Designing Efficient Access Control to Comply Massive ...

Designing Efficient Access Control to ComplyMassive-Multiservice IoT over Cellular Networks

MOHAMMAD ISTIAK HOSSAIN

Licentiate Thesis in Information and Communication TechnologySchool of Information and Communication Technology

KTH Royal Institute of TechnologyStockholm, Sweden 2017

TRITA-ICT 2017:18ISBN 978-91-7729-547-1

KTH School of Information andCommunication Technology

SE-164 40 KistaSWEDEN

Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläg-ges till offentlig granskning för avläggande av licentiatexamen i informations-ochkommunikationsteknik fredag den 10 November 2017 klockan 11.00 i Ka-sal A (Salösten Mäkitalo), Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista.

© Mohammad Istiak Hossain, November 2017

Tryck: Universitetsservice US AB

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In the Name of Allah, the Most Gracious, the Most Merciful.

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Sammanfattning

Sakernas Internet (Internet of Things, IoT) har kommit in i vår vardag föratt förbättra våivskvalitet. Automatisering omfattar alla möliga sektorer medolika kommunikationsbehov varierade från statiska enheter med sporadisktransmission till mobila enheter med real-tids kommunikation. Trots att detfinns många teknologier tillgängliga idag för att stödja IoT tjänster såkancellulära system spela en viktig roll för många IoT tjänster med bärbaraenheter, i fordon och för industriella tillämpningar vilka inte har särskildakrav påmobilitet eller säkerhet.

IoT-tjänster genererar trafik som kan anses vara sporadisk och komma iskurar. Eftersom mobilnäten är utformade för att hantera kontinuerlig data-trafik, såkan befintliga system för styrning och åtkomstkontroll inte hanteraskurvis trafik effektivt. Detta begränsar nätverkets skalbarhet med avseendepåkapacitet för att samtidigt hantera trafik från många enheter. Dessutomkan denna skurvisa trafik i hög grad öka risk för blockering i nätet.

Denna avhandling fokuserar pådessa underliggande utmaningar för attstödja ett stort antal heterogena IoT-tjänster samtidigt som man tillhanda-håller befintliga tjänster i ett och samma radionätverk. En viktig fråga vidanvändning av mobilnät för att stödja IoT-tjänster är hur skadligt detta är förandra tjänster. Denna avhandling syftar till att svara pådetta med använd-ning av kvantitativa resultat för att beskriva de verkliga begränsningarna ibefintliga nätverk.

En viktig slutsats är att befintliga cellulära system mindre effektivt kanstödja skurvis trafik från en stor mängd uppkopplade IoT-enheter, detta itermer av skalbarhet för radionätverkets kontrollplan. Därför föreslås i dennaavhandling lösningar för att bemästra identifierade begränsningar i kontroll-planet hos befintliga system. För att förbättra prestanda för kontrollplanetinför en vertikalt verkande styrning för kärnnätet vilken säkerställer opera-törens kontroll över "capillary gateways". Dessutom medför detta möjligheterför "hand over"cellulära och "capillar networks". Därefter presenteras en enkelmen effektiv access mekanism för att hantera problem med samtidiga anroptill systemet.

Slutligen redovisas inverkan av samtidiga anrop och omsändning pådi-mensioneringen av styrning för åtkomst. Dessutom presenteras en praktisktanvändbar trafikmodell användbar för blandad trafik. De presenterade resul-taten och analysen visar pånödvändiga avvägningar mellan åtkomst, återut-sändning och resursfördelning som funktion av tid och frekvens. Resultatenvisar att med föreslagna scheman såkan cellulära systems åtkomstkontroll va-ra mer skalbara och robusta dåantalet IoT enheter ökar, detta utan medföraextra fördröjning eller krav påytterligare resurser.

Keywords: IoT, MTC, LTE, Control plane, random access

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Abstract

Internet of Things (IoT) has come in reality to improve our living quality.Automation is embraced in all the possible business verticals that have di-verse communication needs ranged from static devices’ sporadic transmissionto mobile devices’ every minute transmission. Despite, there are many tech-nologies available today to support IoT services; cellular systems can play avital role for IoT services, like wearables, vehicular, and industrial IoT, rolloutwhich have either mobility or security concern.

