IN DEGREE PROJECT ELECTRICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2017

Feasibility Study of Vehicular Teleoperation over Cellular Network in Urban Scenario

Genomförbarhet studie av teleoperation av fordon via mobilnätverk i stadsscenario

YIFEI JIN

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ELECTRICAL ENGINEERING

Abstract

With the continuous progress on autonomous vehicle and remote driv-ing techniques, connection quality demands are changing compared withconventional quality of service. Vehicle to everything communication, asthe connectivity basis for these applications, has been built up on LongTerm Evolution basis, but due to various ethical and environmental issues,few implementations have been made in reality. Therefore simulation ap-proaches are believed to provide valuable insights.

To fully model an LTE vehicular network, in this work we first pro-vide a comparison study to select the preferable LTE simulator. Aimingto integrate communication nodes with mobility, a solution for simulationframework is developed based on a state-of-art comparison study on theexisting simulator frameworks. We then further develop the network sim-ulator, and complement it with hybrid wireless channel modeling, channeland quality of service aware scheduler, and admission control strategies. Interms of instant optimization of the network, real-time access is emulatedfor external devices to communicate with the simulator. In this thesis,the evaluation of the framework performance considers two aspects: theperformance of the simulator in LTE V2X use case and the feasibility ofthe service, specifically, remote driving, under realistic network capacity.For our framework, the results indicate that it is feasible to realize remotedriving in an LTE urban scenario, but, as an example, we show that foran area of Kista, five vehicles could be hold by a base-station with guar-anteed service at most.

Keywords: Vehicle to Everything, Long Term Evolution, Real-timesimulation, Quality of Service

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Sammanfattning

Med kontinuerliga framstegen pa autonomt fordon och fjarrkontrollteknikforandras kravet pa anslutningskvalitet i jamforelse med konventionell ser-vicekvalitet. Fordon till allting (V2X) kommunikation, som anslutnings-grund for dessa applikationer, har byggts upp pa basis av Long TermEvolution (LTE) system, men pa grund av olika etiska och miljomassigaproblem har fa implementeringar gjorts i verkligheten. Darfor antas simu-leringsmetoder ge vardefulla insikter.

Att fullt ut modellera ett LTE-fordon natverk, i det har arbetet ger viforst en jamforelsestudie for att valja den foredragna LTE-simulatorn.I syfte att integrera kommunikationsnoder med rorlighet utvecklas enlosning for ett simuleringsramverk baserat pa en jamforelsestudie pa be-fintliga simulatorramar. Vi utvecklar sedan natverkssimulatorn ytterliga-re, och kompletterar den med hybrid tradlos kanalmodellering, kanal ochservicekvalitetmedvetna schemalaggning och antagningskontrollstrategier.

Nar det galler direkt natverksoptimering, emuleras realtidsanslutningav externa enheter for att kommunicera med simulatorn. I denna avhand-ling utvarderas ramverken i tva aspekter: simulatorns prestanda i LTEV2X-anvandningsomradet och genomforbarheten av tjansten, sarskilt fjarrkorning,under realistisk natkapacitet. In vara ramverk visar resultaten att det armojligt att realisera fjarrkorning i ett LTE-urbana scenario, men som ex-empel visar vi att for ett omrade i Kista skulle som mest fem fordon kunnaskotas av en basstation med garanterad service.

Nyckelord: Fordon till allting (V2X), LTE, realtid simulering, service-kvalitet.

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AcknowledgementThe work was carried out in Ericsson Research, Cognitive Automation Lab,from February, 2017 until August, 2017.

Firstly, We would to give my sincerely thank to Dr. Aneta Vulgarakis Feljan,who gives me the opportunity for working towards vehicular ad-hoc network andintegrate with cellular technology in Ericsson Research, Cognitive AutomationLab. Her guidance along side the thesis has always been an assist through outthe period. Her contribution includes but not limit to giving proper support inboth reference material and implementation guidance, knowledge sharing andthesis structural suggestions. My examiner, Prof. Viktoria Fodor also mademuch contribution to the work along side with detailed reflections and patientsuggestions. Mr. Yiqing Wang, as my co-worker, did remarkable progress con-cerning mobility map support and traffic scheduling. My colleague, Dr. RafiaInam, Dr. Elena Fersman, Dr. Athanasios Karapantelakis and Dr. Pontus Jo-hanson, provide help on LTE QoS Priority, systematic overview, Ns-3 simulationand Dynamic QoS negotiation algorithm respectively. Mrs. Xianghan Wang, asmy opponent, spend massive time studying my thesis and reflects with profes-sional feedback. I would express my thanks and best wishes to people supportme in the period beyond mentioned.

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Abbreviation

Table 1: Abbreviation

Abbreviation Full name3GPP 3rd Generation Partnership Project

5G Fifth generation mobile communication systemAPI Application Programming InterfaceBER Bit error rateCA Carrier Aggregation

CQA Channel and QoS Aware SchedulerCQI Channel Quality IndexDCF Distributed Coordination Function

DSRC Dedicated Short-Range CommunicationseNB Evolved Node B

E-UTRAN The Evolved UMTS Terrestrial Radio Access NetworkEPC Evolved Packet CoreEWA Emergency Warning applicationFD Frequency Domain

FEA Finite Element AnalysisFTP File Transfer ProtocolGBR Guaranteed bit rateGPS Global Positioning SystemGSM Global System for Mobile Communications

HARQ Hybrid Automatic Repeat RequestiCS iTETRIS Control System

IEEE Institute of Electrical and Electronics EngineersiTETRIS The Open Simulation Platform for ITS Services

ITS Intelligent transportation systemLENA LTE-EPC Network SimulatorLTE Long Term EvolutionLOS Line-Of-Sight channelMAC Media Access Control

MANET Mobile Ad-hoc NETworksMBR Maximum bit rateMME mobility management entityNAS Non-Access Stratum

NLOS Non-Line-Of Sight channelNS Network Simulator

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Table 2: Abbreviation II

Abbreviation Full nameOBU On-Board-UnitOSM Open street map

OMNeT++ Objective Modular Network Testbed in C++P2P Point to Point communicationPCF Point Coordination Function

PDCP Packet Data Convergence ControlPGW PDN Gate WayPHY Physical LayerPSS Priority Set SchedulerQCI QoS Control IndexQoS Quality of ServiceRB Physical resource block

RLC Radio Link ControlRRC Radio Resource ControlRSSI Received Signal Strength IndicatorSGW Serving Gate Way

SimuLTE LTE User Plane Simulation Model for INET & OMNeT++SNR Signal Noise RatioSNS VSimRTI Network Simulator

SUMO Simulation of Urban MObilityTBR Target Bit RateTD Time DomainTTI Transmission Time IntervalUE User Equipment

UMTS Universal Mobile Telecommunications SystemV2D Vehicle to Device CommunicationV2I Vehicle to InfrastructureV2V Vehicle to Vehicle CommunicationV2X Vehicle to Everything Communication

VANET Vehicular ad-hoc networkVeins Vehicle in Network simulatorVRC Vehicle to Road Side Unit Communication

VSimRTI Simulation of Vehicle-2-X CommunicationWAVE IEEE 802.11p network protocol, wireless access in vehicular environmentWLAN Wireless Local Area Network

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List of Tables and Figures

List of Figures1 LTE Brief Topology . . . . . . . . . . . . . . . . . . . . . . . . . 142 IEEE 802.11 and LTE in V2X use-case . . . . . . . . . . . . . . . 173 Real-time Motor Controlling in SimuLink . . . . . . . . . . . . . 204 VeinsLTE Workflow . . . . . . . . . . . . . . . . . . . . . . . . . 325 VSimRTI Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 336 NS2mobility Workflow . . . . . . . . . . . . . . . . . . . . . . . . 347 Implementation Topology . . . . . . . . . . . . . . . . . . . . . . 368 SUMO Working Flow . . . . . . . . . . . . . . . . . . . . . . . . 379 Simulator Performance . . . . . . . . . . . . . . . . . . . . . . . . 4210 Delay for Critical Stream with 10Mbit Background Traffic . . . . 4311 Packet Loss Ratio for Critical stream in 10Mbit and 1Mbit Back-

ground Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4412 Test route from (188.35, 4454.56) to (968.36, 4094.65) . . . . . . 4513 Packet Loss Ratio for non-Critical stream with 10Mbit and 1Mbit

Background Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . 4514 Capacity for Bus Route per eNB . . . . . . . . . . . . . . . . . . 4615 Example of comparison between parsed building and real building 49

List of Tables1 Abbreviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Abbreviation II . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Comparison of Current Mainstream V2X Simulation Frameworks 354 PHY Layer parameters . . . . . . . . . . . . . . . . . . . . . . . . 385 MAC Layer parameters . . . . . . . . . . . . . . . . . . . . . . . 396 Radio Resource allocation in MAC Layer . . . . . . . . . . . . . 40

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Contents1 Introduction 10

1.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . 101.2 Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.4 Sustainability and Ethical issues . . . . . . . . . . . . . . . . . . 121.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Background 142.1 LTE network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Introduction to V2X communication . . . . . . . . . . . . . . . . 162.3 Comparison Between LTE and IEEE 802.11p . . . . . . . . . . . 172.4 Introduction to Real-time Simulation . . . . . . . . . . . . . . . . 19

3 Related Work 23

4 Radio Resource Allocation for V2X applications 254.1 Communication QoS in V2X Scenario . . . . . . . . . . . . . . . 254.2 TBFQ Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3 PSS Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.4 CQA Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5 Integration Framework and Simulation Tools 295.1 Cellular Network Simulator Selection . . . . . . . . . . . . . . . . 295.2 Integration Framework Selection . . . . . . . . . . . . . . . . . . 30

5.2.1 VeinsLTE . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.2.2 VSimRTI . . . . . . . . . . . . . . . . . . . . . . . . . . . 325.2.3 Ns2mobility Package . . . . . . . . . . . . . . . . . . . . . 345.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6 System Implementation 366.1 System topology . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.2 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376.3 PHY Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386.4 MAC layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.5 RLC layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

7 Evaluation 417.1 Simulator Capability . . . . . . . . . . . . . . . . . . . . . . . . . 417.2 Quality of Service . . . . . . . . . . . . . . . . . . . . . . . . . . . 437.3 Scenario Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . 46

8 Conclusion 478.1 Review of Objectives and Results . . . . . . . . . . . . . . . . . . 478.2 Limitations and Future work . . . . . . . . . . . . . . . . . . . . 488.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

9 Appendix 509.1 Pseudo-code Logic of Hybrid Building Propagation Model . . . . 50

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1 IntroductionSupervised-driving and autonomous-driving are newly developed fields for thevehicle manufacturing industries. Volvo[2] believes almost all traffic accidentsare due to human mis-behaving. In autonomous driving mode, people on boardare not expected to control the vehicle, the vehicle should perform all drivingpractice including brakes, gears, steers and so on. Autonomous diving is thusexpected to make the transportation safer. Moreover, autonomous driving canresult in more environmental friendly and smooth driving, due to optimizedacceleration and braking. In terms of supervised driving mode, the techniqueis adopted in having vehicle staying in the same lane, platooning[1], keepingthe distance with the former vehicle etc. In this process, the vehicle needs thedriver’s continuous supervision. For either autonomous driving or superviseddriving, it is critical to have the command delivered to vehicle under safety andservice quality demand. For remote end, taking feedback from vehicle timely isalso important due to making on-time reaction in the changing driving environ-ment. Regarding both techniques, on-time delivery of commands and feedbackin multiple data streams is a challenging task.

