New Institutionen för systemteknik560891/FULLTEXT01.pdf · 2012. 10. 16. · Ericsson AB Examinator: Danyo Danev isy, Linköping University Linköping, 8 juni 2012. Avdelning, Institution

Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

WCDMA Cell Load Control in a High-speed TrainScenario

Development of Proactive Load Control Strategies

Examensarbete utfört i Communication Systemsvid Tekniska högskolan vid Linköpings universitet

av

Raoul Joshi and Per Sundström

LiTH-ISY-EX--12/4614--SE

Linköping 2012

Department of Electrical Engineering Linköpings tekniska högskolaLinköpings universitet Linköpings universitetSE-581 83 Linköping, Sweden 581 83 Linköping

WCDMA Cell Load Control in a High-speed TrainScenario

Development of Proactive Load Control Strategies

Examensarbete utfört i Communication Systemsvid Tekniska högskolan vid Linköpings universitet

av



Handledare: Mirsad Čirkićisy, Linköping University

Raimundas GaigalasEricsson AB

Examinator: Danyo Danevisy, Linköping University

Linköping, 8 juni 2012

Avdelning, InstitutionDivision, Department

Division of Communication SystemsDepartment of Electrical EngineeringSE-581 83 Linköping

DatumDate

2012-06-08

SpråkLanguage

� Svenska/Swedish

� Engelska/English

�

�

RapporttypReport category

� Licentiatavhandling

� Examensarbete

� C-uppsats

� D-uppsats

� Övrig rapport

�

�

URL för elektronisk version

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84635

ISBN

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ISRN


Serietitel och serienummerTitle of series, numbering

ISSN

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TitelTitle

Belastningsreglering av WCDMA celler i ett tågscenario

WCDMA Cell Load Control in a High-speed Train Scenario

FörfattareAuthor


SammanfattningAbstract

Load control design is one of the major cornerstones of radio resource management in today’sUMTS networks. A WCDMA cell’s ability to utilize available spectrum efficiently, maintainsystem stability and deliver minimum quality of service (QoS) requirements to in-cell usersbuilds on the algorithms employed to manage the load. Admission control (AC) and conges-tion control (CC) are the two foremost techniques used for regulating the load, and differingenvironments will place varying requirements on the AC and CC schemes to optimize theQoS for the entire radio network. This thesis studies a real-life situation where cells are putunder strenuous conditions, investigates the degrading effects a high-speed train has on thecell’s ability to maintain acceptable levels of QoS, and proposes methods for mitigating theseeffects.

The scenario is studied with regard to voice traffic where the limiting radio resource is down-link power. CC schemes that take levels of fairness into account between on-board train usersand outdoor users are proposed and evaluated through simulation. Methods to anticipato-rily adapt radio resource management (RRM) in a cell to prepare for a train is proposed andevaluated through simulation. A method to detect a high-speed train in a cell, and the userson it, is outlined and motivated but not simulated.

Simulation results are promising but not conclusive. The suggested CC schemes show asurprising tendency towards an increase in congestion avoidance performance. ProactiveRRM shows a significant increase in QoS for on-board users. No negative effects to users inthe macro environment is noticed, with regard to the studied metrics.

NyckelordKeywords WCDMA, High-speed Train, Congestion Control, Admission Control, Load Control, Cell

Capacity, Cell Resource Allocation, Train Detection, Quality of Service, Dropping Fairness

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84635

Abstract

Load control design is one of the major cornerstones of radio resource manage-ment in today’s UMTS networks. A WCDMA cell’s ability to utilize availablespectrum efficiently, maintain system stability and deliver minimum quality ofservice (QoS) requirements to in-cell users builds on the algorithms employed tomanage the load. Admission control (AC) and congestion control (CC) are the twoforemost techniques used for regulating the load, and differing environments willplace varying requirements on the AC and CC schemes to optimize the QoS forthe entire radio network. This thesis studies a real-life situation where cells areput under strenuous conditions, investigates the degrading effects a high-speedtrain has on the cell’s ability to maintain acceptable levels of QoS, and proposesmethods for mitigating these effects.

The scenario is studied with regard to voice traffic where the limiting radio re-source is downlink power. CC schemes that take levels of fairness into accountbetween on-board train users and outdoor users are proposed and evaluatedthrough simulation. Methods to anticipatorily adapt radio resource management(RRM) in a cell to prepare for a train is proposed and evaluated through simu-lation. A method to detect a high-speed train in a cell, and the users on it, isoutlined and motivated but not simulated.

Simulation results are promising but not conclusive. The suggested CC schemesshow a surprising tendency towards an increase in congestion avoidance perfor-mance. Proactive RRM shows a significant increase in QoS for on-board users.No negative effects to users in the macro environment is noticed, with regard tothe studied metrics.

iii

Acknowledgments

The time spent at WCDMA Systems at Ericsson during the first half of 2012 has,for the both of us, been a fantastic end to our studies in electrical engineering.Although we would like to acknowledge everyone who in one way or another con-tributed to our stay, whether directly related to our work, playing floorball withus on Mondays or by simply being a friendly face on rainy days, the constraintsof this page restricts us from listing them all.

The simulator team, together with whom we have worked, consisting of MagnusPersson, Erik Geijer Lundin, Jennifer Chen, Karin Lagergren, Ed Kirwan, andDejan Miljkovic, deserves special recognition. We hope we have left behind ben-eficial contributions to your area of work.

We would also like to thank Benny Lennartson for the collaboration and insightsregarding intellectual property processes and law. Moreover, this particular taskas thesis work would not have been possible without Stephen Craig, Mikael Russ-berg, and Maria Gabriela Landazuri Saenz.

Special thanks are also warranted to our university examiner and supervisor,Danyo Danev and Mirsad Čirkić, for indulging the special circumstances thatthis thesis work has entailed.

In particular, we are sincerely indebted to our supervisor at Ericsson, RaimundasGaigalas, for the countless number of hours spent in discussions with us, guidingus, listening to us, and reflecting on topics together with us.

Above all, we would like to express our deepest gratitude to Rajender and RenuJoshi for the unconditional support provided to us outside of all thesis-relatedwork.

Thank you.

Linköping, June 2012Raoul Joshi och Per Sundström

v

Contents

Notation xi

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

I Theoretical Background and Simulation Modelling

2 Background and Related Works 72.1 High-speed Trains in Radio Environments . . . . . . . . . . . . . . 7

2.1.1 Service Maintenance . . . . . . . . . . . . . . . . . . . . . . 72.1.2 Propagation Phenomena . . . . . . . . . . . . . . . . . . . . 82.1.3 Radio Access Networks . . . . . . . . . . . . . . . . . . . . . 10

2.2 Trains in UMTS Networks . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2.2 Radio Resource Management . . . . . . . . . . . . . . . . . 12

2.3 QoS for Mobile Subscribers . . . . . . . . . . . . . . . . . . . . . . . 172.3.1 QoS Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3.2 Radio Link Bearers . . . . . . . . . . . . . . . . . . . . . . . 17

3 Hypotheses and Limitations 193.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Network Modelling 234.1 Table of Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2 Scenario Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 234.3 Network Environment . . . . . . . . . . . . . . . . . . . . . . . . . 254.4 Train Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

vii

viii CONTENTS

5 Network Capacity Determination 275.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.2 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6 Impacts of a Train on a Congested Cell 316.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.2 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

II Studies

7 Congestion Control in Train Scenarios 397.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397.2 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.3 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

8 Proactive Admission Control for an Inbound Train 458.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458.2 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468.3 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

9 Detection of a High-speed Train 539.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539.2 Characteristic Velocity Profile . . . . . . . . . . . . . . . . . . . . . 53

III Final Remarks and Supplementary Material

10 Final Remarks 5710.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5710.2 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . 58

A Appendix 59A.1 Signal Propagation and Networks . . . . . . . . . . . . . . . . . . . 59

A.1.1 Signal Propagation . . . . . . . . . . . . . . . . . . . . . . . 59A.1.2 Radio links . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60A.1.3 Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . 60A.1.4 WCDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63A.1.5 UMTS Networks . . . . . . . . . . . . . . . . . . . . . . . . . 65

CONTENTS ix

A.1.6 Quality of Service . . . . . . . . . . . . . . . . . . . . . . . . 66A.2 Capacity Determination Data . . . . . . . . . . . . . . . . . . . . . 67

Bibliography 73

xii Notation

Notation

Acronyms

Acronym Definition

3GPP 3rd Generation Partnership ProjectAC Admission Control

AMR Adaptive Multirate (speech codec)BLER Block Error Rate

DS-CDMA Direct-Spread Code Division Multiple AccessCC Congestion ControlCN Core NetworkCu USIM - ME interface