IoT services generated traffic are foreseen as a sporadic-bursty traffic. Asthe cellular networks are designed to serve continuous data traffic, the ex-isting system’s access control mechanism cannot efficiently conform to theburstiness of traffic. This limits the scope of the network scalability in termsof simultaneous serving devices’ capacity. Also, this bursty pattern can ex-tensively increase the rate of network’s congestion incident. In this thesis,we focus on these underlying challenges to support a large number of het-erogeneous IoT services with existing services over the same radio network.An important question for supporting IoT services over cellular networks ishow detrimental are the effects of IoT services on other services of cellularnetworks. This dissertation seeks to answer this with quantitative results toindicate the real constraints of existing networks.

An important conclusion is that existing cellular system is incompetent tosupport bursty arrival of massive IoT devices in terms of radio networks’ ac-cess control plane’s scalability. Therefore, this dissertation presents solutionsto overcome the identified limitations of access control planes. To improvethe performance of the access control plane, we incorporate a vertical corenetwork controlled group management scheme that can assure the operator’sgranular control over capillary gateways. Besides, this introduces a uniquehandover opportunity between cellular and capillary network vertices. Then,we present a simple but efficient initial access mechanism to overcome theinitial access collision at the very early stage. Finally, we show the impact ofaccess collision and retransmission on the initial access resource dimensioning.We present a practical traffic model that is realistic for the traffic scenario formixed-traffic. Our presented results and analysis depict the trade-offs betweenaccess rate, retransmission and resource allocation over time and frequency.Our results reveal that with proposed schemes of the cellular system’s accesscontrol plane can be scalable and resilient to accommodate a large number ofIoT devices without incurring extra delay or need of resources to the system.

Keywords: IoT, MTC, LTE, Control plane, random access

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This dissertation is lovingly dedicated to mymother, Nahar Hossain and my better half, Eva

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Acknowledgements

Firstly, I would like to express my sincere gratitude to my advisors Prof. JanMarkendahl and Prof. Jens Zander for giving me the opportunity to join the RS-LAB and be a part of Techno-economic Team. I would like to thank prof. Jan,meine DOKTORVATER, for his continuous support, encouragement, patience, andmotivation during this study. Despite his unfortunate incidents, he was always therefor us whenever we needed him. I would like to offer my special thanks to prof.Jens Zander; his guidance helped me in all the time of this research study andwriting of this thesis. I sincerely appreciate him for all the detailed discussion wehad regarding the study which greatly helped me to understand and formulate theresearch direction.

I am thankful to Dr. Jesus Alonso-Zarate (CTTC ) and Prof. Marina forreviewing my Licentiate proposal and thesis, respectively, and for all their valuablecomments that helped me to improve this thesis. I am very thankful to Dr. AndreasHöglund (Ericsson) for accepting the role as the opponent on my defense.

I would like to take the opportunity to thank all my colleagues at CoS depart-ment. Especially, I would like to appreciate Dr. Ki-won, Dr. Abbasi, Tatjana,Ashraf, Amirhossein, Amin, Luis, and Andres for their thoughtful comments onmy works and all the discussions we had these past few years. Also, I would liketo thank Susy, Madeleine, Susanne, Jenny, Sarah and others for supporting in ad-ministrative matters.

Last three years has been a remarkable journey with a few ups and downs.Apart from the professional attainments, I also have reached a personal milestoneby becoming a Father. To be honest, it was not a smooth start of the parenthoodfor us. We were all alone far away from our family but had a great group of friendsand colleagues who have been like a family to us in Sweden during these last fewyears. I am grateful to Andres, Amirhossein, Mirela, Natasha and especially toPamela for all the supports and help I have received from them. Pamela, Claudia,Luis, and Amirhossein you have been an angel whom I could not thank adequatelyenough. I would like to express my deepest thanks and sincere appreciation to youguys for all your help and support.

