the statistical multiplexing effect.

The measures should allow easy modelling of video information sources.

The following measures have been proposed [28] to fulfil these kinds of conditions:

Bit Rate Distribution

The distribution and the probability density distribution of the encoded bit rate evaluated in singleframe units. Along with the average bit rate and the variance, they are quite adequate forapproximating the required capacity.

Autocorrelation Function

The autocorrelation function is a convenient measure for expressing the nature of temporalvariations.

Coefficient of Variation

In order to express such phenomena as the signal delays that arise when a signal is buffered, thecoefficient of variation is used, as a measure to investigate the multiplexing characteristics whenvariable-rate signals are statistically multiplexed.

Distribution of Scene Duration

The probability density distribution of intervals between scene changes.

Various models have been proposed to model video sources. In applications with uniform-activity-level scenes, the change in the information content of consecutive frames is notsignificant. A typical application of this form is a video telephone where the screen shows a persontalking. In general, correlations in video services with uniform activity levels last for shortduration and decay exponentially with time. A first-order Autoregressive (AR) model is proposedin [29]. Another continuous-state AR model which is found to be quit accurate compared withactual measurements, is proposed in [30]. It has however to be noted that these models are notconvenient for queuing analysis, but mostly used in simulation studies. In order to evaluate regionsof extremely low probability (like cell loss), Markov models can be used.

19

The observation that intrascene bit-rate variations are smooth and that their sum should not exhibitsudden jumps was used by [30], to model the video source as a continuous time, discrete stateMarkov model. This is a type of birth-death Markov model, and only transitions to adjacent statesare possible.

In applications with nonuniform activity-level scenes like motion video, frames of high-activityscenes and scene changes contain large amounts of data followed by frames that contain less data.In addition to the short-term fast decaying correlations (temporal correlations) of uniform activityscenes, there is a long-term slow decaying correlation in the amount of information generated perframe, that occurs at times of scene changes. The Autoregressive Moving Average (ARMA) modelis proposed [31] to take into account the two types of correlation that occur in nonuniform activity-level scenes. The ARMA arrival processes are used in Monte Carlo simulations to estimate theprobability-distribution function of the queuing delay and the mean and variance of theinterdeparture time seen by the arriving cell. However, these models cannot be used in thenumerical and analytical analysis of queues.

A two-dimensional, continuous time Markov model which is shown in Figure 2.6 [32], is ageneralisation of the model developed in [30] for uniform activity scenes. In two dimensions, it isnow possible to model the bit-rate fluctuations in consecutive frames to include jumps to thehigher or lower bit rates, thereby modelling the correlation at scene changes.

The Markov Modulated Poisson Process (MMPP) can be used to model the cell arrival processfrom video sources (see Figure 2.8). The interscene transition are given by a Markov chain. Thismodel views bit-rate variations as changes in the number of packet arrivals. Furthermore, for easeof analysis, all distributions (the scene change interval and state persistence-time distribution) areassumed to be exponential distributions.

The MMPP can also be used when N independent video sources are multiplexed. Since the scenechange interval distribution of the various sources are assumed to be exponential distributions, thescene change interval distribution of the multiplexed model will be an exponential distributionwith an average scene change interval of 1/N of that of a single video source.

2.4.3 Data

The term data is used for any application that uses coded text, that is, any application that is notvoice, audio, video or still image. Despite the fact that data networks have been operational for anumber of decades, traffic characteristics of some data sources are not well understood.

The main difficulty arises due to the fact that there is no typical data connection. Large amounts ofdata are transmitted in a file transfer on a rather continuous basis during the duration of theconnection, whereas only a few hundred bytes are generated by an e-mail.

Furthermore, data connections are not generally established between two users, but betweengroups of users, as in the case of Local Area Network (LAN) interconnection. Although the datacell arrival process in ATM networks has not yet been identified, actual data packet arrivalprocesses have been investigated.