Since the definition of Vehicle to Vehicle Communication (V2V) and Vehicleto Infrastructure Communication (V2I), a major requirement is to build a re-liable, high-bandwidth and ultra-low latency communication network betweenremote controllers and on-board units (OBU). Car industries, and telecommu-nication manufactures have different insights but yet share a similar interest inthis area. Today we use the term Vehicle to Everything (V2X) for the vehicularnetwork connecting cars to other cars, other devices and to the wide-area com-munication infrastructure. Along side with a huge number of newly developedservices and applications in V2X, there are also new demands and challengesfor the network. To guarantee a stable performance, three conditions have tobe met. Continuous connectivity, means the client should keep in touch withthe vehicle for multiple purposes. Priority handling, indicates the commandtraffic should be guaranteed throughout the driving process. Finally, timelydelivery, requires the avoidance of areas with network congestion. As networkcongestion in V2X is correlated with congestion on the road network, optimizingthe communication becomes correlated with path selection on the road network.

1.1 Problem DefinitionIn this thesis, we address the problem of vehicular tele-operation in urbanarea as our major field of interest. Applications will include accident areaand connectivity-blind point avoidance, route rescheduling and speed optimiza-tion. Comparing with previous study of applications in mines[6], vehicular tele-operation in urban area puts more focus on tele-operation for massive numberof User Equipments (UE). Also, in mining scenario, tele-communication engi-neers have the freedom to deploy access point and base station according totele-operation service’s need since nothing has been constructed. In our case,we would like to make better use of the well-deployed base station to pro-vide guaranteed service. However, tele-operation service is challenging in urbanenvironment due to the high speed mobility of the vehicles and demands on

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communication quality. In this case, the telecommunication network, includingthe access network, the core network and cloud end, has to provide low latency(for critical data traffic), guaranteed acceptable throughput (for thought-put-demanding service performance), abundant client capacity (for massive numberof UEs in urban scenario) and small packet loss ratio (for guaranteed commu-nication quality).

To keep the on-time feedback from both vehicle and remote end, the telecom-munication companies have to provide stable performance throughout the driv-ing process. This is challenging because the current wireless networks are de-signed for a best effort solution for throughput demanding services. Also, fordelay-critical traffic like old-fashioned phone conversations, has never been mak-ing optimal use of the bandwidth. According to Ericsson’s Road to 5G[5], theemerging 5G technology will include solutions for V2X networks. However, asLTE is the present technology being implemented in large cities, the objectiveof this thesis is to evaluate whether LTE can already support the tele-operationservice.

1.2 Thesis ObjectivesDue to the high complexity, implementation cost and implementation time ofreal-life experiments, as well as the constraints of the regulations today, we usesimulation to evaluate the performance of remote operation of vehicles throughLTE network. For such a simulation based study, the road system with the ve-hicular traffic, and the communication network need to be considered together.Such a simulation framework does not exist today. In terms of traffic simulationand network simulation, we will divide the work into two inter-related parts. Fortraffic simulation, we use the results of [7] to realize the mobility of the vehicleand rerouting features. We will import their result into the mobility model ofthe communication nodes and feedback the traffic simulator with connectivityblind-point and data congestion alert. This thesis will focus on the simulationand evaluation of the communication network, with the following objectives:

1. Do a state-of-art research on available network simulators, provide a com-parison result and build a realistic V2X simulation scenario in the city ofStockholm.

2. Select appropriate integration framework based on:

• The capability of the supported network simulators. In particular,the network simulator should be able to simulate the full LTE stack.

• The real-time simulation capability of the integrated framework.

3. Implement the simulation through selected simulator and integration frame-work.

4. Through the simulation case, verify the feasibility of applying LTE in V2X.

5. Examine the maximum capacity of the scenario based on a realistic trafficflow route.

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1.3 MethodologyIn this thesis we evaluate the performance of vehicle tele-operation via mod-eling and simulation. Comparing to real-world experiment, the drawback ofmodeling and simulation is that we may not consider some aspects that couldbe crucial in a real implementation. However, simulation has significant advan-tages. In general, simulation is cost-effective and fast, and allows the evaluationof a wider range of environments and system parameters. In the specific caseof vehicle tele-operation, real-world experiments in the early stage of the studywould also mean high risks in the traffic. Due to this, real-experiments wouldalso be against the present laws in Sweden.

Mathematical modeling could be an alternative of simulation based studies.Mathematical modeling in out case is however not possible, due to the complex-ity of the scenario, considering the mobility of the vehicles, the complex radioenvironment, and the complexity of the LTE network. Mathematical modelingcould give some guidelines on the general performance, but the results are ex-pected to be very far from the ones in a realistic scenario.

In evaluation of the modeling methodology, several aspects have to be takeninto consideration: model type, model validity and model usage. The type ofthe model we build in the research are idealization models. We would startfrom making a series of assumptions on the target-of-interest to simplify theuse case. For the modeling of the system components that are not in the mainfocus of the thesis, we use the results of preliminary research. Consequently,the results of the simulations are expected to be correct, if the implementationof the simulator is correct. In terms of model usage, we will manipulate finitenumbers of parameters (QoS properties, traffic, Tx Power...etc), observe the re-sult (Feasibility, Capacity...) and discuss the observed dependencies.

1.4 Sustainability and Ethical issuesThe research will contribute much to sustainable engineering, in both auto man-ufacturer’s perspective and telecommunication company’s perspective.

For auto industries, through dynamic routing and scheduling, autonomousdriving means less unnecessary brake and optimized gear control. This will domuch contribution to expand the life span of engine and gearbox. In platooningperspective, better connected vehicle means shorter vehicle by vehicle distance.This will reduce air-resistance and increase power efficiency. In all, autonomousdriving will deliver better energy consumption plan, and will finally result in agreener carbon foot-print. Through high-performance network, road safety willbe more guaranteed comparing with man-intervened driving, as there could beapplication to manage the whole traffic to avoid congestion and accidents.

For telecommunication companies, location estimation in mobility modelmake a smarter power allocation algorithm. This will decrease the power con-sumption of base stations and contribute a more stable communication channel.Dynamic QoS negotiation and network slicing technology will optimize the us-

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age for the network resource.

Ethical problems are widely discussed for assisted and autonomous driv-ing. There has been discussion concerning the responsibility when the au-tonomous car come into accident. Swedish law previously stands against havingautonomous cars on road. However, recently, the Swedish Transport Agencyhave approved the trials for autonomous vehicles and provides a law explana-tion concerning responsibility in case of accident. Swedish-Transport-Agencywill take the response for supervising the autonomous cars and will take re-sponsibility in case that accident happens. Consequently, ethical problem willbecome less of concern in the future.

1.5 Thesis OutlineThe rest of the thesis is organized as follows: In part 2, Background, we give astate-of-art study concerning LTE network, V2X communication and real-timesimulation. In part 3, Related Works , contributions of pioneers in this fieldare illustrated. Part 4, Radio Resource Allocation for V2X applications, willdo the theoretical comparison among different scheduling algorithms for bestperformance in V2X use-case. In part 5, Integration Framework and SimulationTools, the thesis will focus on the implementation of the simulator integrat-ing mobility to cellular network, and will do a comparison study among theintegration framework and respective existing simulators. In part 6, SystemImplementation, a systematic view of the simulation is made with detailed im-plementation parameters. In part 7, Evaluation, the result of the simulation willbe given and analyzed. In part 8, Conclusion, we will evaluate the simulationassumptions and limitations, discuss future work and conclude the thesis. Inpart 9, Appendix, the algorithm integrated in the simulation will be illustratedby pseudo-code and mathematical proof.

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2 BackgroundIn this section, we will introduce V2X communication and LTE concepts. Basedon previous studies, we will compare 802.11p network, as another widely adoptedsolution, with LTE to prove the capability of the V2X LTE technology. Finally,we will discuss the demands for real-time simulation for LTE services.

2.1 LTE networkFor cellular V2X technology, LTE network, as the fourth generation communi-cation technology, is taking a promising share of the market. The developmentof LTE was initialized in 2004 by the Third Generation Partnership Project(3GPP). It is widely implemented for its high throughput, low latency, Fre-quency Domain (FD)/ Time Domain (TD) Duplex combination, mobility sup-port and relatively simple architecture in mobile network. From a high-levelperspective, an LTE network topology mainly consists of UE, Evolved UMTSTerrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC).The E-UTRAN could be separated into Evolved NodeBs (eNBs) and EPC in-cludes mobility management entity (MME), serving gateway (SGW) and PacketGateway (PGW) as illustrate in Figure 1.

UE

S1-MME

S1-UE-UTRAN

MME

S8SGW SGiPGW IP service port

EPC-Core NetworkAccess Network

Figure 1: LTE Brief Topology

E-UTRAN is the access network consisting of the cluster of eNBs. EacheNB is a base station processing low levels’ controlling message and they areinter-connected through interfaces (X2) to achieve packet transmission from theUEs. The E-UTRAN’s functionality includes: radio resource management (in-cludes power allocation, handover, admission control, channel modeling, multi-carrier aggregation (CA) etc), header compression (for over-headed traffic insmall packet, like the VoIP), encryption and connection to the EPC. Once theUE’s connectivity status has changed, each UE is expected to update the chan-nel state to the EPC through S1 interface.

In EPC, for down-link, controlling message is received from the E-UTRANto the SGW. EPC reads the bearer information in the header and does manage-ment work like frame length decision when re-framing for external IP network.1.In terms of up-link, once the PGW receives a packet from the remote host, it

1Frame length decision: EPC will optimize frame length for the purpose of payload effi-ciency

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performs packet filtering according to Traffic Flow Template (TFT) and at-taches the QoS bearer to the flow according to the TFT. Each TFT is specifiedwith its destination IP address and destination port number for respective UE.These packets will be re-transmitted into radio network. When initialized, eachUE will be attached to its respective eNB. Also, it will be assigned an IP addressby the PGW with at least one bearer from the MME. After these steps, it willbe accessible from outer network.