Last but not the least, I would like to thank my family: my parents NaharHossain and Mokbul Hossain, and sister Tonnie for their love and endless support.I don’t know which words would be appropriate to show my gratefulness to mywife, EVA, for always being there for me regardless I am right or wrong, and forthe heavenly gift, WASI!

Istiak Hossain,Kista, October 2017

Contents

Contents xiii

List of Figures xvi

List of Tables xvii

List of Acronyms xix

I Thesis Overview 1

1 Introduction 31.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 Definition of IoT, M2M Communication, and MTC . . . . . . 41.1.2 Existing Cellular Network as an IoT Service Enabler . . . . . 5

1.2 Research Motivation, Objectives and Problem Area . . . . . . . . . . 61.2.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . 61.2.2 Research Focus and Objectives . . . . . . . . . . . . . . . . . 71.2.3 Problem Area and Research Questions . . . . . . . . . . . . . 8

1.3 Related Works & Research Gap . . . . . . . . . . . . . . . . . . . . . 121.3.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 121.3.2 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.4 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 151.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.5.1 Scenarios Building for MTC Services . . . . . . . . . . . . . . 171.5.2 Network Congestion Control . . . . . . . . . . . . . . . . . . 171.5.3 Initial Access . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.5.4 Control Plane Dimensioning and Implications . . . . . . . . . 18

1.6 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Scenarios and System Model 212.1 Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1.1 Dense Urban Information Society . . . . . . . . . . . . . . . . 22

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xiv CONTENTS

2.1.2 Massive Distribution of Sensors and Actuators . . . . . . . . 242.1.3 Marathon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.1.4 Refined Requirements . . . . . . . . . . . . . . . . . . . . . . 28

2.2 Competing Network Architectures . . . . . . . . . . . . . . . . . . . 302.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.4 Device Arrival Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.5 Performance Evaluation Method . . . . . . . . . . . . . . . . . . . . 332.6 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3 Enhanced Admission Control 393.1 Gateway based Congestion Control for Cellular Networks . . . . . . 39

3.1.1 IoT Service Impact over Capillary Networks . . . . . . . . . . 403.1.2 IoT Service Impact Over Existing Cellular Networks . . . . . 42

3.2 Cellular Network’s Signalling Overhead Reduction . . . . . . . . . . 433.2.1 Proposed Solution: Flexible Admission Control Mechanism . 443.2.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 46

3.3 Discussion of the Results . . . . . . . . . . . . . . . . . . . . . . . . . 47

4 Enhanced Initial Access 494.1 Initial Access Performance . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1.1 Proposed Schemes . . . . . . . . . . . . . . . . . . . . . . . . 514.1.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 53

4.2 Initial Access Channel Dimensioning . . . . . . . . . . . . . . . . . . 544.2.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.2.2 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . 55

4.3 Discussion of the Results . . . . . . . . . . . . . . . . . . . . . . . . . 56

5 Conclusions and Future Work 595.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Bibliography 63

II Included Papers 69

Paper A: On the Benefits of Clustered Capillary Networks for Con-gestion Control in Machine Type Communications over LTE 71

Paper B: Reducing Signalling Overload: Flexible Capillary Admis-sion Control for Dense MTC over LTE Networks 81

Paper C: DERA: Augmented Random Access for Cellular Networkswith Dense H2H-MTC Mixed Traffic 89

CONTENTS xv

Paper D: Enhanced Random Access: Initial Access Load Balancein Highly Dense LTE-A Networks for Multiservice (H2H-MTC)Traffic 99

Paper E: RACH Dimensioning for Reliable MTC over Cellular Net-works 109

List of Figures

1.1 IoT Applications Vertical. . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2 Research Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.3 Thesis Construction based on Work Contributions. . . . . . . . . . . . . 16