It is well known that generation of data from a single data source is well represented by a Poissonarrival process (continues time) or by a geometric inter-arrival process (discrete time). Wheninformation loss occurs, these kinds of services use retransmission as a way of recoveringinformation. The retransmission of the complete data frame is executed every time there is cellloss.

Interactive Data Transmission

A single packet is generated at each time. This could be either a fixed length or a variable lengthpacket. The length of the packet is represented by a certain distribution of fixed mean.

20

This traffic is of bursty nature, relatively short in length, and requires relatively small delay intransmission. Delay variance is not a major problem, but error free transmission is an importantrequirement.

Examples of such traffic are transaction/credit card verification, hotel/airline reservation, WWWaccess and various short message transmissions.

Bulk Data Transmission

The nature of the traffic is similar to the earlier case, but now messages consist of a number ofpackets. This is a batch arrivals case and the arrivals of the packets that make up the message arenot independent. Since ATM networks have a fixed cell size, it may happen that a data packet ofeither variable or fixed size, is fragmented into several cells.

The performance requirements are similar to the previous case, but a slightly higher average delaymight be acceptable. Examples for bulk data transmission are file transfer, database informationacquisition etc.

Candidates to model data sources are the two state MMPP, also called Switched Poisson Process(SPP), including the Interrupted Poisson Process (IPP) and the Geometrically ModulatedDeterministic Process (GMDP), including the on-off model.

2.4.4 Multimedia

The term multimedia is used to refer to the representation, storage, retrieval, transmission ofmultiple media, such as text, voice, graphics, image, audio and video.

Multimedia applications constitute a significant future market. Examples include teleconferencing,entertainment video, medical imaging, distance education, telemarketing and advertising. Each ofthese applications consists of two or more information types, which are listed above. It has to benoted that a strong correlation between successive cell arrivals is characteristic of manymultimedia traffic sources.

Traffic models of information types (i.e. video, voice, data), which are put together in multimediaservices are presented throughout this section. The extension of these models to characterise theintegrated environment of a multimedia service is an important task and is currently underextensive study [33].

Despite this, the MMPP is widely used to model superposed traffic of different information types,and could therefore be used to model multimedia traffic.

2.5 Influences on traffic characteristics

The fundamental difficulty in identifying traffic characteristics is the interdependence of the trafficflow on the network itself.

Care must be taken in traffic modelling since every layer in the OSI reference model (Figure 2.1)is a function of higher and lower layer activity and is influenced by the network behaviour orhuman interaction with the application layer. For example traffic characteristics of call duration,bandwidth, burstiness, burst duration, peak and sustained packet rate will vary for the same genericapplication (e.g. video) with the quality of service that the customer has chosen.

21

Application

Presentation

Session

Transport

Network

Data Link

Physical

Figure 2.1 : Influences on different protocol layers

Video applications, for example, already appear in a vast array of flavours based on proprietaryand international standards. The traffic characteristics and service data will depend on humaninteraction, i.e. whether motion is active or passive, and technical preferences such as theresolution, colour or greyscale etc.

The video and audio codec employed impacts the traffic characteristics dependant on what accessnetwork the codec has been optimised for e.g. ISDN, POTS, ATM, LAN, mobile etc. Tocomplicate the issue further new standards on codec technology are continually emerging anddeveloping.

Protocols at each layer of the OSI reference model introduce variation in flows e.g.

At the session layer, ubiquitous protocols such as HTTP 1.0 which dominates the data trafficflows on the Internet are likely to have been superseded by a more recent version, HTTP 1.1.Mobile specific protocols such as Uplink and languages such as WML (Wireless ApplicationProtocol Mark-up Language) will alter the format and method of data transfer.