MME is the most important physical node in core network. Its functionalityincludes bearer management, connection management and vertical handover2handling. In urban scenario, where priority handing is most essential, we willfocus on bearer management here. For bearer management, the Non-AccessStratum (NAS) protocol rules the whole process. From establish to release,the bearer is responsible for describing the communication quality which ser-vice demands. The bearer will be attached with Guaranteed bit rate 3 (GBR),Maximum bit rate (MBR), Target bit rate (TBR), QoS Class Identifier (QCI),allocated/relative priority, and Head of line Latency (HOL) as QoS informa-tion of the service. Generally, the bearer could be classified into GBR bearer orNon-GBR bearer which assigned for delay critical or throughput critical servicesrespectively. To optimize such trade-off between different flows, MME node isintegrate with radio resource management (RRM) scheme in case of data con-gestion.

To ensure a good user experience, there has been evolution in RRM schemeto meet the needs of various scenarios. Except for admission control, powerallocation, bandwidth allocation (Frequency reuse) etc, scheduling is a commonapproach to realize quality-guaranteed communication. There are two criticalfactors to concern in scheduling for radio network: fairness and performance.Fairness means the system should make every UE to have the chance to beserved. Performance requires the network’s overall quality (total throughput,average HOL, etc) should be optimized. Traditional methods such as round-robin, maximum-throughput and proportional-fair put focus on only one ofthese aspects. Some advanced algorithms have been verified in this thesis toprovide an optimal solution for urban areas with vehicle mobility. Detailed de-scription concerning these algorithms will be give in Chapter 4.

In addition to bearer management, the MME also contributes to connec-tion and mobility management like location re-freshness. In NAS protocol, UEshould refresh its location information whenever it moves out of the trackingarea. In terms of security, LTE involved mutual authentication in both UE andMME alongside with integrity check to secure the communication.

From the layered perspective[10], a LTE cellular network could be dividedinto six layers, Physical Layer (PHY), Media Access Control Layer (MAC),Radio Link Control Layer (RLC), Packet Data Convergence Control (PDCP),Radio Resource Control (RRC) and NAS. PHY, MAC and RLC are of primefocus in the dissertation. In the PHY layer, the main task is transferring packet

2Vertical handover: Handover between protocols. e.g. From LTE to WAVE3Guaranteed bit rate: The demanding throughput of a class of service, should be immune

for congestion in the service process

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for MAC layer into the air. Link-adaptation, power allocation and cell man-agement (synchronization and handover) are the main algorithms in this layer.Physical Resource Block (RB) is the main data unit here. In the MAC layer,Radio resource management and error correction are performed. Instead ofRB, transport block is the data unit, which includes a MAC header prefix andpadding. In the RLC layer, the main responsibility is to reassemble data frag-ments, protocol error detection and IP addressing. Instead of resource blocksdefinition, Protocol Data Unit (PDU) and Service Data Unit (SDU) are intro-duced in the RLC layer. Incoming blocks are refereed as the SDUs from upperlayer and sent blocks are called the PDU. Such definition will still be employedin the upper layers.

2.2 Introduction to V2X communicationAs 3GPP put forward its 14th release[12], as an expansion on V2V communi-cation, initial cellular V2X standard has been completed. V2X communication,including direct communication (node to node) and cellular network, indicatespassing information from vehicle to any communication node which is in driv-ing scenario and vice versa. Based on Device-to Device communication, 3GPPexpands LTE platform for new application and service. In vehicular environ-ment, 3GPP claims the V2X network should be fitting for high speed mobility(250Kph) and much larger networks (thousands of nodes). Previous solutionsincluding Global System for Mobile Communications / Global Positioning Sys-tem (GSM/GPS) and radar/cruise control have met problem when the drivingdistance between trucks becomes narrower. Comparing with the previous V2Xstandard, new features demand for better performance for all aspects includingRRM scheme, frame optimization, etc.

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eNB

RSU

RSU

RSU

802.11p

802.11p 802.11p

OBU

OBU

OBU

LTE

V2V

V2I

CloudApplication

V2N

Figure 2: IEEE 802.11 and LTE in V2X use-case

Vehicle tele-operation, as a typical V2X service, will be analyzed in depthhere. According to the survey from Technische Universitat Eindhoven[11] in2007, tele-operation has been widely adopted in terms of security application,space application, military applications and tele-presence robot. In term oftele-presence technique, remote-driving is one of the use case in this field. Torealize the tele-operation for vehicles: positioning, control command, and videostreams are the key data flows that need to be involved.

In Figure 2, On-Board-Unit (OBU) is usually the user portal for V2X net-work, it processes the received command, gives feedback to application andcontrols the vehicle. In V2X use cases, the remote user should be able to re-ceive video stream and positioning information from the vehicle OBU, and sendcommand flow back to control the cars in a stable manner with acceptable la-tency. To realize such features, a vehicular communication network has to beconstructed. Vehicular communication network are networks in which OBUand external device, including Road-Side-Unit (RSU) and base-station, are thecommunicating nodes. This network should also include core network part tobe able to handle varying priority among multiple flows.

2.3 Comparison Between LTE and IEEE 802.11pThe optional protocol for V2X communication at the moment is Vehicular ad-hoc network (VANET) in 802.11p and cellular network communication. Inpre-5G era, we think LTE is the best representative for cellular network in V2X

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scenarios as proven technology standards. There have been a lot of discussionsregarding the feasibility of the above-mentioned two network protocols. VANETadopts wireless access in vehicular environment (WAVE IEEE 802.11p) as theirstandard, is now the mainstream approach for positioning and other securityapplications.

802.11p is an evolution from 802.11 series, which could be driven back to802.11a. Compared to the classical 802.11a protocol,[13] 802.11p performs bet-ter from OBU to RSU in V2I communication scenario in terms of latency andcontinuous connectivity. Nevertheless, such optimization brings weakness alongside with it strength.

The improvement of low latency comes with a price. To shorten the end toend delay, not only authentication process is omitted, WAVE also adopts broad-casting way to communicate among the connected nodes. In WAVE 802.11p, bybroadcasting without Hybrid Automatic Repeat reQuest (HARQ), each vehiclehas triple roles, sender, receiver and router. As a result, the receiver does notrequest correction re-transmission for sent packet errors, which requires bet-ter Received Signal Strength Indicator (RSSI) performance in the receiver end.Hence, it will result in limitation of radio coverage. Within any distance above40 meters, 802.11p’s throughput fades dramatically. Even within its reach of40 meter, it can only guarantee half of its maximum throughput,[14]. Its con-nectivity totally relies on modulation technique. In extreme cases, there areexamples that the transmission range is shorter than the width of the road.

The original shortcoming for IEEE 802.11 series still persists. Oriented fromDedicated Short-Range Communication (DSRC), 802.11p uses channel with3Mbps down-link in 5.9GHz band (5.850-5.925 GHz). This, comparing withthe 150Mbps as maximum down-link throughput for LTE network, will limitthe performance of embedded applications. However, with up-link bandwidthof 50Mbps, LTE provides the possibility of live video streaming for real-time ap-plications. With regard to throughput, LTE will surely be the optimal choice.As the continuous development of LTE-V carries on, far more beneficial prop-erties are developed which would surpass the present 802.11p protocol. Themajor strength will be lying in latency, cost and security.

Even in terms of latency, cellular network out-weights 802.11p in three per-spectives. From the perspective of PHY layer, there are studies[15] showingduring penetrating a busy road, the coverage of WLAN cells will decay dra-matically. LTE network, however, at the frequency of 2.1-2.6 GHz, will sufferless. In terms of MAC layer, IEEE 802.11p does does not implement QoS lev-els. Point coordination Function (PCF) could be used to give high priority,but it is rarely implemented in real access points. In LTE network, since theeNBs are connected to EPC, there are lots of RRM schemes to optimize theradio access network latency. Besides more complex QoS levels, better controlof the subscribers, like admission control mechanism, and mobility managementare included. For RLC layer, without header compression, 802.11p will easilybecome over-headed[16], like a large header with small data chunk. All in all,fitness for urban environment and optimized RRM scheme makes LTE a betterchoice.

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If taking security into consideration, compared with 802.11a, 802.11p omitsthe registration process aiming for decreasing latency. This, on one hand, pushesthe security application to run on OBU. And on the other hand, makes the sys-tem very vulnerable to falsified packet and replay attacks[17]. For packet falsifi-cation attacks, suppose an adversary in a platooning scenario who monitors theplatoon communication channel (Special for 802.11p device), [17] proves theycan analyze the content and falsify the platooning control command. This mayresult in collision and hijacking of the vehicles. In terms of replay attacks, ad-versary will grasp the platooning identifier through re-transmitting real packetand disguise itself as a platooning member to harm the system,

When bringing cost into focus, V2X communication network will not needadditional base-station construction cost in LTE scenario. The current LTEnetwork will be able to meet the needs in most use-cases. Besides, it also canuse RSU as a back-up network access in congestion area and perform verticalhandover. While for 802.11p, massive construction for RSUs are essential dueto its limited coverage area.

In a comparison study of the LTE and IEEE 802.11p protocol in V2Xnetwork[18], the general rule for both protocols is: in traffic denser area, packetdelay increases while the packet delivery ratio decreases. As for the vehiclespeed, the faster the car is driven, the less packet is likely to be delivered prop-erly. Comparing between the protocols, they proved LTE has better endurancetowards increasing traffic load and faster vehicles. But they did not find a ca-pacity for LTE network, and this would be the one of the contributions fromour thesis.

Based on the above reasons, we will study LTE network as it is a morepromising and capable technology. Its strength includes:

1. Better coverage for large area.

2. Smaller latency for data transmission.

3. Better privacy and information security.

4. Less construction cost.

2.4 Introduction to Real-time SimulationReal-time simulation refers to simulations when the pace of the simulation pro-cess is the same as the real-world time [20]. In computer simulation, time isoften represented as discrete series. Real-time simulation is necessary, whenthe outcome of the simulation is used to control a dynamic system, as it canbe expected in the case of remote vehicle operation. Real-time simulation de-mands time proceed in steps of equal time period. During each period, fourtasks should be accomplished [20]:

1. Refresh use-case environment and parse the input data to reset systemvariables.

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2. Simulate state changes until the end of the considered time period.

3. Exchange result with other nodes in the framework.

4. Proceed time to get synchronized with real-world time, wait for the nexttrigger when the time arrives.

First, the simulator is expected to update the input parameters to refreshthe whole scenario. As the configuration text are most likely in scripts, a parseris needed to read the simulation inputs. Second, according to the features andproperties of the simulation model, inputs are taken into formulas and equationsfor preliminary solution, and this could be the result from a sub-component of asystem. In the simulation process, external connected device could get involvedand they are considered as a separate simulation node. Every simulation nodeis sampled once in each simulation time-step[20]. After each simulation nodederived its own result, it will exchange with other simulation node (simulator,visualizer or external device) through Application Programming Interface (API,for parameters) or parsers (for scripts) to produce the final result. Finally, thesimulator waits for the next trigger as the real-word time continues, to proceedto the next simulation step.