2.1 Dense Urban Information Society . . . . . . . . . . . . . . . . . . . . . . 222.2 Massive MTC [1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3 Marathon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.4 Cellular Connection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.5 Capillary Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.6 Simulation Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.7 Control Signalling Controlled by LTE-A [2]. . . . . . . . . . . . . . . . . 332.8 HTC and MTC Multiservice Device Arrival Pattern. . . . . . . . . . . . 342.9 HTC and MTC Multiservice Device Arrival Pattern. . . . . . . . . . . . 35

3.1 Considered Cellular and capillary Grouping in Parking Lot Scenario. . . 403.2 Capillary Networks performance. . . . . . . . . . . . . . . . . . . . . . . 413.3 Cellular Networks performance. . . . . . . . . . . . . . . . . . . . . . . . 423.4 LTE-CaN Flexible Admission Control. . . . . . . . . . . . . . . . . . . . 433.5 Connection Setup Through CaNs. . . . . . . . . . . . . . . . . . . . . . 443.6 Connection Setup Through OTT Gateways. . . . . . . . . . . . . . . . . 453.7 Performance of Flexible Admission Control Mechanism. . . . . . . . . . 46

4.1 RA Collision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Sensitivity Test of Collision Occurrence. . . . . . . . . . . . . . . . . . . 504.3 DERA Procedure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.4 PD Calculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.5 RA Performance Comparisons. . . . . . . . . . . . . . . . . . . . . . . . 534.6 RACH Resource Planning and Traffic Arrival Modelling. . . . . . . . . . 554.7 State Transition of the Proposed Model. . . . . . . . . . . . . . . . . . . 564.8 Rach Dimenstioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

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List of Tables

2.1 KPIs and Requirements for Dense Urban Information Society [1]. . . . . 232.2 KPIs and Requirements for Massive MTC. . . . . . . . . . . . . . . . . 252.3 Marathon-IoT Service Example. . . . . . . . . . . . . . . . . . . . . . . 272.4 Requirements and KPIs for the Marathon Use Case. . . . . . . . . . . . 282.5 Simulation Assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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List of Acronyms

3GPP 3rd Generation Partnership ProjectACB Access class barringAIV Air interface variantsBS Base stationCaNs Capillary NetworksDERA Delay Estimation Based Random AccessDR Delay registrationDL DownlinkeMTC Enhanced MTCERAR Enhanced Random access responseETSI European Telecommunications Standards InstituteEC-GSM Extended coverage GSMxMBB Extreme MBBGWs GatewaysH2H Human to humanHARQ Hybrid ARQICT Information and communication technologyIoT Internet-of-ThingsKPIs Key performance indicatorsLPWAN Low-power wide area networksLTE-A Long-Term Evolution AdvancedMMC M2M CommunicationM2M Machine to machineMTC Machine type communicationmMTC Massive MTCMMDs MMC devicesMBB Mobile broadbandmHealth Mobile HealthMNOs Mobile network operatorsMTDs MTC devicesNB-IoT Narrow band- IoTNOs Network operators

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xx LIST OF ACRONYMS

PRACH Physical random access channelPDP Power delay profilePD Propagation delayQoE Quality of experienceQoS Quality of serviceRAN Radio access networkRAT Radio Access TechnologyRRC Radio Resource ControlRACH Random access channelSPP Switched-Poisson processTA Timing alignmentUL UplinkUE User Equipment

Part I

Thesis Overview

1

Chapter 1

Introduction

Advancements in wireless information and communication technology (ICT) haveresulted in a significant growth of new services. One of the prominent services inthis domain is Internet-of-Things (IoT). IoT refers to the interconnection of smartdevices like sensors and actuators which are remotely connected mostly via wirelessnetworks. Among many other wireless technologies, the ubiquitous coverage, secureconnectivity, data rate, and mobility competence make cellular networks attractivefor many IoT services like high-end wearable, vehicular and Industrial services. Thisnew service scope opens up a new revenue opportunity for mobile network operators(MNOs) and vendors. Thus, today there is a strong drive for accommodating IoTtraffic in cellular network with minimal Capital Expenditure (CAPEX) [3]. Despitethe opportunities of revenue from IoT services, the cellular networks face severalnew challenges. Standardisation bodies like 3rd Generation Partnership Project(3GPP), European Telecommunications Standards Institute (ETSI) have pointedout the technological barrier of existing cellular system which is the need to supporta large number of devices per radio cell. This study seeks to address the keytechnological barriers of the access control plane in accommodating massive IoTdevices on the cellular network.