DCH Dedicated Channel (transport channel)DL Downlink

FDD Frequency Division DuplexFDMA Frequency Division Multiple Access

FER Frame Error RatioGGSN Gateway GPRS Support NodeGMSC Gateway MSCGPRS General Packet Radio SystemGSM Global System for Mobile CommunicationsHLR Home Location Register

HSDPA High-speed Downlink Packet AccessHSPA High Speed Packet Access

IP Internet ProtocolIS-95 cdmaOne (a 2nd system in Americas and Korea)ISDN Integrated Services Digital NetworkITU International Telecommunications UnionIu RNC - CN interface

Iub RBS - RNC interfaceIur RNC - RNC interfaceLC Load ControlME Mobile Equipment

MSC Mobile Switching CenterMT Mobile Terminal

Notation xiii

Acronyms

Acronym Definition

NBAP Node B Application PartNode B see RBSOVSF Orthogonal Variable Spreading FactorPER Packed Encoding Rules

PLMN Public Land Mobile NetworkPSTN Public Switched Telephone NetworkQoS Quality of ServiceRAB Radio Access BearerRAN Radio Access NetworkRBS Radio Base StationRNC Radio Network ControllerRNS Radio Network Sub-systemRRC Radio Resource ControlRRM Radio Resource ManagementRSSI Received Signal Strength IndicatorSGSN Serving GPRS Support NodeSHO Soft HandoverSIR Signal-to-interference RatioSMS Short Message ServiceSNR Signal-to-noise RatioTDD Time Division Duplex

TDMA Time Division Multiple AccessTE Terminal Equipment

TLA Three-letter AcronymUE User EquipmentUL Uplink

UMTS Universal Mobile Telecommunication ServicesUSIM UMTS Subscriber Identity Module

UTRAN UMTS Terrestrial Radio Access NetworkUu ME - RBS interface

VoIP Voice over IPWCDMA Wideband Code Division Multiple Access

1Introduction

In this introductory section we present the purpose of this thesis, through a back-ground of the topic at hand, and the pertinent problem description.

1.1 Background

"Yes I’m on the train now. Somewhere between Norrköping and... hello? hello?"

Wideband code division multiple access (WCDMA) and high speed packet access(HSPA) are the mobile communication techniques behind today’s third genera-tion networks, and are deployed world-wide in a multitude of differing environ-ments. With the growing demand for mobile broadband services, the number ofusers steadily increases, resulting in higher performance demands on the RadioAccess Network (RAN), giving rise to a series of issues that need to be addressed.

A RAN can be characterized as dynamic on different levels. On the link level,multipath fading and shadowing properties vary instantaneously and substan-tially depending not only on the distance from the radio base station (RBS) andsurrounding landscape but also on user equipment (UE) orientation, velocity andcurrent weather conditions - resulting in dynamic radio linkage properties inboth uplink (UL) and downlink (DL). On the load level, users enter cells, exitcells, and switch between capacity-varying services seamlessly - resulting in dy-namic load levels for the RBS to manage using various strategies such as admis-sion control (AC) and congestion control (CC).

This thesis will delve into an extreme case of such a dynamic scenario. Considera bulk of mobile voice callers entering a cell at high speed, requiring access to themobile network simultaneously and soon thereafter exiting the cell. During this

1

2 1 Introduction

time, the RAN must adequately admit users while not jeopardising overall net-work performance. The radio network controller (RNC) must co-ordinate hand-over requests from neighbouring cells, continue to serve existing in-cell users,and perform hand-over requests for active users exiting the cell.

Simultaneously, acceptable quality of service (QoS) must be maintained for asmany served users as possible through various prioritization and load controlschemes; however, this may not always be possible due to the inability of thesystem to prioritize amongst a bulk of approaching users and regulate load in-stantaneously.

These types of complex and dynamic environments put extensive demand on thenetwork’s radio resource management (RRM) where inadequate AC and CC canlead to dropped connections for all users and network collapse due to overload.

1.2 Purpose

The thesis work study is carried out at the WCDMA Systems Department atSwedish telecommunications company Ericsson AB in Stockholm. The goal isto develop and evaluate a feasible load control strategy for a multi-cell UMTSnetwork in a scenario where a large group of mobile users enters and exits a cellat high speed. The proposed strategy is to be developed, implemented, and eval-uated using a simulator made available by Ericsson AB.

1.3 Problem Description

The problem addressed in this thesis deals with a high-speed train carrying asizeable bulk of mobile users subscribed to an active speech service. The trainenters cells that are heavily loaded in terms of cell DL power to users in the cells;that is, there is no, or a shortage of, excess power to handle the incoming train.The sudden entrance of a train adds substantial interference to the network, andthe RNC may be unable to coordinate allocation of necessary resources in timefor satisfactory handover of all on-board users.

The above description breaks down into addressing the issue of admission controland congestion control in the network. They are used to regulate the load andwill, in this thesis, be referred to together as load control (LC).

Given the circumstances a train in a congested cell entails, LC schemes are nec-essary that take into account some level of fairness between on-board users andoutside users. From one vantage point, and with certain reservations, on-boardusers became part of a cell’s load the very instant they engaged in their phoneservice regardless of the distance from the actual cell since the fixed track of arailway guaranteed passage through the cell in question.

From the above reasoning, if the approach of a train into a certain cell is knownin advance, the knowledge of the imminent load on the cell’s resources should

1.4 Thesis Outline 3

be taken into account by the RRM algorithms, in order to have the required re-sources available when the train enters the cell. A natural inference is how todetect an actual train moving through a cell and the users on-board. How canthis information be signalled in the network as to prepare a cell further away?

1.4 Thesis Outline

In chapter 2, the background to high-speed trains in cellular networks is intro-duced together with insight into previous works that have been carried out withinthe field.

In chapter 3, the hypotheses and limitations to the study are presented, buildingon the background and related works. The hypotheses will form the basis for thepurpose of the simulations run and conclusions drawn.

In chapters 5 and 6, the simulation environment is presented and described, andthe capacity of the environment is determined in order to ensure a congested statebefore a train’s effects on a cell are examined. A train is thereafter run throughthe congested environment, and the effects recorded and analysed to determinewhether QoS and fairness metrics can be improved, and if so, by how much.

In chapter 7, congestion control dropping schemes are analysed, introducing met-rics for fairness between outside and on-board users and the impacts fairness hason the network utility. Dropping schemes that are algorithmically biased to sys-tematically single out certain types of users is in this thesis considered unfair.

Thereafter, in chapter 8, attempts are made to avoid cell congestion by reservingresources for an approaching train through the use of proactive admission con-trol. Trains carrying a certain number of users will be reported to cells further upthe cell network and capacity reservations will be performed. Reservation of suchresources warrants disabling of complex HO and AC mechanisms and train userscan be readily accepted into the cell. In the case of some train users still causingcell congestion, suggested fair dropping schemes from chapter 7 will kick in.

The chapter 9 will deal with the assumption in previous chapters that an oncom-ing train and its users are known in advance. Investigations into whether Dopplervelocity measurements of in-cell users can be used to detect a train in motion andthe users on-board through a network will take place.

The final chapter 10 will provide a comprehensive conclusion for the study in itsentirety together with a final discussion of these conclusions.

1.5 Contribution

The contribution of this thesis is a pragmatic and systematic end-to-end approachfor high-speed mass transit vehicles such as trains. The pragmatic approach en-tails no additional hardware solutions but merely optimizing performance based

4 1 Introduction

on current technology and signalling algorithms. The end-to-end approach en-tails that this thesis provides insights from initial train detection to the subse-quent actions, and restoration to default conditions following the train’s depar-ture.

An additional novel contribution in this thesis is the introduction of fairness be-tween user types in congestion schemes and the balancing of such fairness againstnetwork utility.

Part I

Theoretical Background andSimulation Modelling

2Background and Related Works

The aim of the background chapter is to provide the theoretical aspects of a high-speed train in a WCDMA radio environment. Special focus is put on the UMTSnetwork’s radio resource management, and how mobile devices are handled byit.

2.1 High-speed Trains in Radio Environments

Trains are interconnected series of vehicles that move along a fixed railway, car-rying passengers in train cars. Although they are commonly perceived to passthrough picturesque landscapes and urban milieus, a less thought of aspect ofthe environments that trains pass through are the dynamic radio environmentsthat subject mobile users on-board to demanding conditions when attempting tomaintain steady and satisfactory service levels.