In real-time simulation, a good example is simulating motor controlling inSimuLink[21] (Figure 3):

Parsers and Pre-defined conditions

Equation and Formular

External Connected Device

Time Trigger Frequency

Figure 3: Real-time Motor Controlling in SimuLink

In Figure 3, the use case is simulating a FPGA computing process to drivethe external motor. There are 3 nodes in the topology: the laptop which loadedwith controlling algorithm that simulating FPGA in real product, an controller

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which controls the motor and the motor which can feed its status back to laptopto illustrate in graph. User could manipulate different FPGA inputs to optimizethe parameters according to motor performance before massive production. Af-ter initialized (Switch motor on), the simulator continuously refresh the inputfor FPGA calculating motor velocity and take the result into controller scripts.The controller scripts will transfer the motor controlling signal to motor. Themotor should adjust its speed to the set velocity within refreshing period. Inthis process, if the refreshing period is too short, FPGA may not have enoughtime computing or let controller running the control scripts, so motors may failto adjust to demanding speed within the time. Thus, the real-time simulationcan not be constructed. To avoid this, we need involve chronological principle ofreal-time simulation[20]. According to chronological principle of real-time simu-lation: A real-time simulation is valid if and only if the real-time simulator canaccurately produce the internal variable and outputs within the same length oftime that its physical counterpart would. The simulation running time shouldbe shorter than the time-step of each simulation pace in reality.

Second, real-time simulation should be a time-invariant system or near-time-invariant[22] , which is for a transfer operator f from x(t) to y(t):

y(t) = f(x(t), t) (1)

If the system is time invariant:

y(t) = f(x(t), t) = f(x(t)) (2)

where system function is independent with time. And we could derive: forreal value t and time-invariant transfer y(t) = f(x(t)), any real value m:

y(mt) = f(x(mt)) (3)

This proves if the refreshing period is large enough, it will not affect thetransfer relation between input and output. In our example, it means wecould always derive the relation between input parameter and external motorspeed from simulation with any adequate refreshing period. The third featureis linearity[20]:For arbitrary real value α:

f(αx(t))

f(x(t))= α (4)

In actual engineering, most system are non-linear system, so small variationcould result in huge fluctuation in final result. If we consider simulation frame-work itself is the system, the variation factor is time and the variation amountis smaller than the time step, the system could be unstable in the final result.There are proposed solutions such as attaching time stamps to every periodicresult and interpolation sequence. The aim for all the approaches is to mini-mize the jitter of the simulator. The more jitter will be approached to zero orconstant, the less the system will suffer from timing errors.

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When co-simulating, it is always important to discuss the simulator’s per-formance. Since there are always trade-offs in capability, timing issues andaccuracy in real-time simulation, evaluation of a simulator should be made inall aspects. In [23], a state-of art study is made in simulator comparison oncomputing resource utilization perspective, especially on memory-usage, cpu-utilization and computation time under real-time scenario. Among all fouroptional simulators, (ns-3, Omnet++, SWAN and OPNET) authors compared,ns-3 was found as the best memory-efficient, least computation-time and leastcpu-utilized simulator. Their result claims more than 1400 nodes could be sim-ulated in network topology under real-time scenario. However, the limitation ofthe study is they only compare the processing period instead of result visual-izing period (Period of simulator demonstrating the result in figure, plain text,etc by real-time), which was measured much more time and memory consumingthan processing period.

Besides, the integration for multi-simulation-tools in real-time simulationcould be a source of problem. We studied the code base for LTE User PlaneSimulation Model for INET OMNeT++ (SimuLTE), Simulation of Vehicle-2-XCommunication (VSimRTI), etc on their mechanism for keep synchronizationwith network simulation component and traffic simulation component. Thereare two main-stream schemes to keep synchronization at the moment: time-triggering and event-triggering. Time triggering is: in the simulator, there isa centralized time table which control each simulation steps, and this timercould be adjust to real-world time for real-time features. According to [24], forcomplex algorithm and possible retries, it is the most fitting solution. Simi-larly, event triggering will trigger after the former simulation step completed,and record the triggering time. In [24], it has more flexibility in run-time, butsacrifices its algorithm complexity (Algorithm will be performed in run-time)and priority handling (before a step is done, no latter process is performed).According to [7], the traffic simulator will be time-triggered in our simulation,so the location information will only be available on every integer second. Thisdemands the integration framework should either be time-triggered at similarpace or have a good estimation on each simulation step’s time consumption toquery the traffic simulator at right time and keep updated. In network simu-lator, dynamic channel modeling and plotting is quite heavy-weight computingprocess which hard to estimate time consumption. Due to the uncertainty ofthe time consumption, event-triggered simulator may not query at exact integersecond and get outdated data. In purpose of simplicity, we tried to find a timetriggered solution.

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3 Related WorkThe development of V2X solutions is a hot topic in research and industry, anda variety of results are available that contribute to our work.

For simulator study, paper [26] indicate their contribution as developinga new simulation framework called VSimRTI. As a combination of driver be-havior simulation and network performance estimation, VSimRTI has been themainstream tool for 802.11p V2X service simulation. This paper illustrates 2scenarios, in urban and rural area in Berlin respectively, running V2X serviceand proving feasibility evaluation for service for moving vehicle. The drawbackis this study limits its scale in several streets and service type.

As for the traffic algorithm, through VSimRTI, the first contribution is incongestion avoidance field. In paper [27], authors would like to simulate a GSMrerouting algorithm to optimize traffic route and vehicle speed. Focused oncoupling between driving strategy and communication network, VSimRTI willbe very suitable to simulate the process like a car stops at traffic light, avoidcoverage blind point and traffic congestion and slows its speed when close topassengers. Through implementing Dijkstra routing algorithm, the conclusionis that if 40% of the cars are connected, 50% travel time reduction will be madeunder the speed of 50km/h. The limitation of this study is the radio path lossis over-simplified: shadowing fading not considered. While in urban scenario,Line-of-sight (LOS) channel is usually not available due to the shade of obstacles.

For radio network study in V2X scenario, in a later publication [28], dy-namic propagation model under multi-obstacles scenario is further discussed.In the simulation though VSimRTI, authors uses 802.11p to hold an EmergencyWarning Application (EWA) service where every message is important. Bydoing real-world measurements, they point out the simulation result using con-ventional fading model over-estimates the network performance. In their case,ray tracing algorithm is implemented in transmitter to identify the specific lo-cations of buildings in the surrounding environment to optimize the stochasticfading model. According to their result, their model is more close to real RSSIdistribution. However, they do not analyze multi-UE use case.

Paper [30] contributes to the integration of network simulation and trafficsimulation. They point out loading huge map information has always been theobstacle of building a real time V2X simulation. To solve this issue, they putforward a post-processing plug-in including parsing Open Street Map (OSM)files and map-matching. Parsing OSMs is to filter out the information whichis not related to the road and reduce loading and rerouting time, while mapmatching is to point out the exact location of a point according to the GPSposition. The research provides an optimized parser for traffic simulator to loadenvironment parameter with lower processing delay. We adopted their work inour implementation to shorten the time consumption when loading Kista map.

For implementation feasibility of a specific service, paper [29] uses UniversalMobile Telecommunications System (UMTS) with VSimRTI. They build a newtrace-based mobility model libtbus to simulate the EWA service with mobility.

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The result shows that the enhanced simulation model performs much more ac-curate to reality in the exemplary simulation area, with a result that 99% of allEWA messages arriving in less than 600 ms. Specific to vehicle tele-operationby cellular network, paper [31] does implementation through Ns-3 simulatorunder LTE network. The authors measured data freshness and delay to verifythe feasibility in urban scenario. By setting a series of QoS parameters, thenetwork performance and the connectivity of critical traffic is guaranteed. Theresult shows: in Ns-3 simulation of LTE, it is feasible to have V2X service inthe considered scenarios . By measuring the RSSI of the scenario, they mappedout the connectivity blind points. Beyond this previous work of simulating LTEnetwork with Ns-3, we provide a better resource allocation algorithm and inte-grate a traffic simulator. In addition, we also provide an estimate on the numberof vehicles that can be supported in the V2X system, and consider a re-routealgorithm to avoid blind points.

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4 Radio Resource Allocation for V2X applica-tions

To make the trade-offs between fairness, QoS demands and limited radio re-sources, a ’smart’ radio resource scheduling scheme needs to be implemented.Unlike round-robin, proportional-fair or best-effort, we need to find a balancebetween different flows for total throughput, latency and fairness among flows .Beyond discussion in 2.1, specifically for LTE use case, we should find a sched-uler which varies priority in time and channel variation. Three dynamic QoSschedulers are introduced in this chapter and to decide for the most fitting one.

4.1 Communication QoS in V2X ScenarioFor various services in V2X scenario, we will focus on the QoS waveform oftele-operation service in the scenario. For tele-operation control of the vehicle,there are 3 major streams in the scenario: command, positioning and live videostream. As they affect the remote operator, we consider these as ’critical traffic’.According to V2X scenario, the throughput of the critical traffic will be quitestable due to they mostly are periodically transferred traffic (periodic locationreport and control command, video steam with constant streaming speed andbuffer size). In comparison with the non-critical traffic, the tele-operation ser-vice will be less likely to generate bursty data, which in turn may allow morestable QoS. On the contrary, for the non-critical traffic, both the QoS require-ment and the bandwidth demand will change in time. Such non-critical trafficare basically data traffic generated by other application which do not serve fortele-operation service, they are simulated as the scenario’s background datatraffic.

As serving for tele-operation service, the critical traffic has the feature oflatency-sensitivity. Once the communication link can not realize the demand-ing HOL delay, it is crucial to make latency as their most concerned schedulingcriterion. But once in acceptable latency, seeking the best-effort strategy withmaximum throughput to guarantee the application performance becomes moreimportant. Non-critical traffic’s QoS majorly reflects as the throughout de-mands. Comparing with critical traffic, their QoS waveform are more likely tobe a best-effort solution on maximizing the total throughput. Between differentflows, fairness will be kept to ensure each flow could have the chance to getscheduled.

To keep such dynamic balance between demanding QoS, channel status, andfairness by time. A dynamic QoS aware scheduling algorithm will be essential forguaranteed communication for V2X use-case. For general dynamic QoS awarescheduling algorithm, we consider three options most adopted in IoT scenario:Token Bank Fair Queuing Scheduler (TBFQ), Priority Set Scheduler (PSS) andChannel and QoS Aware Scheduler (CQA).