Transmission control protocols e.g. TCP are maturing and protocol extensions are being addedthat will dramatically alter the measured packet flow. Experiments have shown [36] that theTCP congestion control algorithm had more control over the loss rate in the shaping bufferthan the service rate of the buffer. TCP responds to corruption as if there is loss of informationbits or congestion in the network and responds accordingly. Successive lost segments triggerscongestion avoidance which continually resets the slow start procedure which leads to aninefficient use of resources over a satellite link with significant propagation delay.Alternatively any loss under steady-state operation is interpreted as congestion and congestioncontrol response is invoked. In either case the steady state traffic characteristics will bemodified.

The current Internet Protocol standard, which underlies the operation of the Internet willpossibly be superseded by IPv6 (or later) and other network layers such as IPX and SNA mustbe catered for. The relative dominance of these network protocols is unknown at this time.

Traffic shaping at the network edge would alter the characteristics of the traffic entering andleaving the network e.g. through the addition of delay, smoothing of burstiness for non-real timetraffic and by cell prioritisation.

queuing

networks

human

influence

network

behaviour

USER

22

2.6 Criteria for the Selection of Source Models

There are, usually, several alternatives to represent a particular traffic source and different levels ofcomplexity. The most important criteria upon which the selection was based is briefly discussed.

First of all, the chosen source model must be accurate with respect to our assumptions. It should beclose to reality and the different parameters should not have only a statistical but also a physicalmeaning. Analytical and simulation results of the model should be compared, if possible, tomeasured performance of real traffic sources to verify the source models accuracy and validity.

From the analytical point of view, tractability (superposition/queuing) is an important feature. Thismeans that the use of the source models in analysis should lead to solutions that lend themselvesfor numerical computation. In many cases, general methods such as iterations to solve systems oflinear equations, aggregation methods to reduce the dimensionality, matrix analytical methods tosolve structured Markov chains etc. could be applied. Often, the exploitation of the specialstructure of the processes involved, may make the model much more suitable for numericalsolutions, without losing its probability interpretation.

Another important feature of source models is its generality and usability. A typical model shouldbe able to represent a large class of sources with similar characteristics. Since most of the sourcemodels are also used in simulations, care should be taken so that it is possible to represent themodel in a simulation environment. It is also important that the model is statistically stable,otherwise there may be significant problems in the overall network simulation model that might bedifficult to detect. The statistical stability is measured over a period of time which is proportionalto the highest level of resolution in time specified by the source model and its number of differentstates.

Finally the number of parameters of the model should be taken into account. This number isusually directly related to the complexity of the description of the model. The aim is to use a modelthat is adequate for our purpose, but uses a limited number of parameters. This makes the analysiseasier and the computation faster.

2.7 Multi-State Markov Source Models

2.7.1 General Modulated Deterministic Process (GMDP) Model

The GMDP is based on a finite state machine having n states. In each state, cells are generatedwith constant interarrival time Ti (therefore it is called a deterministic process), the index i beingthe state. The number of cells which are emitted in state i may have a general discrete distribution.Usually, the GMDP also includes silence states where no cells are generated and the duration ofthese states may also have a general discrete distribution. If the burst and silence durations havean exponential distribution then the model is called a Markov Modulated Deterministic Process(MMDP). The on-off model is a two state MMDP with one silence state.

The state transitions are governed by a transition matrix where each element denotes theprobability of moving from state i to state j once the sojourn period expires.

Usually, voice traffic sources can be characterised when using this model with 2 states, whilstvideo traffic sources may need 3 states to be characterised.

2.7.2 The On-Off Model

One traffic model, which is widely used for the characterisation of ATM sources, is the on-offsource model. This model has been successfully used to realistically model packetised speech, stillpicture and interactive data services. According to the on-off model the ATM cell stream from a

23

single source is modelled as a sequence of alternating burst periods and silence periods. Thismodel is a 2-state Markovian representation of an ATM source as shown in Figure 2.2. Theduration of each burst is exponentially distributed with mean 1/a ms. During such a period ATMcells are emitted with constant interarrival time T ms, where T = 1/PCR. After generation of theATM cells an exponentially distributed silence period with mean value 1/b ms follows. Thiscorresponds to a geometrically distributed number of packets per active period (i.e. burst), withmean value 1/(aT), followed by an exponentially distributed silence period, with mean value 1/b.