2.1.1 Service Maintenance

High-speed travel, as defined by the European Union, states that:

The high-speed advanced-technology trains shall be designedin such a way as to guarantee safe, uninterrupted travel:- at a speed of at least 250 km/h on the lines speciallybuilt for high speed, while enabling speeds of over 300 km/hto be reached in appropriate circumstances.

[Union, 1996]

Whereas high-speed train operators battle with providing this uninterrupted ser-vice in terms of train departure and arrival times, neither train operators nor tele-

7

8 2 Background and Related Works

com operators have succeeded in guaranteeing uninterrupted service in termsof mobile services that users engage in while travelling at such speeds [Review,2008, Yglesias, 2008]. Instead, the trend of banning voice calls seems to takeprecedence in various places worldwide - perhaps being fueled by the inabilityto maintain a service as such in the first place [Go, 2011].

Dropped phone calls on trains are prevalent issues for professionals and com-moners alike with the general conception that dropping of phone calls can beattributed to so-called dead spots in the network coverage areas due to hilly ter-rain and temporary weather conditions. This would not, and does not, explainphenomena such as stationary users in the vicinity of a railway station havingtheir calls dropped due to an approaching train or dropped calls in urban areaswhere dead spots are next to nonexistent.

There are three characteristics that distinguish a train from other types of cellu-lar activity that can, and should, be exploited for finding suitable control mecha-nisms. For one, speeds are substantially greater than ordinary cell traffic, rangingfrom 100 up to 400 km/h. Secondly, moving patterns are strongly predictable asrailway track deployments are fixed. Finally, users on the train are clustered to-gether along a relatively straight line between over 200 to 400 m, depending onthe length of the train [Tang et al., 2011].

2.1.2 Propagation Phenomena

Figure 2.1: A high-speed train in a radio environment. Train movements arefixed to railway deployment.

2.1 High-speed Trains in Radio Environments 9

One of the special propagation phenomena that arises from high-speed travel is avast increase in the Doppler spread, Ds, of the channel, driving down the channel’scoherence time, Tc, through the relation:

Tc ≈1Ds

(2.1)

Equation 2.1 provides an idea of the effects the Doppler spread channel has on theUE and RBS communication. The train creates a large relative velocity betweenthe transmitting and receiving terminals and the resultant increased spread ofmultipath propagations with independent and random Doppler frequency shiftsresults in a high Doppler spread [Liu et al., 2011]. This would most likely beaccentuated further in an urban scenario than a rural, where paths both to andfrom a train are greater than in flat rural landscapes [Ahlin et al., 2006].

High Ds proportionally lowers Tc, a measure for the time-variation of the chan-nel. The speed of the train thus essentially creates a profound fast-fading channelwhere destructive or constructive interference from the multipath result in chan-nels with very low coherence time. A key issue here for the train case is the in-tercarrier interference (ICI), where the duration of transmitted signals are longerthan the channel’s Tc. Signals outside Tc will thus interfere with subsequent sig-nals, aggravating the decoding process of these signals, and consequently causingincreased signal distortion.

Depending on the service a train user is engaged in, the distortion may take differ-ent forms, and to overcome distortion, higher SNR would be necessary. Deep fadesare thus not uncommon risking a total loss of communication between senderand receiver at times; however, these effects can be mitigated in various ways us-ing the time diversity of the Doppler spread. Essentially this entails manipulat-ing the randomness of Doppler shifts of the independent paths in order to obtainuncorrelated copies of the same signal to strengthen the robustness of the channelagainst deep fades. Liu et al. takes the idea one step further for railway networksby proposing specific antenna architecture along a railway network consistingof sectorized and directional antennas for Doppler mitigation and Doppler diver-sity gains. With such gains, required radio link SNRs for maintaining connectionscould lower the the transmission load on UE and RBS transmit power levels andthereby provide longer periods of soft handover (SHO) where the radio networkcontroller (RNC) can ensure successful handover.

It is also worth noting that high-speed trains come in different types and onekey issue is the ability to dampen propagation attenuation due to the train bodyitself [Gunnarsson, 2005]. The train body loss depends on the signals’ ability topropagate through windows. A way of avoiding further train body attenuation isthrough the use of repeaters installed on the train as in figure 2.2. Repeaters havethe advantages of lowering UE transmit powers (thus preserving battery power)and mitigating train body and Doppler losses to such an extent where sparserRBS deployment would be possible [Gunnarsson, 2005].


Figure 2.2: Repeaters (in red) can be installed on trains to strengthen signalquality

2.1.3 Radio Access Networks

Figure 2.3 illustrates a wireless network design consisting of a series of RBSswith individual coverage areas, adding up to serve a larger area as a whole, calledservice area. A high-speed train that runs through this area with users on-boardwill consistently perform handover actions from one RBS to another, in order toavoid dropping of connections and thus loss of services.

Figure 2.3: Schematic coverage map of a wireless communication system

As illustrated, an RBS’s coverage area will be unique depending on the topologyof the terrain for the RBS in question, while at the same time being heavily de-pendent on propagation conditions and interference from users in neighbouringcells. Some techniques that have been used to battle this is the use of an um-brella cell. An umbrella cell is activated on top of microcells to serve high-speedusers and lowering the number of hand-offs required for high-speed terminalsmoving through the serving area as depicted in figure 2.4. It also fills in the gaps

2.2 Trains in UMTS Networks 11

of possible dead spots between micro cells’ coverage areas [Ioannou et al., 2003].Although umbrella cell solutions are popular, they do not span the entire rail-way network and issues are readily brought about when it comes to HO from oneumbrella cell to the next. Also, for an umbrella cell to be deployed, hardwareinvestment is necessary where umbrella cell RBSs need to be deployed at a greatheight in order to cover a large region - possibly becoming a bit of an eyesore inan urban environment.

Figure 2.4: An umbrella cell on top of other cells that is activated in high-speed environments

Other possible solution have been presented by Gunnarsson who briefly discussesdifferences in the deployment of microcells along the railway track versus dis-tant RBS with directional antennas. Directed RBSs from a distance have largercoverage areas than microcells next to the track, and can thus be deployed moresparsely. Moreover, further weakening the strategy of close-to-track deploymentis accentuated Doppler losses in the DL.

In underground train environments, where radio signals do not propagate freely,leaky coaxial cables are laid along the track in order to guide radio signals frompoint of origin to the location of the train. The leaky property of the cables allowsignals to permeate along the otherwise difficult underground tunnel. However,this type of deployment substantially degrades network performance with highpassenger density and considerable losses are incurred at the terminals of thecables [Zhang, 2005].

2.2 Trains in UMTS Networks

In this section, the UMTS network used for third generation mobile telecommu-nication networks is presented on the level that is relevant for network manage-ment of high-speed trains.


2.2.1 Overview

The universal mobile telecommunications services (UMTS) network can be re-garded from a number of different perspectives, such as logically, functionally,or by which sub-network they belong to. In Figure 2.5 the network is dividedfunctionally.

UMTS essentially covers the entire process, from the hand held user equipment(UE) via the RBSs to the core network (CN), and out to external networks suchas the internet; however, all the radio-related functionality occurs within theUMTS terrestrial radio access network (UTRAN), and in the interface betweenthe mobile equipment (ME) and UTRAN. This thesis thus deals with LC withinthe UTRAN and not elements beyond the RNC. For a complete description of theUMTS network, see the appendix.

Figure 2.5: UMTS high-level system architecture with network elements.Node B is the UMTS specific term for the more general RBS.

External NetworksCNUTRANUE

Cu

Uu

Iub

Iur

Iu

2.2.2 Radio Resource Management

Radio Resource Management (RRM) covers the ensemble of algorithms that dealwith regulating the load of a cell and administering the distribution of resourcesto subscribers as determined by QoS parameters. In a high-speed scenario, timeis of crucial value, as there is little time to regulate overload, and little time fordecision-making in admission and handover policies.

RRM is necessary for efficiently utilizing the air interface and the associated re-sources. Without it, QoS could not be guaranteed, and high capacity could notbe ensured for a maintained coverage area.

Handover

As a train passes through a cell, it will have the serving RBS in its active set,providing the resources necessary for maintaining the active service at QoS re-quirement levels. As coverage areas from neighbouring cells overlap, the trainwill, within a period of time, be located in the intersection of two coverage ar-


eas, thus having two separate cells in its active set. This allows for soft handover(SHO) enabling smooth transmission of services from one cell to another.