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4.2 TBFQ Scheduler

TBFQ scheduler[33] is a QoS-aware scheduling algorithm based on the leaky-bucket mechanism[34]. Each flow j has its own token pool with size pj , packetarrival rate λj , token rate µj and loan/borrow budget lj and bj . Ej is thecurrent balance status of flow j which could be negative. Also, all flows have ashared token-bank B which will collect the overflowed tokens.

The data packet will only be transferred once a token is attached. If µj > λjfor long, pj will overflow the additional token to B until it reaches lj , and Ejwill gradually increase in this period. Once a sudden burst data flow is coming,making µj < λj , flow will start to borrow tokens from B if Ej < bj . Once thereare several flows borrowing at the same time, their priority will be given as Ej

µj,

which means the more data-rate a flow lent to others beforehand, the better itwill be served in time of needs. This is a best-effort mechanism, which is morelikely to achieve maximum throughput regardless of priority.

4.3 PSS SchedulerPSS scheduler[33] is QoS-aware, Frequency division-time division (FD-TD) jointscheduling algorithm for LTE MAC layer. It controls priority through TBR. Ingeneral, flows transmitting lower than TBR are prioritized than flows transmit-ting higher than TBR. The scheduler contains two stages. The first stage iscluster division where divide user with flows lower than TBR and higher thanTBR in TD. For user lower than TBR, a best effort strategy (BET) is used togain throughput. For user high than TBR, a Proportional Fair (PF) strategy isused to keep fairness in FD respectively.

For UE transmitting slower than TBR, suppose flow k (as representationof resource blocks cluster) with user j, Tj(t) as moving average throughput,Rj(k, t) as estimated wide band throughput. its priority matrix in TD[35] as aBET-like form:

ˆi(k, t)BET = argmax(1

Tj(t)) (5)

From above equation, we could infer when Tj(t) < TBR, the smaller ofTj(t), the more likely the flow will be prioritized. If TBR is infinity, PSS isequivalent to BET scheduling. For UE transmitting faster than TBR:

ˆi(k, t)PF = argmax(Rj(k, t)

Tj(t)) (6)

Tj(t) > TBR shows a PF way of scheduling, as judging how far the ongoingaverage throughput is from the estimate maximum value.

After the flows are classified into two groups in TD scheduler, for FD, Nmuxflows with highest priority will be selected to FD scheduler. two optional al-gorithms are provided: PF and Carrier over Interference to Average scheduler

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(CoItA):

ˆMschk[k, n] = argmax(Rj(k, t)

Tschj(t)) (7)

ˆMCoIk[k, n] = argmax(CoI(j, k)∑NRBG

k=0 CoI(j, k)) (8)

Tschj(t) can be approximate as the throughput of user j, CoI(j, k) is anapproximation of SINR of user j of resource block group k. Both 2 algo-rithms use the same fairness methodology, as equation 7 schedules the flowwhose throughput could be most optimized and 8 schdules the UE whose RSSIcould be most optimized. The FD scheduler will select the maximum productof matrix (Msch,MCoI) to guarantee the throughput of lower quality flows inweaker connection to reaching TBR.

In all, PSS will prioritize flows with good quality and SINR but cannot reachTBR as a BET strategy. The further it is away from TBR but could be opti-mized, the better it will be scheduled.

4.4 CQA Scheduler

For CQA scheduling[33], it is based on joint frequency domain (FD) and timedomain (TD) scheduling. It is optimized for latency sensitive traffic like Voiceover Internet Protocol (VoIP)[36]. Firstly, in TD phase, the scheduler will groupthe user by their HOL Delay. User with larger HOL delay will be considered asimportant users, the grouping matrix will be:

mjtd(t) = d

djHOL(t)

ge (9)

Here dHOL is the Head-of-Line latency, j is the flow sequence, t is the trans-mission time interval (TTI), g is number of resource blocks contained by aresource block group. When g is smaller, a more detailed scheduling will beimplemented, while the complexity and time consumption will be larger.Secondly, for each resource block group k = 1...K, in FD phase:

m(k,j)fd (t) = djHOL(t) ∗m

jGBR(t) ∗m

k,jca (t) (10)

Here mjGBR(t) is the GBR weighting matrix, it is calculated as:

mjGBR(t) =

GBRj

Rj(t)=

GBRj

(1− α) ∗Rj(t− 1) + α ∗ rj(t)(11)

Rj(t) is the moving average throughput of flow j. If flow j is critical traffic,GBRj > 0, makes it prioritized. Else if flow j is non-critical tra ffic, GBRj = 0,so mj

GBR(t) = 0, so it will not be scheduled first and follows a round-robin

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method.

When we compare two critical traffic, such as video flow and command flow,we should consider mk,j

ca (t) more in this case. For a throughput lower than10Mbit/s , we neglect the frequency selective effect and consider a flat-fadingchannel over radio resource groups. For a flat fading channel, we have the pro-portional fair matrix:

mk,jca (t) = mk,j

pf (t) (12)

mk,jpf (t) =

R(k,j)e

Rj(t)(13)

R(k,j)e is the estimated achievable throughput of flow j in cluster k. This pa-

rameter reflects CQI value: for a fixed UE, R(k,j)e is a constant due to its fading

environment should be the same and channel quality should not be concernedwith traffic type. For command flow, Rj(t) is relatively smaller than video flow.Besides, once the packet starts to lose, Rj(t) will dramatically decrease as thereare few packets in one TTI. Thus, command flow will be prioritized comparedto video flow.

In all, CQA scheduler follows the principle of prioritizing flows with worstlatency, prioritizing critical traffic, priortizing traffic which has good channelcondition and prioritizing traffic with small throughput demands.

4.5 ConclusionAll three schedulers are implementing a QoS-aware algorithm. Compared toCQA, TBFQ performs better in dealing with packet burst and PSS focuses onguaranteeing the best link to reach TBR in the scenario. While they both ig-nored latency, and throughput becomes the only factor which is concerned inpriority handling. In our scenario, latency is a critical parameter and we areaiming for a dynamic QoS allocation scheme. Above all, we take CQA scheduleras the optimal solution and will implement such scheduler into our frameworkin the following chapters.

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5 Integration Framework and Simulation ToolsWe developed a simulation test-bed consisting of three components: traffic sim-ulator which provides mobility model in the urban scenario, network simulatorincluding the simulation of the wireless environment and the network proto-col stacks, and simulation framework which integrates network simulator withtraffic simulator. Additional tools in the system include parsers for reading con-figuration, visualizer to produce result and map database as the storage of theenvironment parameters. Our focus will be on network simulator and integra-tion framework. The selection of the traffic simulator is discussed in [7].

5.1 Cellular Network Simulator SelectionThere have been a large variety of choices on simulators. Our demand is seek-ing an option which is open-source, LTE supported and capable of co-simulatingwith the traffic simulator. It should cover PHY, MAC and RLC layers, fit formobility model 4 for a large scale and could produce visualized results. NetworkSimulator-3 (Ns-3), Objective Modular Network Testbed in C++ (Omnet++)and VSimRTI network Simulator (SNS) are often used network simulators withdifferent strengths and weaknesses. Through our study, we would like to offer astate-of-art comparison concerning the three considered simulators.

Omnet++ [37] is a discrete-event-sequence-based simulator which was de-veloped in 1997 and was specially designed for communication network simu-lation. It can be integrated with other traffic simulators through Vehicle inNetwork simulator API (Veins) to co-simulate or work single and produce vi-sualized results and reports. The major strength of Omnet++ is the mobilityintegration. It provides user options both using their default mobility routeshapes (like triangle or rectangle route) and manually user-defined models. Themajor weakness for Omnet++ is that it is an event-triggered simulator. Asmentioned in Chapter 3, for real-time simulation, this is very inconvenient forco-simulating with time-triggering traffic simulator in terms of synchronization.Another drawback is that Omnet++ is not originally developed to support LTE.In terms of 802.11p, INET, an add-on model, was integrated to provide protocolstack. As a open-source framework, a add-on package called SimuLTE is devel-oped to complement the limited LTE features, as it will be further discussed in5.2.1. For PHY layers, fading and propagation estimation is only performed in802.11p’s frequency band (5.9GHz) instead of LTE frequency band. This causesinaccuracy in coverage estimation and dramatically underestimates the result.

SNS[26] is developed by Daimler Center for Automotive Information Tech-nology Innovations as an integrated light-weight network simulator for VSim-RTI. Since it was specially designed for VANET, it can be well-integrated intothe simulation framework and has very good mobility support on 802.11p. Itreads through the external API of traffic simulator to timely update the locationof the vehicles. As it imports route and environment parameters from OSM, the

4Mobility model: a component in LTE protocol stack which contains traces of UEs. In alarge scale, for multi-UE scenario, trace will become too complex to be developed.

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users could define their own rerouting strategy based on specific urban scenar-ios. Another strength is that SNS is capable for simulating multi-hoop routing.Nevertheless, it has shortcomings in its capability. Besides lacking support forLTE protocol stacks, all simulation is performed under 802.11p standard. Inchannel modeling, any NLOS channel is considered as in-connectivity area. ForLTE which is supposed to cover a large area, shadowing will definitely existin urban scenario with high-rise buildings . Moreover, it doesn’t support anyRRM scheme in MAC layer, so a critical service can only be guaranteed withperfect RSSI and adequate available resource blocks. In addition, the simulationcan’t work in real-time mode with LTE scenario. Since while installing protocolstacks, the simulator is hard-coded not to differentiate receiver and sender, as802.11p installs the same protocol stack on both nodes. This in LTE is obviousnot the case.

LTE-EPC Network Simulator (LENA)[38] of Ns-3 is an external LTE andEPC package for Ns-3 simulator. Comparing with the previous choices, it per-forms better in many aspects. In term of integration with simulation framework,LENA is an internal package of Ns-3, so it could fit seamlessly. For LTE capa-bility, it provides a full-layer model of LTE network with focus on PHY, MACand RLC layer in both access network and core network. Considering mobility,its propagation model and fading pattern take account of the relative locationbetween obstacles and eNBs. With an external emulation5 access, it providesAPI for remote physical client to access and control a simulated vehicle in thescenario. Comparing with Omnet++, LENA is a time-triggering simulator,making it easier to build a real-time co-simulation framework.

All in all, after comparing all solutions, LENA will be the optimal solutionfor our scenario. While it also has some drawbacks. As it omits the MME nodein the EPC, it will be hard to simulate the network in full topology. Also, itneeds traffic simulator to produce the whole trace before the network simula-tion starts. According to chronological principle of real-time simulation, oncethe time cost of generating and reading traces from the traffic simulator exceedthe refreshing gap of the system (50ms), this will be unfeasible to create a real-time implementation. In terms of the simulating platform, LENA shares theproblem as Ns-3, including lacking support for IPv6 with NS-3 and requiringall Ues to be constructed and subscribe to respective eNB before the simulationbegins. These problems rise in the integration part and will be analyzed morein depth.