SilenceBurst

a

b

1-b

1-a

Figure 2.2 : 2-state Markovian representation of an ATM source

T

Burst period Silence period

mean value 1/bmean value 1/a

Figure 2.3 : The on-off source model

Note that this model is a special case of the General Modulated Deterministic Process (GMDP); itis equivalent to a two state Markov Modulated Deterministic Process (MMDP) with one silentstate.

The on-off traffic source model as shown in Figure 2.3, can be described by the parameters(PCR,m,,ton) as follows:

PCR=1/T, ton=a-1, m=a-1/T(a-1+b-1) and =(a-1+b-1)/a-1

a and b are the transition rates, i.e. a is the inverse of the mean burst duration, b is the inverse ofthe mean silence duration, m is the mean cell rate, is the burstiness and ton is the average burstduration.

2.7.3 The Interrupted Poisson Process (IPP)

The IPP is a Poisson process that is alternatively turned on for an exponentially distributed periodof time (active period where cells are emitted) and turned off for another exponentially distributedperiod of time (silent period), like the on-off model. The difference however is that during theactive periods, the interarrival times of cells are exponentially distributed (i.e. in a Poissonmanner).

The advantage of modelling the arrival process from a single voice source as IPP is that theaggregated arrival process from multiple sources can be modelled by a Markov Modulated PoissonProcess (MMPP). This is due to the fact that the IPP is a special case of an MMPP and thesuperposition of MMPPs is an MMPP.

24

Let r1, r2 and respectively denote the average duration of the active and silent periods, and thecell generation rate during the active period. The simplest way to determine these IPP parametersis to set the mean talkspurt length (a-1=ton) to the mean sojourn time of the cell arrival process r1, toset the mean silence period length [b-1=ton(-1)] to r2 and to set the mean cell generation rate duringa talkspurt -1 to that of the cell arrival process (T). But this parameter matching underestimatesthe performance, therefore the two-moments and peakedness method has been proposed [34]. Letm, c and z be the mean arrival rate, squared coefficient of variation, and peakedness of the cellarrival process. Under the assumption of exponential talkspurt and silence distribution, they aregiven by the following equations:

m =a

(a +b )T

-1

-1 -1

a2c =1-(1-aT 2)

(a+b 2) 2T

z aTabT

m be

mb

a b TT m= +

+

+

11

1/

( )

Then the parameters in an IPP can be expressed as:

= 1m

+(c - 1)(z - 1) m

c +1- 2z

2 -1

2

2z-1+c+1)-c1)(-(zm1)-2(z=r 22

-1

1

2z)-1+c2z](-1+c+1)-1)(z-c[(m)1-1)(z-c2(=r 222

-122

2

2.7.4 Interrupted Bernoulli Process (IBP)

The IBP is a discrete version of the IPP. Time is slotted, with a slot length being equal to the cell inthe medium. A slot is either in an active state or in a silent state. A slot in an active state contains acell with probability a and no cell with probability (1-a), while no cell arrive in a silent state.Given that the slot is in the active state (independent of whether the slot contains a cell or not), thenext slot is also in the active state with probability p and changes to the silent state with probability(1-p). Similarly, given that the slot is in the silent state, the next slot is also in the silent state withprobability q and changes to an active state with probability (1-q). Accordingly, both the activeperiod, Pr(X=x), and the silent period, Pr(Y=y) are geometrically distributed. That is

Pr (X=x) = (1-p) px-1, Pr (Y=y) = (1-q) qy-1 x,y 1

with respective average duration times 1/(1-p) and 1/(1-q).