In this chapter, we present state of the art and overview of the academic researcharea. Then, we discuss the motivation towards our research.

1.1 Background

We want to collect and analyse the colossal amount of data from our environmentto control our surroundings and improve our living standard. For that, massivenumber of devices should communicate with each other or with the remote serverin an autonomous fashion to create a granular view of our surroundings. The ad-vancements in wireless communication and computing provide that opportunity tomake this possible through IoT. Recent market research anticipated an exponentialgrowth of IoT services in coming years[4]. According to Machina Research, Cisco,

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4 CHAPTER 1. INTRODUCTION

Figure 1.1: IoT Applications Vertical.

Huawei and Ericsson market forecasts the cellular IoT connections will reach around14∼20 billion by 2025 and the predicted global revenue will be around $235 Billion[5]. As depicted in Fig. 1.1, many new IoT services are expected to emerge indifferent business verticals that will certainly play a vital role in the future wirelessbusiness domain. This is why machine to machine (M2M) communication is one ofthe key focus areas of ICT market and research arena.

In order to support IoT services, a key aspect of cellular radio networks is theaccommodation of large number of devices per cell. However, Existing cellularnetworks have been adapted to serve mobile broadband (MBB) services with fewconcurrent access opportunities. Hence, it is important to evolve and develop capa-bilities thus the base station (BS) can handle the traffic and concurrent connectionof the IoT devices that belongs to different service verticals. In recent studies, re-searchers have focused on the enhancement of wireless architectures and systemsto support connected devices along with existing human-centric traffic within net-works like LTE, LTE-A, and future 5G. As a part of this effort, we study the keychallenge to accommodate a huge number of devices per cell from cellular system’saccess control plane perspective.

1.1.1 Definition of IoT, M2M Communication, and MTCBefore going to further discussion, we would like to elaborate our understanding ofIoT, M2M, and MTC terminologies which we will carry throughout the thesis.

Devices are becoming smart by embedding sensors and actuators that enable theability to communicate between devices autonomously. An avalanche of terms de-

1.1. BACKGROUND 5

scribe this initial transformation in ICT ecosphere such as ’Internet of Everything’,’Industrial Internet’, ’Web of Things’, ’Cyber Physical Things’, ’Ambient Intelli-gence’ and ’Machine to Machine (M2M)’ [6]-[7]. Even though each of these termsmay describe a substantial research area, they are all categorised by customaryphysical objects equipped with automation, sensors, actuators and communicationcompetencies [8]. Höller et al. [9] describe this as "a world where physical ob-jects are seamlessly integrated into the information network and where the physicalobjects can become active participants in business processes." which is, form ourviewpoint, the appropriate definition of IoT.

M2M communication has an established definition as a set of wired and wirelesscommunication between autonomous machines without human mediation in controland monitoring usage [10]. In [11], C. Anton-Haro and M. Dohler referred M2Mcommunication as a system that includes all the ICT able to measure, deliver,digest and react to information in an autonomous fashion. However, since M2M isa popular term to refer IoT as well; we prefer to use ’M2M Communication (MMC)’to represent the wired and wireless connectivity between autonomous machines.

In the article [12] Machine type communication (MTC) appeared as a synonymof MMC. It is a working terminology defined by 3GPP. The term MTC is frequentlyused as a terminology when a device is connected through a cellular network [13].From our aspect, MTC exclusively represents the cellular connectivity service be-tween smart machines as well.

Briefly, we consider MMC and MTC as an enabler of IoT devices’ networkwhere M2M is another synonym of IoT. All terms discussed in this section signifythe notion of ’connected devices’ and their communication plane.