It is important to note that trains, or in essence any fast-moving vehicle, will beeligible for SHO during very limited times. This is especially true for high-speedtrains where hard handover occurs to a greater degree and is responsible for amajority of dropped connections due to failure of admittance into the new cell[Tang et al., 2011]. This naturally places high demands on the handover and ACalgorithms.

Tang et al. presents a technique to overcome this in the high-speed scenariothrough the installation of dual antennas on the roofs of trains. It uses the char-acteristic of the length of the train to increase the probability of successful SHOby extending the time the train is in both cells by installing one antenna at thehead of the train, and another at the rear.

Admission Control

When a new radio link is to be set up, AC will estimate the requirements of theestablishing link and examine the impacts a possible establishment will have onthe network’s QoS and coverage area. In a low-loaded cell, where both the air-interface and RBS power consumption is low, a new radio bearer (RB) for theestablishing link will be set up, defining the QoS attributes for the specific link(see section 2.3). If an RB cannot be directly established the admission controllerwill attempt to release resources by, for example, switching down existing usersto lower rates (for more examples see 2.2.2).

In the case of downlink admission control, a new link can be established if it doesnot result in the network using more power than a certain threshold, defined inthe radio network planning.

If

Ptotal−old + ∆Ptotal > Pthreshold (2.2)

then admission is blocked by the admission controller.

∆Ptotal is estimated based on the initial power the user requires, which in turndepends on the distance from the RBS. In essence, the distance from an RBS doesnot need to be specifically determined in order to estimate the power required- rather the distance will be represented in the initial power estimate as deter-mined by the outer loop power control [Holma and Toskala, 2004]. The outerloop power control sets the target for the fast power control needed to providethe required quality of transmission - no worse, no better. This is especially hardin a high-speed train scenario where the outer loop power control will consis-tently update the signal-to-interference ratio (SIR) targets, and the fast powercontrol will be required to keep up. In a review of Doppler techniques with ap-plications to HOs, Tepedelenlioglu and Abdi points out the value of consideringDoppler values in AC policies and initial power estimations for HO. If Dopplerdiversity schemes were used to combat the fast fading channel effects of trains,


the outer loop power control would be able to report lower SIRs, increasing thelikelihood of admittance and thus successful HO.

Power thresholds are in WCDMA service-based, i.e. different types of serviceswill be allowed admittance to a varying degree. Best-effort thresholds are usuallyreserved for services without special priority. In the case of the speech service,a prioritized service in third generation networks, higher thresholds are usuallyused. Admission of HOs are prioritized over admission requests from new callset ups since HOs entail already active on-going services in the RAN. A key met-ric from telecom operators is to keep as many active services alive as possible.Blocking of new callers is thus favourable to blocking of handover requests.

AC schemes based on power levels are quite common and several proposals existin maintaining efficient usage of power resources with dynamic user load levels.Xiao et al. propose a distributed admission control algorithm that maintains thesystem’s power levels at Pareto optimality and uses this for admitting incomingconnections. The algorithm strictly deals with the DL, and iterates for each estab-lishing connection one by one. Also Liu et al. discusses power-based admissionalgorithms for single-incoming connections In this case by establishing an adap-tive call admission control algorithm that accepts a new incoming connection ifthe network has reached a steady-state with all ongoing n connections meetingminimum SIR with (Eb/N0) > γσ (n). The issue with such algorithms, althoughefficient, is that they would have to be run as many times as users on board thetrain, which is not feasible when attempting to simultaneously admit a bulk ofusers at high speed.

A more promising strategy for high-speed trains is suggested by Liu et al., whosuggest a self-learning AC scheme that employs a single-module adaptive criticdesign (ACD) from neural network control architecture. ACDs are defined asschemes that approximate dynamic programming optimal control over time innon-linear environments. The idea is that the admission controller learns fromthe network environment and user behaviour over time to collect experience as abasis for admission policy. This is done by defining a utility function, U , whichuses a cost function, E. E rewards the system for correctly accepting or rejectinga call, and inversely penalizes the system for incorrectly accepting or rejectinga call. The self-learning approach builds on calculating the utility and storingthe state of each action that includes total inference, call type, and call class.Upon connection request, the admission controller will either accept or rejectthe call depending on the future state of the network. For a high-speed trainscenario, this could prove to be an interesting case where it would suffice that theadmission controller know how many callers and of what service types to make asingle decision on bulk admittance or rejection based on the experienced futurestate of the network.

Other, potentially feasible methods, build on predictive AC schemes [Kim et al.,2000, Chin et al., 2006]. One way of using predictability is by gaining knowledgeof when and where a handover request will occur based on user mobility patternsas proposed by Evans and Everitt. By using an aggregate of user mobility history,


probabilistic schemes can be modelled that maps a certain mobility pattern toa specific time and location for a handover request, as illustrated in figure 2.6,and therefore each future user can be reserved a certain bandwidth in advance.These patterns reflect the routines of a users’ habitual lives and therefore suggesta stationary mth order Markov source of events.

Figure 2.6: Resource reservations using predictive AC schemes

HO 36% HO 64%

AC specifically for a train scenario has not been extensively covered in the lit-erature. In fact, the only found sources do not deal with the issue in a UMTSenvironment [Kim et al., 2000, Lee et al., 2011, Lattanzi et al., 2010] except for[Karimi et al., 2012]. Kim et al. provide solutions in a geostationary satellite en-vironment by using terrestrial base stations as gap-fillers for satellite handover toensure continuous transmission. Lee et al. provide a solution for a situation sim-ilar to this study; however, the solution entails the installation of extra on-boardmobile routers for WiMAX and WLAN technologies and solely for internet accessand no speech services.

Congestion Control

Congestion control kicks in when the total amount of resources demanded by ac-tive users exceed a predetermined network capacity limit, as illustrated in figure2.7. Admission control will continuously strive to avoid a state of congestion byblocking new users, but due to the dynamic properties of the network, networkcapacity may at any point drop below current users’ demand or users’ demandmay rise above the network capacity. Regardless of cause, the effect forces CCto resolve the overload by returning the total demand of users to an acceptablelevel.

There are a number of actions for CC to take. Primary measures entail deny-ing downlink power-up requests from the UE, switch down rates for high-speedpacket users, handover to other WCDMA carriers if possible, or, if available,


handover to GSM. A last-resort situation is to drop active users in a controlledfashion. These controlled fashions can for example be based on dropping usersrequiring the highest amount of resources in order to resolve a congested situa-tion quickly and enter the congestion recovery mode. In the congestion recoverymode, previous load control actions such as handover to GSM or rate adaptationscan be reset [Rodrigues et al., 2009].

Figure 2.7: Conceptual illustration of the system’s load dynamics

Normal operation Congestion resolution Congestion recovery Normal operation

Network

capacityUsers'

demandCongestion

CC techniques specifically for high-speed train environments are virtually non-existent, mainly because proposed CC policies do not distinguish between usersof a certain service type and another user of the same service type, travelling athigh speed.

Measurement reporting

Other than bearing and transmitting actual service-related data such as speechframes for speech services, one of the primary functions performed by differentnetwork elements and transmitted across interfaces is that of metric measure-ment and measurement reporting.

These are important to be aware of in this thesis, since there is a lot of informationone would like to know about a train and its on-board users, but not everythingcan be assumed to be known.

Various measurement reporting serve different purposes. For example, the re-ceived signal code power by total received power, EcNo , is used for the handoverdecision-making process. Another example is the signal-to-interference ratio(SIR) for power control purposes.

The RBS signals the RNC with measurements on the total transmission power onits carriers, providing information on the amount of available power resourcesat the base station. These measurements are commonly transmitted as a powerratio in decibels (dBm).

The RNC also receives measurements of the block error rate (BLER) from theRBS. The measurement is supported by the UEs in order to provide feedbackinformation to the RBS for adjusting SIR targets for power control procedures.

2.3 QoS for Mobile Subscribers 17

2.3 QoS for Mobile Subscribers

The type of service that a user subscribes to will entail certain requirements ofthe network, and in a cell with multiple users subscribed to multiple services theaggregated requirements may very well exceed the capability of the WCDMA net-work. It is preferable from a network utilization efficiency perspective to allocatesufficient resources in order to satisfy a user on an individual basis rather thanto let all users equally share resources regardless of service requirements. QoS isthe foremost metric on user satisfaction, and breaks down to a series of quantifi-able requirements in the network plane. From previous descriptions of issues ofhigh-speed train environments, it should be evident that QoS requirements aremore difficult to maintain for high-speed train users than slow-moving users.