5.2 Integration Framework SelectionThere are also demands for the integration framework which connects the traf-fic simulator with the network simulator. The framework should include thefollowing features:

1. The framework should be able to work in emulation or real-time simulation5In LENA, real-time simulation is included in emulation, emulation also includes real-time

communication with external devices which is not simulated

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mode.

2. The framework should include (in network simulator or additional pack-ages) as-detailed-as-possible LTE architecture.

3. The framework should be capable of importing routing information fromtraffic simulator into the network simulator.

4. The framework should import environment data to construct an urbanwireless scenario.

Due to the above demands, we compare three most popular frameworks fromall: Ns2Mobility, VeinsLTE and VSimRTI. Other options including Open Sim-ulation Platform for ITS Services (iTETRIS) are ignored due to the absence ofdocumentation.

5.2.1 VeinsLTE

VeinsLTE[15] is an package summary integrate for Omnet++ for heterogeneousvehicular network based on IEEE 802.1p and LTE. It consists of several compo-nents as separate packages to co-simulate. INET, SimuLTE and Veins are theadopted packages for LTE V2X simulation in this framework. Its work-flow isshown in Figure 4:

We can infer from the figure that VeinsLTE, inherent from Omnet++ , is anevent-triggered simulation framework. Each simulation step is proceeded frommobility model (vehicle position change) to RLC layer (data block assignment).In this process, the lower layer feeds its result (mobility, RSSI model, packetloss ration) to upper layer to drive the next step. After these, it will trigger thetimer to record the finishing time and go for next round.

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Figure 4: VeinsLTE Workflow

Revising our feature-of-demand, VeinsLTE is coping well with the mobilityand mapping issues due to the attendance of Veins and Omnet++ mentionedabove. However, as mention above and Chapter 3, due to the event-triggeredfeature, it is unfeasible to build a realtime integration under VeinsLTE. Beyondthis, SimuLTE, as a model included in the framework, has limitations in itscapability on simulating radio network. After studying the code, there are fourmajor drawbacks. First is the control plane has not been developed, so RLClayer is not fully simulated. Second is no radio bearer implemented, makingit impossible to realize QoS aware scheduling. Third is handover is not im-plemented, user will not be able to simulate any scenario more than one cell’scoverage. Forth is due to it only supports access network, so protocol stack incore network has not been implemented.

5.2.2 VSimRTI

VSimRTI[39] is a simulator coupling framework for V2X simulation. It inte-grates traffic simulator, communication network simulator and application sim-ulator all together into the same framework. VSimRTI adopts the fundamentalconcept of High Level Architecture (HLA) to communicate with the simulators.These simulator are defined as simulation node in HLA topology. Through theHLA structure, there is a master host which controls all simulations nodes in thescenario. To start with, it creates APIs called ambassadors to each simulationnode. The ambassadors are used to transfer commands and query status re-

32

spectively for each simulator to keep real-time status and synchronization whileco-simulating. This is beneficial for further extension of the framework as it willonly needs to create new ambassadors to include new simulators.

VSimRTI

Server Ns-3 Installer

Read Updated Node

CMD_CONF_RADIO

Create Enb, Ue Nodes

Open emulation port

Attach NetDevice

Initialize Simulation Time

Node ID, Pos.X, Pos.Y

Mobility installer

SUMO

(Location Information)ContinousUpdatewithCloudServer

IP arrangement

App Installation

NetDevice

Container

CMD_ADVANCE_TIME

Node

Container

Attach

Enb to Ues

Assign Bearer

Xml Settings:Base station settings

Set Attribute

Initialize LTE helper

readConfigurationMessage

LOOP

CMD_UPDATE_NODE

CMD_ENDSimulation::Stop()

Command sequence

Real Time Simulation flow chart

Figure 5: VSimRTI Workflow

As shown in figure 5, for the VSimRTI work-flow, we can roughly definethe simulation process into 3 stages: preparation stage, processing stage andtermination stage. In preparation stage, CMD_UPDATE_NODE is trans-ferred to all the ambassadors for seeking potential nodes in the traffic sim-ulator. The mapping model will try to attach the traffic node to networknode respectively and install mobility model for the network. Once a nodeis found, CMD_CONF_RADIO will be delivered. Admission control andeNB attachment will be done by the network simulator. After the UE sub-scribes, the processing stage will begin and all the ambassadors will receiveCMD_ADV ANCE_TIME to proceed the ’clock time’ for all simulators inthe framework until the next node is discovered. When the ’clock time’ reaches ittermination, all ambassadors will be closed by VSimRTI through CMD_END

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and terminate all simulators for termination stage.

While keeping the framework extendable, HLA methodology also brings un-desired features. Its limitation is that it force every simulator in the frameworktriggered by the same time period, while they could be in different ’clock time’.In preparation stage, although the the ’clock time’ in master node is 0, in sim-ulators the time would already elapse in time-steps pre-defined by VSimRTI.Hence, for protocols with long preparation stage (Like LTE), framework willlost synchronization in processing stage. Moreover, VSimRTI put more focuson MAC and RLC layer, so the fading features of the urban area are ignored inthe simulation. Lastly, in network simulation, the client should be terminatedlater than server due to latency and possible re-transmission. Additional packetloss is caused due to sudden termination when CMD_END is sent.

5.2.3 Ns2mobility Package

Figure 6: NS2mobility Workflow

As shown in figure 6, NS2mobility can directly read the trace file of the vehiclesthrough parsers. As it mostly lies in Ns-3, LTE protocol stack will be loaded byLENA and mobility model will be installed on LENA’s communication nodesby NS2mobility. By this means, channel modeling will be dynamic while read-ing UE’s mobility status from NS2mobility and this will affect all layers in theLTE network by time. Through other integrated packages, we could simulatea fading pattern of the demanding area based on OSM’s building environmentinformation. In addition, NS2mobility provides emulation mode which could

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allow cloud applications outside the simulator to access the UEs simulated in-side the simulator. It’s performance bottle-neck is the parser’s performance.For a large scenario, serializing and parsing the trace file is a heavy load whichcould cost seconds. We optimized a xml parser to save the time cost, as we willillustrate in Chapter 6. Overall, the comparison result for three frameworks isshown in Table 3:

Table 3: Comparison of Current Mainstream V2X Simulation Frameworks

Category VeinsLTE VSimRTI NS2mobilityTraffic Simulator SUMO 0.26 SUMO 0.28 SUMO 0.28

Network Simulator Omnet++ 4.6 Omnet++ 5, SNS, Ns-3 Ns-3Simulating Protocol LTE 802,11p LTE

Simulation triggering Event-triggering Time-triggering Time-triggeringReal-Time mode With add-ons No Yes

PHY layerCA Support 2500 Null TD 2600, TD 2500, 2100

Spectrum Tech FDD Null TDD and FDDMobility model Plot by user Import from OSM Import from OSMChannel model Rayleigh, Rician Freespace with delays Shadowing and PenetrationAntenna model omni, dipole user-defined user-defined

MIMO No No Yes

MAC layerRadio Bearer No Limited Yes

Handover Vertical Only Yes YesScheduling Round Robin QoS aware QoS Aware

RLC layerIP addressing Access network only Under same Gateway YesControl plane No No YesData stream Dummy Traffic Dummy Traffic Encoded Data

5.2.4 Conclusion

As there are both drawbacks for VeinsLTE and VSimRTI in implementation,we select NS2mobility with LENA as our preferred solution, based on followingstrength:

• Can work in emulation/real-time simulation mode.

• Most completed model on LTE protocol stack in both access network andcore network.

• Can import mobility trace from map database, suitable for complex vehicletrace and routing.

• Channel modeling is provided with self-adjusted fading pattern in all fre-quency bands and scenarios.

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6 System Implementation

6.1 System topologyOur simulation on V2X communication network basically includes four aspects:Mobility, PHY layer, MAC layer and RLC layer. Based on the analysis in Chap-ter 5, we will use Ns2mobility and LENA. The topology is illustrated as Figure 7:

Ue 1

Ue 2

eNB

Router & Gate way

P2P link

Remote host

PGWSGW

MME

EPC

Security ComponetMobility handlingBearer Control

Mobility AnchoringIP allocationPacket filtering

S1-MME

External Access

1.0.0.3

1.0.0.2

1.0.0.11.0.0.x

7.0.0.1

7.0.0.2

7.0.0.3

Fading model

Access Network

Figure 7: Implementation Topology

In Figure 7, as discussed in Chapter 2, our implementation can be dividedinto three parts. Access network includes the vehicles as the Ues in V2X scenarioand common eNB to cover the whole area. As the total size of Kista is 2.15Km2 area, we assume the area could be covered in one cell with identical beam-forming. The vehicles, as the mobility model of Ues, are driven in 60 km/h atfull speed and will stop or slow down at traffic light, bus stops and congestedarea. In EPC, as discussed in Chapter 5, MME will not be constructed as asingle node in core network but integrated with SGW due to the limitation ofLENA. As a unique feature inherent from Ns-3, We set up a P2P link to remotehost for external network to access the simulated scenario.

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6.2 Mobility ModelTo build the mobility model for network simulator, the trace of the vehicles willbe generated by OSM and route by SUMO. OSM is a non-profit database toprovide free geographic data. Through this database, we can filter out streets,buildings, RSUs and even grassland and forest. SUMO is the traffic simulatorwe adopt, we need to parse the OSM data into html format routing informationand SUMO will provide us with optimized route. Concerning the routing algo-rithm, referring to Y.Wang’s publications[7], a Dijkstra route-seeking algorithmis implemented.

The routing process contains several steps[7], as shown in Figure 8. First, weneed to import environment information from OSM. Due to OSM is a large database which contains much more than we need, what we need to first filter outthe demanding information including bus stops, RSUs, office buildings, mallsand open-area (grassland, parking-lot and public square). To reach this goal,we adopt Java OSM and MapEdit to filter out needed indexes in OSM files.Another reason for wiping off the spared data is compressing the file size forNS2mobility to enhance the performance. After this, SUMO will parse the .osmdata-base into .xml configuration file (using the netconvert) which contains thetraffic network of the map. We can not import all the vehicles in the scenarioautomatically through reading from parser or OSM, since they do not containany traffic information, so users are expected to define every vehicle manuallyin SUMO.

Filter out details(JOSM)

Extract map from OSM

(file).osm.xml

Define OSM highway types

(file).typ.xml

Get traffic network(netconvert)

Run

netconvert

(file).net.xmlImport geometry

shapes

Run

Polyconvert(file).poly.xml

Configure typemap

Figure 8: SUMO Working Flow

As the traffic is varying by time, ACTIV ITY GEN is a package adopted tocreate a realistic scenario of the case. It will vary the traffic load of the roadnetwork by time instead of an uniform distribution. Another implemented add-on is DuaRouter, which will do the dynamic routing to avoid congestion area.After these steps, a trace file will be produced for NS2mobility as the movingpath of the vehicle.