1.1.2 Existing Cellular Network as an IoT Service EnablerNetwork operators (NOs) are now focused on how to drive more revenue by scalingup new services. The IoT market, in this scene, is perceived as the imminent avenuefor MMC revenue growth with various reports depicting market size to reach 50billion connections by 2030 [4]-[16].

The MMC service sets can be categorised based on their communication mediumi.e., wired, and wireless technologies. Wired technologies such as copper and fiber-optic cables are capable of delivering ultra-reliable, high-speed, ultra-secure connec-tivity with an assured low delay than any other technologies available in the market.However, high deployment cost, installation difficulties, high maintenance cost andmore are motivating IoT actors to consider alternatives to wired communicationsolutions as an end user connectivity solution.

Mobility, seamless connectivity, low deployment and maintenance cost, scalabil-ity, and service integration with existing ubiquitous networks make wireless MMCas a stronger and affordable alternative solution. As a result, we can see moreand more applications are adopting wireless MMC. Widely alleged IoT services likeconnected cars, smart Grid, fleet management, Wearable IoT devices, tracking de-vices, flood sensors, earthquake sensors require either vast coverage area or mobility.


The ubiquitous connection of cellular networks actually can meet both of the IoTcommunication service criteria. As a result, although there are a bunch of short-range radio access technologies such as Wi-Fi, ZigBee, 6LoWPAN, Bluetooth LowEnergy, IEEE802.15.4, and IEEE 802.11ah currently available in the market, con-necting end-user IoT devices through MTC service is beneficial for many servicesin the context of seamless connectivity.

One of the main criteria of IoT services is longer battery lifetime of a battery-driven sensor. Also, the price of the sensor has to be below ten dollars. Theultra-low power operation and simple hardware and protocol requirements expe-dited a new communication paradigm, which is currently complementing traditioncellular and short-range technologies. This new technology is known as low-powerwide area networks (LPWAN). A Plethora of LPWAN are available or will comein coming years. Out of these, a few of the solutions are proprietary solutionswith very low data rate like SIGFOX, LORAWAN, TELENSA, and INGENU[17].Some other technologies are in the standardisation process and will be availableby 2020. Among them, prominent solutions are: narrow band- IoT (NB-IoT), en-hanced MTC (eMTC), Extended coverage GSM (EC-GSM) and long range lowpower IEEE802.11ah where the primary focus of these systems is larger coveragewith competitive data rate. However, these systems are vulnerable to interference.According to [17], coverage area and data rate decay exponentially with the incre-menting number of IoT devices due to interference. Also, these technologies do notfit with the sporadic transmission of multi-service devices, as the increment of theduty cycle drastically reduces the capacity of connected devices per site. Further-more, deployment of these technologies brings new cost for MNOs. So, the use ofexisting cellular networks for IoT services could be a feasible option for operatorsin many developing countries if they can use the same deployed system to supportIoT with other services.

1.2 Research Motivation, Objectives and Problem Area

In this section we elaborate the research motivation, aim and the focused area withrelevant research questions.

1.2.1 Research MotivationIn addition to the extreme MBB(xMBB) services, in future, cellular networks areexpected to handle a range of new connectivity services i.e. IoT. Naturally, theIoT sensors density will be higher in places where the population density is higher.In such case, the plurality of IoT services gives a very stringent requirement onconcurrent access capacity and latency. It is expected that LTE-Advanced and 5Gtechnologies should support all service sets within one network to keep the servicecosts and deployment expenses low.

In EU projects like METIS-II [18], and NGMN [19] defined use cases like denseurban information society, and massive Internet of things that reflect the demand

1.2. RESEARCH MOTIVATION, OBJECTIVES AND PROBLEM AREA 7

to accommodate both xMBB and massive heterogeneous MTC traffic within anetwork. Also, during our study, we introduced an extreme mix-traffic scenarioof Marathon use case. In all the above mentioned scenarios, density of devicesare assumed between 200,000∼1 million per square kilometre where the inter-sitedistance of macro cells in the dense urban scenario is bounded to 500 meters. Inaddition, quite rigid requirements like coverage should be more than 99%, accessreliability should be more than 99.99%, and the connection setup delay should bearound 10 ms stressed over and over in most Cellular IoT related use cases. Theuse cases considered in this dissertation are explained in details in Section 2.1.