2.7.5 The Birth-Death Process

A birth-death process is a Markov model where only transitions to adjacent states are generated.The continuous-time birth-death process is used to model voice and video. This process can beviewed as the superposition of N independent homogeneous on-off sources.

This continuous-time process is a fluid approximation model and bit rates can be seen as switchingbetween states with discrete values, and the time spent in each state is given by a random Poisson

25

time sequence.

For voice, instead of modelling the individual information sources, the total bit rate of Nindependent active voice sources is modelled. To model the actual video source, bit rate` ` ` ` `take on only discrete quantized values and are assumed to sampled at random Poisson times in thetime domain.

If p(i,j) is the transition rate from state i to j, the birth and death rates are given [35] by:

p(i,i+1) = (N-i) b i0

where a and b are the transition probabilities.

The equilibrium probability of being in state i is given by the binomial distribution:

PN

ib a bBi

i N i=

= + ( ) / ( )1

The two dimensional, continous time birth-death process shown in Figure 2.4 can be used to modeljumps to higher or lower bit rates in video scene changes. Each dimension of the model can beviewed as the one-dimensional birth-death process discussed above. c and d are the transitionprobabilities to low-activity and high-activity levels respectively. Note that the cell rate in state Nis NAl and is Ah + NAl in state (1,N).

...

...

N-1 N

1,N-1 1,N

0

1,0 1,1

1

N b (N-1)b 2b b

2a N aa (N-1)a

cd

Figure 2.4 : Two-dimensional birth-death process

If a single video source is modelled in this manner, the bit rate when multiple information sourcesare multiplexed can be modelled with the same structure. Thus the multiplexing of N video sourcescan be modelled with a state-transition-rate diagram like that shown in Figure 2.5.

26

...

... 1,N-1 1,N

0

1,0 1,1

1

.

.

.

.

.

...

...

.

...

...

M,0 M,1

NMNM-1

NMb (NM-1)b b

NMa(NM-1)aa 2a

2b

Nd

(N-1)d

d Nc

c

2c

M,NMM,

NM-1

Figure 2.5 : State-transmission-rate diagram for aggregate source model

2.7.6 The Markov Modulated Poisson Process (MMPP)

The MMPP has been extensively used to model various B-ISDN sources, such as voice, video, aswell as characterising superposed traffic. It has the property of capturing both the time-varyingarrival rates and correlations between the interarrival times. Also, if individual traffic sources aremodelled by an MMPP, the superposition of different sources can be described by an MMPP.

State 2State 1

r1

21

r2-1 -1

Figure 2.6 : Two-state MMPP

An MMPP is a doubly stochastic Poisson process. The arrivals occur in a Poisson manner with arate that varies according to a n-state (phase) Markov chain, which is independent of the arrivalprocess.

As the simplest case, Figure 2.6 shows the 2-state MMPP (also called Switched Poisson Process(SPP) ) having Poisson arrival rate j in phase j, j=1,2, which appears alternately exponentiallydistributed sojourn time with mean rj

-1. This is characterised by (R,) where R is the infinitesimalgenerator of the underlying Markov chain and the arrival rate matrix, defined by:

Rr r

r r=

=

1 1

2 2

1

2

0

0

The n-state MMPP is similarly characterised by (R,) with each matrix of n x n size.

In special cases, the MMPP becomes a renewal process, which is characterised by statisticallyindependent and identically distributed interarrival times.

If 1=2= , the MMPP reduces to a poisson process with rate . If 2=0, it is called an Interrupted

27

Poisson Process (IPP).

Superposition of MMPPs

The superposition of MMPPs is also an MMPP. Therefore it can be used to model superposedheterogeneous traffic. Consider N MMPP models, each with parameters Ri and i. Then, thetransition rate matrix R and the arrival rate matrix of the superposed process are:

R = R R ... R = ...1 2 N 1 2 N

where denotes the

13082710 cost 253 service efficient network interconnection via satellites

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