2.3.1 QoS Classes

QoS architecture is provided by 3GPP, and is divided into four different trafficclasses. The four traffic classes are the conversational class, streaming class, inter-active class, and background class [3GPP, 2011].

QoS is differentiated into the respective traffic classes primarily based on howdelay-sensitive each class is.

Conversational and Streaming classes serve to carry real-time traffic flows, andtherefore encompass the most delay-sensitive services. Examples of such servicesare traditional telephony speech, and newer applications such as Voice over IP(VoIP) and video telephony for the Conversational class, and video streaming ser-vices such as YouTube®for the Streaming class. The Interactive class compriseservices such as web browsing, database retrieval and general server access, andis less delay-sensitive than the Conversational and Streaming classes. The Back-ground class is the least delay-sensitive class, covering services such as E-mail,MMS, and SMS.

In a high-speed train scenario, load control algorithms are activated to regulatethe cell load so that minimum QoS requirements are met. As presented in sec-tion 2.2.2, congestion control will attempt a series of different schemes priorto dropping an on-going service. Considering a train at high-speed, there willhardly be a sufficient amount of time to perform a series of congestion actionsprior to dropping actions. It is therefore elementary to consider the situation ofonly speech users (and no data users), and what control scheme to employ whendropping such users. Since a user being dropped results in failure of retainingQoS, the user will be labelled as an dissatisfied user. If train users are system-atically dropped from a network in higher proportions than macro users, thedifferences in QoS could be argued as unfair.

2.3.2 Radio Link Bearers

Figure 2.8 depicts the QoS layers involved in the UMTS network.

End-to-end service QoS requirements can be systematically broken down into


mutually exclusive bearers with specific QoS requirements required to deliverend-to-end QoS requirements. Relevant for the thesis are QoS requirements forthe physical radio bearer (RB) service.

Figure 2.8: UMTS QoS Architecture. Physical radio bearer service is of rele-vance to the thesis.

UMTS

TE TEMT UTRAN CN Edge Node

End-to-End Service

CN

Gateway

UMTS Bearer ServiceExternal Bearer

Service

TE/MT Local

Bearer Service

Radio Access Bearer ServiceCN Bearer

Service

Radio Bearer

Service

RAN Access

Bearer Service

Backbone Bearer

Service

Physical Radio

Bearer Service

Physical

Bearer Service

RAN RRM deals with QoS in the MT-UTRAN layer and is thus responsible forQoS for the parameters defined by RBs.

3Hypotheses and Limitations

In this chapter, relevant hypotheses that will be investigated are formulated andexplained. Thereafter, limitations and the approach is outlined and finished offwith the methodology of each part-study in question.

3.1 Hypotheses

The background and review of related works, put into context of the purpose ofthis study, provides some interesting insights. A train’s sudden rush into a cellwill place substantial requirements on the admission controller since, by design,the algorithms are, regardless of scheme, generally designed on a per connectionbasis. In situations with a high user arrival rate, this puts a considerable strainon the hardware of the admission controller, especially in a congested cell whereavailable resources are scarce. Self-learning or predictive algorithms for the ad-mission controller that need not initiate a decision process upon connection re-quest may seem favourable in these cases.

The review also suggests that it is possible that the system is put in a situationsuch that users are admitted, even though inherit dynamics of the environmentultimately might cause the system to have to perform congestion action on al-ready admitted users. In this situation, it is reasonable to believe that a givencongestion control algorithms might introduce a situation where either users onthe train or in the macro environment could be argued as being systematicallydiscriminated against as a group. Therefore, investigation of how different CCschemes effect the fair treatment of these groups is of interest when evaluatinghow well a system handles a situation involving users on a train.

As previously mentioned, denying a user admission upon the setting up of a call

19

20 3 Hypotheses and Limitations

is, from a satisfaction perspective, to be preferred before being forced to use con-gestion resolution actions on the connection in a later stage. It is thus reasonableto pursue a solution where admission control is utilized to avoid the situation ofa train of users entering a cell when it is in a congested state. Using the ideas ofpredictive AC schemes, a cell can prepare its load if it has knowledge of an incom-ing train. In such a case where the cell load has been prepared in advance for anincoming train admission control would not need to be performed upon handoverof train users, but admission could be granted without any ado. This would re-solve the issue of running a bulk of users through extensive control algorithmsand avoid blocking resulting in retention of QoS.

Actual detection of a train has not been covered in the literature earlier. A fac-tor that distinguish a group of train users from its external environment is theapparent fact of them being a bulk of tightly placed users travelling at the same,relatively high, speed in comparison to the outside environment. A feasible wayto thereby detect such an object would be through the study of their radial veloc-ity, which can be theoretically measured by a receiver. If the radial velocity ofthe train users produce a substantial deviation from the over all distribution ofradial velocities of UEs in the cell, then this characteristic could be used to tagthese users as train users, and thereby initiate a cell preparation scheme, grantedone knows the network of cells that a railway track passes through.

3.2 Limitations

The limitations to this thesis are mainly attributed to time constraints. This en-tails that the research exclusively deals with QoS in the downlink. Uplink isnaturally also of interest from a QoS point-of-view; however, due to some signif-icant differences between the DL and UL, such as transmission power capacitiesand scheduling schemes, it warrants a separate study. It should be observed thatthe method of study is similar in both cases.

Quality of service as a whole provides requirements for the entire end-to-endUMTS bearer service from originating UE to destination UE. Since this entirerange is not relevant for the study, solely the QoS pertaining to radio access bearerservice shall be addressed.

Another limitation that bears mentioning is that the study deals with speechusers exclusively. Two major consequences of this is that the strategy that will bedeveloped is solely applicable for single-service network and not a multi-servicenetwork which is preferable. The other consequence is that rate adaptation (RA)will not be applicable during load control. The reason for this, rather hefty lim-itation, is that services other than speech are only allocated available resourcesremaining after speech users’ demands have been fulfilled. This would bear com-plicated impacts on the scope of a study that primarily focuses on load controlin a train scenario and the straight-forward way to tackle a problem as such is tolimit one self to load control of a single service.

3.2 Limitations 21

The most prominent limitation to the study is the use of a simulator. Althoughthe intention is to model a real-life scenario as close to reality as possible, a modelthat captures the major features of the scenario in both architecture and simula-tion results suffices. This will further be discussed in the methodology. Further-more, the simulator is currently unable to use real-world propagation gains aspart of the simulations and solely relies on theoretically calculated path losses.

4Network Modelling

This chapter describes the fundamental properties of the simulation environmentused for all studies in this thesis work. The description will cover the main pa-rameters present in all simulations performed throughout the thesis work, andspecifications for specific simulation runs will be described further in the corre-sponding study chapters.

4.1 Table of Notations

The following notation will be used for mathematical descriptions throughoutthe remainder of the report.

• u A cell service user.

• T The set of cell service users onboard a train.

• M The set of cell service users not onboard a train. In this thesis, these usersare also called macro users.

• C A cell.

• Su The active set of user u.

• λ The arrival rate of new callers into the system.

4.2 Scenario Architecture

A cellular network can be built using blocks of hexagonal cells as in figure A.2.Illustrated in Figure 4.1 is a hexagonal subsection of the serving area which will

23

24 4 Network Modelling

be exclusively used for simulating ends in the thesis. The color scheme in thefigure is used to help identify the three separate coverage areas (or sites) used.The reader should note how the hexagonal subsection uniquely covers three exactsites without overlap.

Figure 4.1: three-site, three cells/site simulation serving area with a cell ra-dius R and site-to-site distance D=3R

2

The dotted serving area in figure 4.1 also outlines the wraparound perimeter of thenetwork. A user crossing the dotted perimeter in the beige-yellow (or cream) cellthree in the top-right hand corner will be wrapped around so to continue in cellthree in bottom-left. This rule is true for the entire simulation area and allowsfor continuous mobility of users and propagating signals.

As depicted, the radius of the cell determines the length of the site-to-site dis-tance and thus also the physical area the serving area spans. Since the cell radiusplays an important role in total power transmitted by an RBS to users in its cover-age area, a specific radius will have to be determined in order to correctly modelRBS transmit power congestion as is the purpose of the study. Cells can have radiiranging from a couple of hundred meters to several kilometers with varying RBSoutput power limits. The resulting differences in power densities for a given dis-tance from the RBS will be inherently associated for capacity calculations andit is thus necessary to fix both RBS output power levels and the cell radius forsimulation purposes. Therefore, a maximum output power of 20W (43dBm) will

4.3 Network Environment 25

be assumed and a radius will be varied until for a suitable number of users untila substantial size of available power is consumed. A radius too small will leadto code congestion prior to power congestion and too large a radius will lead topower congestion for an unrealistic few number of users. A suitable level willlie somewhere in between. Having varied radii from 250 meters to 350 meters,simulations showed that a radius of 300 meters provided power congestion levelswith a substantial amount of users. Details can be found in the appendix.