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In the process of importing mobility to network simulator, there is no exist-ing parser to import the geometry of the buildings in the area. We developedourselves a parser adapting libxml2[43] library.

6.3 PHY LayerComparing with other cities, Kista is an urban area with its own features. It ishard to use an universal fading model. Comparing with Manhattan’s model[42],coverage will not be limited to street grids as antenna is higher than most of itssurroundings. Comparing with COST 231-Walfish-Ikegami model[42], Kista haswider roads and owns more open-area and most area could be served with LOSchannel with little shadowing. For very precise simulations, one would need thomeasure and model the fading pattern in the area, however, this is out of thescope of this thesis. Instead, we construct the following model, with parametersgiven in Table 4:

Table 4: PHY Layer parameters

Parameter ValueeNB Tx 60dBm

eNB Location (880, 4100)eNB Antenna amplifier 0db

eNB Noise Figure 5dBeNB Height 10 m

UE Antenna amplifier 0dbUE Height 1 m

UE Noise Figure 5dBDownLink Earfcn 2750

Uplink Earfcn 20750Carrier Frequency 2600 MHz

Spectrum Channel Type Multi-model Spectrum Channel

1. The buildings only have one room regardless of size.

2. All buildings have concrete walls with windows. For indoor receiver (notconcerned in our research, for RSSI map’s consistency), penetration costis constant. For outdoor receiver, penetration cost follows log-normal-distribution.

3. No access point inside building is considered, the only transmitter is atoutdoors. Handover between protocols is ignored.

4. Buildings’ shape are modeled as their external rectangular (refer Chapter8). Grassland, parking-lot and square are regarded as open area.

To simulate, the NS2mobility will first attach the mobility trace of a vehicleto a respective simulated UE. The trace will record the location of vehicles by

38

second. The parser will be initialized next and read the environment profile inOSM database. It will classify the usage type of the land and filter out the build-ing areas. Then it will calculate the external rectangular for each building andmodel it as obstacles in the scenario. The simulator will identify channel statusand availability of LOS path before EPC starts. When the simulation starts,the UE will seek its attached eNB and feedback Channel Quality Index (CQI).The hybrid building propagation model will be activated afterwards accordingto the scenario with modeled buildings included. It integrates Okumura-Hata,Itu-R1411, Itu-R1238 and external wall loss into one propagation model. Byvarying the location of UE, it will consider the surrounding buildings for thechannel model and adopt different algorithm to calculate the path loss6.

6.4 MAC layerBased on PHY layer’s resource block, MAC layer will construct transport blockswith MAC headers. After the simulation starts, each flow will be assigned a ra-dio bearer for priority handing and QoS control. Scheduling will take effectbased on both QCI parameter, GBR and CQI from PHY layer. A series ofassigned parameters is given in Table 5:

Table 5: MAC Layer parameters

Parameter ValueAdmission Control Always Yes

MAC Header Bearer tag onlyResource Scheduling Channel and QoS Aware Scheduler

P2P Throughput 100GbpsP2P Delay 10ms

Total Delay Demand 50msPLR Less than 1%

In our use-case, each vehicle has four different data streams: vehicle com-mand flow, position flow, video flow and dummy 7 with different priorities andthroughput demands. As discussed in Chapter 2, initializing a radio bearerdemands for the prerequisites of initializing EPC model, and each bearer willbe activated in the UE-attaching process. The bearer assigning will be donethrough assigning specific TFT to destination port respectively. In our sce-nario, the radio resource will be allocated as given in Table 6[44]:

In case part of a UE’s flow is rejected from service subscription, the admis-sion control strategy is fixed as ’always Yes’ to keep every UE’s critical trafficgets served [45]. Beyond the critical traffic, there still exist background dummytraffic created by common LTE users which should be scheduled if possible. We

6For illustration, we give the pseudo code in the Appendix.7Dummy traffic: dummy traffic is a traffic generation model. In this model, a block is filled

with dummy data, usually zeros or blanks. In our case, we use dummy traffic to simulatebackground traffic flow generated by user in the same scenario from various unrelated (forexample: passengers using youtube) service.

39

Table 6: Radio Resource allocation in MAC Layer

Flow Type Bearer Type GBR PriorityCommand GBR_CONV_VOICE 300kbps 1Positioning NGBR_CONV_VOICE 80bps 2

Video GBR_VIDEO_TCP_DEFAULT 10Mbps 8Dummy GBR_VIDEO_TCP_DEFAULT 0Mbps 9

adopt CQA scheduling algorithm[36] in MME to ensure reliable connectivity forcritical stream as well as reaching a best-effort solution for non-critical streamas we discussed in Chapter 4.

6.5 RLC layerIn the RLC layer, our major concern is IP addressing and setting up the appli-cation in our network. SGW will bridge access network to core network togetherby transferring the control message. PGW will be responsible for exposing themto external network through appropriate re-framing and packet filtering accord-ing to TFT for various bearer. Concerning the ’client-server’ pattern for eachservice in the V2X scenario, the principle is the clients are activated after theservers and terminated before the servers stopping listening. This is due topossible latency and re-transmission budget. Most flows (command, position,dummy) will be assigned a UDP server and client on GBR with dummy contentfor simplicity. While for the video stream, as we would like to take encod-ing and decoding latency into account, we involved a new package evalvid[46].evalvid could simulate H.263, MPEG-4, H.264 codecs and we could measurethe latency and jitter in the codec altogether. In our case, we will code thevideo in the MPEG-4 format. Another issue, in the RLC layer, is assigningmaximum transmit unit in the P2P link, here we assign 1500 bits according toEthernet V2 framing in IPv4, as the most common case in the IEEE Standardfor Ethernet[47].

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7 EvaluationWe will evaluate the simulation process and result from three aspects: simula-tor capability, quality of service and scenario capacity . Simulation capabilityevaluation will focus on the process of simulating LTE V2X tele-operation ser-vice with selected simulation framework. We will evaluate the quality of servicecapabilities of the system by measuring the expected delay and packet loss ratefor one single UE. Finally, we will evaluate the system capacity, that is, themaximum number of UE that can be served with the required quality of ser-vice.

7.1 Simulator CapabilityIn Figure 9, we sum up our simulated scenario by layer. Each layer could bothwork as a single simulator and work as a framework through APIs and parsers.In OSM database, we filter out the required area and simplify lanes (filter outbicycle lanes and walking path) and edges (clear environment information, liketrees alongside the road, which is not related to the road) for a smaller file. Tosimulate real-time traffic scenario, we produce mobility trace to simulate thetrack of the vehicles. For mobility trace, we simulate all 15 bus routes in Stor-stockholm Lokaltrafik (SL) ’s database passing through Kista. To bridge eachnode’s mobility to radio network, we attach the bus’ trace to respective com-munication node, and import obstacle information (mainly buildings) throughparser into LENA. The parser will refresh in a certain period of time to updatethe location or environment change.

In PHY layer, according to the environment information from OSM andtraffic simulator, we conclude in the north part of Kista, due to lots of high-risebuildings and offices, the RSSI becomes weak. It is also obvious that behindhigh-rise buildings there is a obvious shadowing effect which forms up the darkblue dot (low RSSI) after a bright red area (high RSSI). As we could see in themap, major streets are mostly in good coverage and we will select one street tostudy in next Chapter.

This result reflects to MAC layer where the video stream becomes unstablewhen the bus drives through the dark blue area in north-east Kista. In this case,the traffic simulator will reroute the bus to a new path if connectivity is uncer-tain. As when driving into dark blue zones, the transferred packets (illustrateas arrows in Figure 10) will decrease and LENA will perform a hoop-by-hooptable to show where the packet is dropped. In contrast, in south-east Kista,it will be better connectivity due to more open areas. In such case, a steadystream (as shown in Figure 10) can be observed.

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OSM environment data

Channel Modeling

Good Coverage Area

Bad Coverage Area

Relatively Less Crowded

Major Streets

Majo

r Stre

ets

Major S

treets

Crowded Area

Mobility Trace

PHY Layer

UDP Stream

MAC & RLC Layer

Scheduling

Resource Allocation

Enb

Remote hostEPC

Ue2Ue1

Filtering out irrelevant nodes and buildings

Select demanding area

Figure 9: Simulator Performance

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7.2 Quality of ServiceIn Chapter 6, we presented the communication requirements for remote driving:less than 50ms delay, constant jitter and 99% guaranteed communication. Inthis scenario, we will simulate only one bus route to verify the feasibility ofhaving V2X service in the Kista scenario with these given QoS demands.

(a) Command Flow Delay with 10MbitDummy Traffic

(b) Video Flow Delay with 10Mbit DummyTraffic

Figure 10: Delay for Critical Stream with 10Mbit Background Traffic

Figure 10 shows the per packet delay as a function of time, that is, as the UEmoves in the area, for the command flow, starting at 10s, and for the video flow,with 10Mbits background traffic. On Figure 10.a, we can observe that commandflow, as having relatively low throughput and using the highest priority GBRbearer, will be guaranteed in both delay and jitter in almost all areas in fadingmap. This proves our derivation in Chapter 4. While for video, on Figure 10.b,as it is assigned with relatively low priority, would be affected by the flow ofcommands and the UE position. So, it could have more fluctuation since othercritical traffic begin to transfer and it is no longer highest prioritized. While itis still in acceptable latency which less than 50ms and relatively small jitter. Interms of dummy traffic (starts from 0.01s), it will not affect the transmission ofvideo flow due to its flow is attached to the lowest priority non-GBR bearer.

Another source of fluctuation is the changing channel quality. This is re-flected in Figure 11. This figure shows the PLR of video stream changing bythe simulation time while the vehicle is moving in urban area (as shown in Fig-ure 12). Due to the changing distance between vehicle and eNB and relativelocation between obstacles and receiver, the Packet loss Ratio (PLR) will fluc-tuate due to changing received signal strength. Since it is still coming close tothe transmitter, the PLR is still in decreasing trend by time. The reason canbe split into two aspects: the total available RB numbers and scheduled RBsper flow.

From channel capacity perspective, if we have a increasing CQI feedbackfrom UE. eNB can transfer more bits in a symbol using optimized modulation.Thus, the resource block per packet needed is decreased, and this could ease the

43

shortage of RBs. For throughput-intensive service, more data packet could bescheduled due to more available RBs and lead to decreasing PLR.