4.3 Network Environment

The radio propagation environment used in the simulations is meant to model arelatively harsh, urban environment. This implies substantial signal attenuationin the area surrounding base stations.

Users outside of the train, denoted macro users, will all be engaged in typicalspeech services, with call durations that are Gaussian distributed with mean 60seconds. For improved reality measures, unreasonably short call lengths will beprohibited with a lower limit call duration of 15 seconds. Moreover, macro usersare distributed across outside and indoor environments where indoor environ-ments induce additional propagation losses.

The lower bound on the call length is introduced in order to limit the impactof very short term deviations on a channel that could cause a short call to bedissatisfied even though the total amount of data lost would be considered negli-gible. For a 15-second call, for example, a 2% BLER will correspond to 0,3 s of areceived message lost.

Macro users will be generated at random positions across the serving area at at ar-rival rate λ and will move in a straight line at low velocities in a random assigneddirection. It should be noted that users are throughout their lifetime engaged ina speech call. If a call is terminated so is the user.

4.4 Train Modelling

Users on the train, henceforth train users, are modelled in the same manner asmacro users with regard to call duration and service type. When train users’ callsexpire they are replaced with new users somewhere on the train so that the over-all number of active train users are kept at a constant 40 users during the entiresimulation. The substantial differences between macro and train users, from thesimulation perspective, are that train users are all put in an indoor environment,further degrading signal quality, and assigned a high-speed train velocity with aspecified straight direction that follows the deployment of a railway track.

The conceptualized railway track simply motivates and defines the direction ofmotion of train users and is deployed as a straight track at an angle of π12 rad.This angle entails that throughout the simulation, users will pass through allcells in the serving area with varying distances to nearby RBSs. This is illustrated

26 4 Network Modelling

in figure 4.2 where the faded track depicts the continuation of the bold trackfollowing wraparound. Following the illustrated process, the track will enter cellone in the bottom-left following the faded track’s exit from the serving area.

Figure 4.2: The railway track’s deployment in the serving area. The track’sangle at π12 rad leads to varying pathways with wraparound

3

As the railway track serves to model the direction of users’ movement, the concep-tualized train similarly serves to model the physical distribution of users along astraight line and the users’ speed.

The train is modelled after the modern Japanese high-speed train, the N700 se-ries Shinkansen on the Kyushu Shinkansen railway network that has a maximumspeed capacity of 260 km/hour. Since the train in the simulation scenario movesthrough a dense urban environment such speeds are not reasonable and the trainis thus modelled to slightly under half its maximum speed. Since intermediatecars carrying passengers are of 25 m in length each, typically containing eightcars in a trainset, the train as a whole is modelled to a total length of 200 m.All in all, this means that train users are generated in a random position along astraight line of 200 m while moving at 35 m/s.

5Network Capacity Determination

In this chapter, an attempt is made to approximate the capacity of a cell in thesimulated network. The purpose of this investigation is to find a suitable usercount for the serving area when the QoS constraints of the network reach theirborderline limit. The results of the investigation will serve as the basis for allfuture studies in this thesis.

5.1 Method

For determining the capacity, the scheme proposed by Evans and Everitt is pur-sued. Users are assumed to be uniformly spread across the serving area and thusentailing a roughly equal number of users in each cell. This number is graduallyincreased until the QoS constraints are reached. For the purpose of this study,that QoS constraint is based on user satisfaction and the capacity will be definedas the number of users N , when a certain percentage of the total amount of usersare satisfied. It is noted that this static capacity metric is only valid for the net-work architecture presented and described in section 4.2 on page 24 and is by nomeans a valid metric for every WCDMA cell in question.

Since different locations across the serving area will experience different radioenvironments, and that different cells will have a different user load over time,the metric for capacity per cell will be averaged over satisfaction levels for allindividual cells in the network. Simply using percentage of satisfied users on thenetwork level would have been a less appropriate metric for cell capacity.

27

28 5 Network Capacity Determination

5.2 Simulation Setup

Macro users are generated at random positions in the serving area with a setarrival rate of λ users/s. As mentioned previously in section 4.3, users will havea Gaussian distributed call duration with mean m = 60s. The user load in thesystem will systematically increase until the departure rate, µ, of users is equalto the arrival rate. The load in the system thus stabilizes at:

N ≈ λm users when µ ≈ λ.

By increasing λ, a series of simulations can be run for which N systematicallyincreases proportionally until capacity saturation levels are reached.

These simulations are run without any LC activated since the purpose is to findthe maximum number of users supported by the system when all users are grantedrequired resources at a best-effort basis. It should be noted that the limiting fac-tor here has been designed to be RBS transmit power in the cell rather than codeshortage so N can be increased without worries of running out of available codes,as is the aim of the thesis study.

Since the system’s user load level stabilizes at N when µ ≈ λ, only terminatedcalls once this state has been reached will be used in the evaluation set for usersatisfaction levels. Since λ varies between successive simulations, so will µ. Inorder to acquire same number of samples for evaluation, simulation times of eachrespective simulation will have to be adjusted. The reader is referred to the ap-pendix for further details regarding simulation lengths.

5.3 Evaluation Metrics

The capacity of a network in terms of number of users can be discussed. It canbe thought that if ten users utilize 100% of RBS resources then ten users is thecapacity of the network; however, given that codes are available, 20 users canutilize 100% of RBS resources if each user uses half the resources relative thefirst case. Since lower allocated power to users will jeopardise required SNRto maintain guaranteed QoS requirements for the users, the term satisfaction isintroduced as a viable metric for the entirety of the thesis work.

A satisfied user is one whose speech service, for the entire duration of the ser-vice, has a (BLER < 2%) and is (not dropped as a result of LC)

It follows from the definition that if at least one of the two requirements arenot upheld, the user is labelled dissatisfied. The metric for satisfaction placeslower bounds on the SNR a user is entitled to and ergo an upper limit on thenumber of users in the system. As long as 100% of users are satisfied there will beroom to handle additional users. The capacity definition will reflect a statisticallyconfident metric of dissatisfied users.

A cell’s capacity is the number of users, N , where 95% of the N users are satis-fied users.

5.4 Simulation Results 29

Simply accounting for the total sum of an RBS output power does not directlyreflect the actual load on it. What is of greater interest is the total sum of all activeusers’ demand on RBS DL power and how this relates to the RBS’s maximumoutput power limit of 20 W per cell. Users demanding 200% or 150% of thisvalue reflects different load levels. It is also necessary to distinguish between acongested cell and a saturated cell. Whereas a congested cell has little availablespace for additional users in terms of power, a saturated cell has no availablespace.

A power congested cell is one where the aggregate of users’ downlink power de-mand is greater than 75% of the serving base station’s maximum outputpower level.

A power saturated cell is one where the aggregate of users’ downlink power de-mand exceeds the serving base station’s maximum output power level.

It should be noted, following the definition, that if a cell is saturated it is alsocongested but cell congestion does not imply cell saturation.

5.4 Simulation Results

Figure 5.1 depicts the mean satisfaction level per cell versus the mean number ofusers per cell. It shows a clear trend of falling satisfaction levels with increasinguser load in the system - as expected. At values around 85 users/cell, the increasein arrival rate seems to overload the system and satisfaction drastically plummets.There is also a deviation from the trend at around 77 users (~λ = 12) wheresatisfaction takes a sudden upturn, not in-line with the rest of the trend. Thismay be due to pure statistical reasons.

Figure 5.1: Network satisfaction levels

92,00%

93,00%

94,00%

95,00%

96,00%

97,00%

98,00%

99,00%

100,00%

35 45 55 65 75 85

Mean number of users/cell

Me

an

% o

f s

ati

sfi

ed

us

ers

/ce

ll

30 5 Network Capacity Determination

It is noteworthy that although the 95%-mark is targeted fairly well with 85 users/cell,there seems to be no substantial elbow room for the number of users to deviatefrom this number before the system collapses. Although 80 users/cell has a slightmargin to the 95%-limit it allows for some fluctuations to the right without jeop-ardising the capacity threshold.

As previously mentioned, low QoS itself does not imply power congestion. Fig-ure 5.2 provides the corresponding mean transmit power level per cell. As rea-soned, the aggregate demanded DL power increases over time - with 85 users/cellwell pressing on the saturation limits of the cell. The 80-user mark is well into thecongestion state of the cell and therefore strengthens the result of 80 users/cellbeing an appropriate measure for the cell’s capacity.

Figure 5.2: RBS transmit power levels

0,00

5,00

10,00

15,00

20,00

25,00

35 45 55 65 75 85

Mean number of users/cell

Mea

n D

L p

ow

er/

cell (

W)

Since an arrival rate of 13 seems to model the sought after environment ade-quately, both in terms of QoS and DL power levels, this will be used to simulatethe rest of the studies in the thesis.

6Impacts of a Train on a Congested

Cell

Prior to studying possible improvements of the situation that arises due to a trainin a congested cell, the default effects must first be established and benchmarkedin order to provide a framework for analysis for future results. In this chapter,these effects are accounted for and main issues are addressed.

6.1 Method

Given the cell capacity in terms of number of users from chapter 5, a train willbe run through the simulation area as described in chapter 4 in section 4.4. Theresults from these simulations will be used as the point of reference for hereaftersuggested and modified LC schemes.

For now, basic LC schemes are now applied, consisting of basic AC and basic CCschemes. Two aspects that are of substantial interest are fairness and utility of thenetwork.

Utility

As explained earlier, AC serves to restrict admission of users in order to not jeopar-dise users’ QoS requirements and thus user utility. It will therefore be of interestto evaluate the effects a congested cell has on QoS levels on macro users and trainusers respectively. The framework for analysing the QoS levels for the two usergroups will be referred to as the network utility.

Fairness

The simulation serves to model the actual real life scenario of a train enteringa congested cell. If the CC algorithms are biased in any way when targeting

31

32 6 Impacts of a Train on a Congested Cell

users for various CC schemes, it will be of interest to evaluate fairness betweenthe dropping probabilities of macro and train users in a congested scenario. Ifthe dropping rate of train users is significantly greater than macro users, or viceversa, the dropping is considered unfair.


The simulation is set up identically to the simulations in chapter 5 with the fol-lowing modifications:

• The arrival rate of macro users is set to λ = 13 in accordance with the resultsfrom section 5.4.

• The number of train users is set to 40, the length of the train to 200 m, andthe velocity of the train to 35 m/s at an angle of π12 rad as described andexplained in section 4.4.

• Admission control is activated with the admission threshold set to 75% ofthe maximum transmit power of RBS for a cell (15 W) and restricts admis-sion of users in SHO and newly generated users according to equation 2.2in chapter 2.

• Congestion control is activated with a controlled dropping scheme that tar-gets users for dropping according to the amount of power resources theydemand of the RBS. The user, regardless of the type, with the highest poweron its radio link is dropped. This scheme is denoted as the highest link power(HL) scheme and applies the rule:

From the set U of n users in the cell with U = {u1, u2, ..., un}, with radiolink power set P = {Pu1 , Pu2 , ..., Pun } choose user uc : max{P } = Puc

6.3 Evaluation Metrics

As can be understood from the simulation network, a given cell will only be underthe influence of a train during certain periods of time throughout the simulation.Since this study is only interested in the impact of a train on a cell under theinfluence of a train, such an influence must be defined. In this report, a cell issaid to be under the influence of a train if one or more users on the train has thecell in question in its active set.

Similarly, a macro user is said to be under the influence a train if one or morecells in the user’s active set is under the influence of a train.

A cell, c, under the influence of the train, T , is one where ∃u ∈ T : c ∈ Auand thus

A user, u′ , under the influence of the train, T , is one where (∃u ∈ T : c ∈ Au) ∧c ∈ Au′


With the above definitions in place, solely macro users under the influence of thetrain will comprise QoS metrics

The main QoS metric for macro users used in this study is the satisfaction averagetaken on a per cell basis over all users who has had the given cell in its activeset when the given cell was under the influence of the train. This measure willbe compared to an overall satisfaction of the train users taken over the entiresimulation, in order to see if satisfaction differs between the two groups.

As an additional metric on the different treatment of macro and train users withregard to LC, CC drop rate will be calculated for train and macro users respec-tively, and AC block numbers will be studied.


Figure 6.1: Train’s impact on Cell DL Power in green site, cell 1

800 850 900 950 10000

5

10

15

20

25

Time (s)

Cell

DL P

ow

er

(W)

Figure 6.1 depicts the effects the train has on the DL power levels in a cell versustime when the train is both in the cell and when it is not. It is important to notethat this only depicts the effects on one cell and graphs for the other rail cellscan be found in the appendix. The presence of the train is illustrated with ared rectangle with stemmed red bars within the rectangle of blocked train users.Two whole periods of the train’s presence can be observed in the middle of thegraph, while sections of the end of the train’s first period can be observed in thefar left and the beginning of the fourth to the far right. The repetition of the

34 6 Impacts of a Train on a Congested Cell

Figure 6.2: Number of users in green site, cell 1

800 850 900 950 10000

10

20

30

40

50

60

70

80

90

100

Time (s)

Num

ber

of

Users

in C

ell

train’s presence occurs due to the wraparound perimeter. Bearing in mind thatthe presence of the train is equivalent to the cell being under the influence ofthe train, it ought to be noted that it suffices with at least one train user to havethe cell in question in its active set to have the presence of the train declared.Also, there are several short time intervals of train presence surrounding longerrectangles of train presence in figure 6.1. These sparks of train presence signalsthe cell breathing dynamics of a cell’s coverage area where an approaching user ona train may just momentarily be in the destination cell and in the next instant not.It is reasonable to assume that the physical train crosses the physical hexagonalcell-model border in conjunction with the longer rectangular presence and notthe short bursts of train presence prior to it.

A straight-forward inference from figure 6.1 is the upsurge in DL power that oc-curs due to the presence of the train; however, it is noteworthy that this doesnot as a rule occur due to cell entry although it seems to be the case for a ma-jority of cases. Another observation, in-line with expectations, is that blockingoccurs for DL power levels above 18 W - the admission threshold for speech ser-vices. Although no blocking occurs in some train periods due to not reaching the18 W-limit, the presence of the train strongly correlates with DL power valuesexceeding the congestion limit of 15 W.

Figure 6.2 is situated directly below figure 6.1 for the purpose of seeing mutualtrends. The reader can verify impacts on the number of users in the cell whenthe train is in the cell by simply tracking downwards from figure 6.1 to 6.2.

What can be observed from figure 6.2 is that the number of users in the cellclimbs steadily with the constant arrival rate but does not reach a stable number


of users before the train enters and effectively, for some reason, reducing the totalnumber of users in the system. Upon train cell exit, the number of users start torise again.

Table 6.1: Results of QoS Metrics

User Type Satisfaction Drop Rate Number of BlocksMacro Users 97% 2% 222Train Users 60% 3% 1386

Table 6.1 shows the results from the impact a train has on the QoS. Althoughthere was only a marginal difference in drop rates, there was substantial greaterdifferences in satisfaction and number of blocks.

Part II

Studies

7Congestion Control in Train

Scenarios

In chapter 6 it was noted that congestion control schemes with downlink poweras their primary decision basis, are likely to result in a bias towards being morelikely to target on-board users as a group. In order to mitigate this bias, thischapter introduces a method to modify an existing CC scheme in order to increasefairness between the two groups in terms of being targeted by CC. The impact ofthe method is evaluated mainly with regard to how it affects the performance ofthe scheme upon which it is applied.

7.1 Introduction

There are two fundamental sides to any scheme that serves to target a certainuser which have to be put in relation to each other and mutually prioritized inthe design of CC. This thesis will refer to these two properties as fairness andutility, and define them as follows.

Fairness

To prioritize fairness in a congestion action target selection scheme in this con-text is to strive towards giving all users the same levels of service, and thus thesame risk of being targeted by CC, regardless of the effect on overall system per-formance.

Utility

A congestion action target selection scheme prioritizing utility will have the over-all system performance as the leading performance indicator, disregarding theeffects on single users on an individual level.

39

40 7 Congestion Control in Train Scenarios

Since single rate AMR voice traffic sets the most stringent QoS demands, theonly resource release action against it is the dropping of the connection. Thismeans that in the context of this report, CC releases resources by terminating theconnections it targets.

The main viewpoint from which system performance is judged in this thesis isQoS which, from a voice traffic point of view, means satisfaction. CC affects thesatisfaction in the system in two ways; firstly by keeping the system from anoverloaded state, ensuring that each user receives satisfactory signal strength inthe downlink to maintain a BLER less than 2%. The second, and less pleasantway that CC affects satisfaction, can be seen from the definition of satisfaction in5.3 that a dropped user is also automatically a dissatisfied user.