From CQA Scheduler perspective, as the location of the transmitting an-tenna is located at (880, 4100) (Figure 12), the moving vehicle is from (188.35,4454.56) to (968.36, 4094.65). In this process, as vehicle coming closer to theantenna, connectivity will be more guaranteed and CQI will increase. As theCQI is rising, Rkk,j as maximum achievable throughput will increase. Accord-ing equation (13), with a slowly changing moving average throughput (Rj) suchflow will be more scheduled.

(a) Video Flow PLR with 1Mbit DummyTraffic

(b) Video Flow PLR with 10Mbit DummyTraffic

Figure 11: Packet Loss Ratio for Critical stream in 10Mbit and 1Mbit Back-ground Traffic

Figure 13 shows the comparison of PLR of dummy packets under differentthroughput by simulation time. Comparing Figure 13 (a) with Figure 11(a),we could prove that flow with GBR bearer is prioritized. Comparing Figure 13(b) with Figure 11(b), from packet loss perspective, we conclude only dummytraffic itself will lose more packets when RB is in shortage. Yet, comparing withFigure 11(a) and Figure 11(b), there is no changes in the video flow which isattached to GBR bearer. This is due to NGBR-bearer will only be served in thecase where GBR-bearer is fully served. So comparing Figure 13 and Figure 12,despite the varying change of dummy traffic, its GBR = 0, so the GBR weightis a constant 0. Thus, varying dummy traffic will not make any difference inFigure 12, but changes the PLR in Figure 13.

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Tx

Vehicle Destination

(968.36,4094.65)

Vehicle Set-Off

(188.35,4454.56)

(880,4100)

Figure 12: Test route from (188.35, 4454.56) to (968.36, 4094.65)

(a) Dummy Flow PLR with 1Mbit DummyTraffic

(b) Dummy Flow PLR with 10MbitDummy Traffic

Figure 13: Packet Loss Ratio for non-Critical stream with 10Mbit and 1MbitBackground Traffic

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7.3 Scenario Capacity

Figure 14: Capacity for Bus Route per eNB

As we proved we can satisfy the QoS demand of the V2X service. Let us nowdefine the capacity of scenario as: the maximum number of vehicles served by abase station with guaranteed QoS. For a scenario with crowded vehicle traffic,it is valuable if we could have the maximum capacity of Kista. In this scenario,we simulate the cases from one bus to nine buses. All nine optional buses follownine realistic bus routes through Kista. As the admission control strategy is’Always Yes’: all UEs will be subscribed and served.

In figure 14, we illustrate the relation between the number of vehicle in thescenario, worst PLR of all the vehicles and the vehicles which have more than1% command packet loss. We could observe the turning point of the line graphis when we have six vehicles in the scenario, the worst connected vehicle beginto lost packets. And from 7th vehicle resisted in the scenario, the most pri-oritized stream is affected. The conclusion shown in Figure 14 is that if thenumber of vehicles is more than six per eNB, new subscriber will start to ruinthe entire network. Due to the CQA scheduler mechanism in Chapter 4, onceseveral streams with same priority can not be all served, the ones with betterCQI and longer HOL delay will be served. To optimize the wireless capacityusage, one solution could be reroute the vehicle to better connected area. Anrerouting strategy is made in Y. Wang’s ongoing publication [7] as a option.

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8 ConclusionIn this section, we evaluate our work based on thesis objectives. We will summa-rize our contributions in V2X simulation according to the objectives in Chapter2. For features we failed to realize, possible inaccuracy source and other furtherresearch directions, we will provide as the our studies’ limitation and futurework from every simulation aspect. Lastly, we will cover our whole thesis re-search value in conclusion part.

8.1 Review of Objectives and ResultsThesis Objectives:

1. Do a state-of-art research on available network simulators, provide a com-parison result and build a realistic V2X simulation scenario in the city ofStockholm.

2. Select appropriate integration framework based on:

• The capability of the supported network simulators. In particular,the network simulator should be able to simulate the full LTE stack.

• The real-time simulation capability of the integrated framework.

3. Implement the simulation through selected simulator and integration frame-work.

4. Through the simulation case, verify the feasibility of applying LTE in V2X.

5. Examine the maximum capacity of the scenario based on a realistic trafficflow route.

For objective 1, we did a comparison on the existing network simulators.Based on the comparison, we built the scenario with traffic information andbuilding environment. We provided parsers and APIs for integration frameworkto connect traffic scenario with radio network. Theoretically, we compared threedynamic scheduling algorithms and developed environment-specified channelmodel for defined scenario. We implemented these modules in our simulationfor more accurate result and better performance.

For objective 2, It is the first contribution of simulator which combines LTEnetwork with mobility simulator in a realistic urban scenario. Its strength showsin its extendable architecture and accessible for remote host and external appli-cations for the simulate network. For visualization, each layer in the frameworkadopts separate visualized tools, user can either simulate the case in a separatelayer, or combines all the simulator together to make a completed V2X simula-tion. However, due to the real-time component’s code is pre-compiled and notopen-source , the LTE network part was built as off-line simulation instead ofthe real-time simulation. It could reach near-real-time features in small scalescenarios. Future work is needed for further development.

47

For objective 3, according to simulator LENA and SUMO, we developedrespective mobility scenario and radio network. We integrated them togetherthrough NS2Mobility to reach near-real-time performance.

For objective 4, we verified our simulator performance for the LTE network.Our result shows for single UE, it is feasible to support remote driving applica-tions under real-time QoS demand.

For objective 5, due to the optimized scheduling, we could conclude in ourscenario, maximum five vehicles could be served per eNB with guaranteed con-nectivity and acceptable latency. This could be a universal limitation for Kista-like urban pattern.

8.2 Limitations and Future workBeside the contributions, our work also has a variety of limitations where stillhave space to proceed in the future. We will split the limitation into simulationprocess and V2X system modeling. Simulation process includes development-wise limitations and valuable features for further study. V2X system modelingwill discuss on the neglected aspects which could affect the accuracy of our sim-ulation in theory.

For the simulation process, the first limitation concerns the real-time fea-tures. In our current implementation, the trace-file is recording the mobilitymodel in the scenario for every second, which demands to reproduce the wholefile when rerouting. This could be a heavy load in large scenario with multipleUEs. A better Input and Output (IO) stream could be realized. Second, a re-mote vehicle-controlling application could be developed. As we already realizedthe emulation access to the vehicles in the network, use could access the virtualvehicle simulated in the simulator. In this case, it will be beneficial if we coulddevelop applications for V2X services such as security application, video server,vehicle data-base and etc in remote end and simulate the service life-cycle. Torealize system level vehicle management and re-scheduling, this could be a bonusin any aspect.

For optimizing the V2X system itself, admission control should be imple-mented. The implemented strategy is ’always yes’. But in a larger scenario,we can not subscribe every UE in the environment as our result already showstoo many UEs will result in the crash of the whole network. The core networkshould have a better admission control and corresponding handover algorithmto handle the case where too many UEs are congested in one cell.

Beside that, channel model could also be a concern. When we build upthe building model, some assumptions will affect the accuracy for the price ofsimplicity. In modeling building shape, we take the minimum external rectan-gular of the building, which is inaccurate in case of multiple buildings. For thecase as two buildings constructed close, they are likely overlap in their exter-nal rectangular, Thus, a LOS channel could be regarded as NLOS channel inthis case. In Figure 15, two more walls are taken into the calculation of NLOS

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loss for parsed building, and some LOS channels are regarded as NLOS chan-nels. Also, in real case, land-of-use will varies beyond buildings and open-area.More land types including forest and grassland should be modeled in the system.

Concrete Building

Parsed Building

eNB

UE2

UE1

NLOS Channel

LOS Channel

BLOCKED

Redundant Penetration Loss

Figure 15: Example of comparison between parsed building and real building

For core network, as the result shows in feasibility, the ongoing CQA algo-rithm is always set GBR flows higher priority than NGBR flows. If GBR flowsoccupy the available RBs, UE with NGBR bearer will be in starving: RB willnever be scheduled to them while a critical flow is being served. The fairness ofthe algorithm still needed to be improved in this sense. Also, in case of packetburst, a UE with good connection and huge GBR but little current throughputcould immediately congest the system with a data burst. This need much timefor the scheduler to recover as it calculates throughput on a moving averagebasis. For scenario with more complex service, the model for their QoS flowwill be more complex and we currently do not have a QoS waveform to describetheir demands. Hence, developing QoS model for more service in V2X scenariocan form a more detailed packet traffic environment. This can further lead to amore realistic use-case.

8.3 ConclusionTo sum up, through our thesis, we provided a state-of-art comparison amongthe network simulators and V2X simulation frameworks. By selecting mostfitting simulation component, we developed a simulation framework for V2Xcommunication with mobility model, LTE model and dynamic radio resourceallocation scheme for urban scenario. We illustrated the performance of oursimulator on every layer to prove its capabilities. Through our simulator, weverified the feasibility to realize remote driving in this scenario under requiredQoS requirements. Based on guaranteed communication, we gave the capac-ity of the scenario based on the system performance. Finally, based on thelimitations of the implemented simulator, we discussed future work from bothmodeling and development perspective.

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9 Appendix

9.1 Pseudo-code Logic of Hybrid Building PropagationModel

We involved five packages to support this feature:Mobility − building − info, building − penetration− loss−model, building −helper, hybird − building − propagation − loss − model and oh − building −propagation− loss−model.

Result: Fading Propagation in dBinitialization;while TxNode is outdoor do

Read RxNode location;if RxNode is outdoor then

if Propagation distance > 1km thenif The Height of Tx < The Height of Building OR The Heightof Rx < The Height of Building then

Propagation loss = ITU − 1411 model + Shadowing model;else

Propagation loss = Okumura−Hata model;end

elsePropagation loss = ITU − 1411 model + Shadowing model;

endelse

if Propagation distance > 1km thenif The Height of Tx < The Height of Building OR The Heightof Rx < The Height of Building then

Propagation loss = ITU − 1411 model + External WallLoss;

elsePropagation loss = Okumura−Hata model + ExternalWall Loss;

endelse

Propagation loss = ITU − 1411 model + External Wall Loss;end

endend

Algorithm 1: Hybrid Building Propagation ModelThis algorithm employs two propagation models. If Tx is higher than the

surroundings, Rx is over 1km from Tx, we choose Okumura−Hata at the fre-quency of 2.6GHz. Because it is design for macro-cell cases and we can considerthis case as the antenna is on rooftop. Outside 1km reach we could considerthe shadowing effect not a major concern. Else we adopt ITU-R 1411 modelsince it supports both LOS and None-Line-of-Sight (NLOS) cases. Specifically,in NLOS cases, traffic load and road width have to be pre-defined for best esti-mation. The shadowing factor follows a log-normal distribution of N(0, 7) (foroutdoors) and N(0, 5) (for indoors). External wall penetration is assumed tobe 5dB. This algorithm will cause inconsistency in boundary cases, a smooth

50

window function is employed to keep the consistence.

51

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