In order to measure these two aspects of the utility of a given CC scheme, thefollowing metrics will be used.

• The proportion of time spent by a cell under the influence of a train that isspent in congested state, i.e.,

Time spent by the cell in congested stateTime spent by the cell under the influence of a train

• Drop rate of macro users in a cell under the influence of a train, i.e.

# of macro connections dropped by CCTotal # of macro connections present in the cell when under the influence of a train

• Drop rate of train users over the duration of the simulation time.

The main aspect of fairness of interest to this study is the difference in impact ofa given CC scheme on train users versus on macro users. For this purpose, therespective satisfaction and drop rate measures given for macro and train userswill be analysed in relation to each other.

7.2 Description

As is easily derived from the system utility metrics, the so called Highest Linkpowerscheme, where the connection estimated to require the highest amount of powerin the downlink, is the optimal method from a utility point of view, since it resultsin the largest amount of resources freed per iteration. However, due to their highpower demand as previously discussed, this is likely to result in a heavy target-ing of train users. In order to reduce this unfairness, connections in the cell canbe divided into two sets of connection, one containing all connections belongingto train users, and another containing all connections belonging to macro users.When a connection is to be selected as target for congestion action, the proba-bility pt is introduced as the probability that the connection to target is chosenfrom set of train user connections. If this probability is set to be the same as theproportion of total connections belonging to train users, i.e.

7.2 Description 41

P { set T is chosen } = nTnM+nTand analogously the alternative

P { set M is chosen } = nMnM+nTwhere nT is the number of connections belonging to train users, and nM is thenumber of connections belonging to macro users, then the choice of whether todrop a macro or train user is made under the premise that all connections areequal, resulting in exact fairness on a group level. When this initial division intosubsets of connections is performed, any congestion scheme could potentially beapplied on the subset of connections chosen.

As is always the case when measures are being taken to increase fairness into acongestion action scheme, this will impact the utility of the system. How largethis impact is depends on the characteristics of the applied scheme as well asthe distribution within each subset of the property upon which the selection pro-cess is based. For any utility prioritizing scheme however, the addition of thismethod will result in a prioritizing fairness towards train user over the overallperformance of the system. This effect could be regulated by simply introducinga scaling factor on one or the other of the sets as

P { set T is chosen } = CnTnM+CnTand

P { set T is chosen } = nMnM+CnT, which would result in a C times as high probability per user of a train user to beselected than a macro user. However, the impact of such modifications will notbe studied in this thesis work where we will always have C = 1.

Since this thesis limits itself to CC with regard to downlink power, the suggestedmethod will only be evaluated with regard to power based CC schemes. The CCschemes used for evaluation are described below.

Random

This is perhaps the simplest of the three, and also the one providing maximumbetween users within the set on which it is applied. The Random scheme simplychooses a connection at random from the set, which means that all connectionsin the set has exactly the same risk of being the target of CC action. Probability-wise, Random renders the initial division into macro and train users meaningless,since

P { connection c ∈ T is chosen } = nTnT + nM

1nT

=1

nt + nMwhere M is the set of macro user connections and T is the set of train user connec-tions, which is the same as the probability of connection c being chosen directlyfrom the set of all connections.


Highest Link Power

Opposed to Random, Highest Linkpower, as described earlier, is the ideal schemefrom a utility point of view.

If PT is the set of downlink power values belonging to macro user connections,PM is the set of downlink power values belonging to train user connections, andpc is the downlink power value of connection c, then

P { connection c ∈ T is chosen } ={ nT

nT +nM1 , if pc = max(PT )

0 , otherwise.

This gives us the expected value of the downlink power freed from a single dropas

E{p} = nTnT + nM

E(max(PT )) +nM

nT + nME(max(PM ))

with the introduction of the suggested method, as opposed to

E{p} = max(PM + PT ),

which is the expected power value recieved if the method is not applied, and thehighest linkpower scheme is applied directly on the entire set of all connections.

As can be seen from the equation, how much the introduction of the fairnessscheme impacts the average power released per connection dropped under thehighest linkpower scheme depends mainly on two factors. Firstly, it dependson the difference between the maximum power in each set. If the maximumpower in the train set is of approximately equal size as the maximum power inthe macro set, then the introduction of fairness in this case would be marginal.However, if the difference is large, and more specifically if the set with the pro-portionately larger maximum power of the two has a smaller cardinality, then theimpact could be large.

Proportional Fairness

The Proportional Fairness scheme is considered in this study to be a bridge be-tween the maximum fairness of the random scheme, and the maximum utility ofthe highest linkpower scheme. In this scheme, connections are selected with aprobability relative to the proportion of the total downlink power of the set occu-pied by that connection. This entails that a connection occupying a large portionof the total downlink power will be more likely to be dropped, but the drop willnot be certain, as is the case in the highest link power scheme. The result is ascheme that is fair in the sense that a given connection, no matter the radio en-vironment, always will have a chance to remain for the entirety of the call, buton average, the power freed per drop will still be larger than the random scheme,given that the distribution of power values within the set is not constant. Theprobability of a certain connection being dropped in this scheme is as follows.

7.3 Simulation Setup 43

P { connectionc ∈ T is chosen } = nTnT + nM

pc∑PTp.

Without elaborating into the math of the expected value of power freed per con-nection under this scheme, it can be understood that it will serve as a cross be-tween the two previously mentioned schemes, which is the purpose for which itis included in this study.

For the situation at hand, based on local expertise at Ericsson, it is reasonable tobelieve that the proportion between the cardinalities of the macro and train setsis about equal for a given situation, which is also the situation in our simulations.Since train users travel at approximately the same speed relative the RBS, andin a relatively homogeneous radio environment compared to the macro users, itis reasonable to believe that the power value distribution for train connectionswill have a considerably higher mean but smaller variance. The macro users,on the other hand, are generally travelling at lower speeds and in a more diverseenvironment, resulting in a lower mean value but with a large difference betweenthe highest and lowest power value, where the highest might well be on par withthe train connections.


To test the suggested method, the default scenario from the last study will beused with AC disabled. The reason for this is that AC limits the amount of CCrequired to avoid congestion in a given situation. This reduces the number of CCaction samples available for evaluation, without having any important impact onthe comparison of the suggested metrics between different runs.


The results of the simulations can be seen in 7.1. The fairness with regard todrop rate between macro and train users seem to be higher with than without thegroup fairness scheme. However, due to the very characteristics of the situationwhere train users are only in a cell for a very short amount of time, there wasnot enough time in this thesis to run long enough simulations to produce a highenough number of drops in order to produce a reliable comparison between thedrop-rates. However, it is reasonable to believe that the math presented aboverepresents the situation well on this behalf.

Looking at the differences in total drops and time spent in congested state forhighest linkpower, prop. fairness and random, results are in line with the ideathat increased fairness comes to the expense of utility. However, comparing theresults of each scheme with and without the group fairness scheme, the resultsare unexpected. The results show a tendency towards a higher utility with the


scheme than without, which is not in line with theory, since the group fairnessscheme should introduce a on average lower power release per drop.

A possible explanation for this result is that the linkpower used as basis for targetselection in all the studied algorithms are based on the momentary estimate ofthe downlink power requirements for the connections, and does not take intoaccount the probable development of said requirement. For a train user travellingthrough a cell, it is likely that the initial estimate of its required downlink power,based on a position at the cell edge travelling at a high speed, is much higher thanit’s actual power consumption over time, given that it passes relatively close to theRBS. For a slow moving macro user on the other hand, a high power requirementis likely to preserve for the duration of the call. This could entail that, in somecases, the dropping of a macro user with a high power consumption is betterfor the utility in the long run than the dropping of a train user with a highermomentary estimate.

Table 7.1: Results of Fairness Metrics

Scheme DR Outside DR On-board Drops % in Congestion

No class fairnessRandom 4% 1% 76 57%

Proportional Fairness 11% 5% 109 24%Highest Link Power 4% 8% 63 15%

With class fairnessProportional Fairness 8% 8% 109 20%Highest Link Power 4% 3% 144 17%

7.5 Conclusions

Because of the low number of drops per time performed in the run scenario to-gether with the stochastic properties of the simulation, more extensive simula-tions are required in order to produce more reliable results with regard to fair-ness between train and macro users. However, the des

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New Institutionen för systemteknik560891/FULLTEXT01.pdf · 2012. 10. 16. · Ericsson AB Examinator: Danyo Danev isy, Linköping University Linköping, 8 juni 2012. Avdelning, Institution