Furthermore, from [20], one can see that the MTC has bursty traffic patternand much likely to compete with other service devices for network’s uplink andcontrol channel resources in co-located geolocation. Such traffic characteristicscan introduce radio network congestion as mass concurrent transmission1can occuroften because of the swarm existence of MTC devices, and their sporadic and burstytransmission of small data packets. In short, we can say, though the massive IoTservices require much less bandwidth than the MBB services, the network may needsto handle a higher number of concurrent access in densely populated geolocations.

To exemplify, let us consider an event-driven service-an earthquake monitoringscenario. Assume the sensor MTDs are deployed in an earthquake prone city likeSan Francisco where on average 10,000 small to medium scale earthquakes occurevery year. If we consider the density of these sensors is 60 MTDs/km2 [22] thenthere will be around 377 MTDs in a square kilometre. Now, if we consider ahexagonal cell region where cell radius is 2 km, there will be 754 MTDs in acell. Also, let us consider the speed of seismic surface wave which is around 14km/s. In this case, if an earthquake occurs all these sensors will activate within150 ms which means 51 access requests/slot will be received by the base stationfor fifteen consecutive slots. But with allocated control plane’s resources existingcellular systems can handle only 240 MTDs in 150 ms. In addition, if we consider 54preambles are used for initial access mechanism per 10 ms slot, and then the collisionprobability will be around 61%. To this end, we identified two significant challengesin which IoT service can hamper the overall systems operation. Motivated by thefact, we looked into the following major challenges to support event-driven IoTservices in cellular systems without hampering other services in the network.

1.2.2 Research Focus and Objectives

The main objective of this dissertation is to verify the existing radio networkscapability to support traditional services along with IoT services. Our work focusis to make the system ready for accommodating multi-service devices without anyvertical split of radio resources for access control plane. To do this, we studyrelevant scenarios where the mentioned challenges may have a drastic impact on

1As explained in 3GPP TS 22.368 radio congestion use case[21]


performance and possible solutions related access control’s congestion and collisionof cellular networks.

Based on our preceding discussion, we see that access control channel’s per-formance regarding access completion is an essential factor in paving the massiveIoT services over cellular networks. For that, the particular research problems andquestions address two key characteristics of access control plane: i) admission con-trol, and ii) initial access. Based on this, the principal research questions addressedin this dissertation are as follows:

• HQ1: Do the contemporary services of cellular networks be im-pacted by the IoT services?

• HQ2: Can we support massive IoT services with the existing cellu-lar networks access control plane’s allocated resources and how?

1.2.3 Problem Area and Research QuestionsBased on the literature review discussed in Section 1.3.12, we see that the signallingoverload, access rate, and delay are the potential control plane’s barriers, identifiedin the literature, to enable massive IoT services over present cellular networks. An-other major constraints, is congestion and collision prone access procedure. Basedon these identified traits, the specific research problems are defined as below:

Massive IoT Impact on different Cellular Networks Architecture

IoT devices that require no mobility, such as metering devices, weather monitoringsensors or surveillance devices, are usually deployed in residential areas or office fa-cilities where short-range technologies3 already exist. It is envisioned that devicesfrom different service groups are connected to the Internet via a gateway usingshort range technologies. The backhaul in such gateways could be wired or cellularconnected. If the gateway’s backhaul is cellular connected, the existing cellular net-work does not have any control over gateway connected devices. Even though thegateway is deployed by the operators, current cellular systems offered a groupingmechanism which is quite static and preconfigured system that cannot handle thegroup link uncertainty. It results in poor QoS, latency and resources utilisation ofthe networks even in low traffic load.In this scene, the issue is to provide granu-lar control and knowledge about the devices connected to the CaNs proficiently toincrease the overall QoS throughout a heterogeneous network.

The possible benefits of CaN to support IoT services over cellular networks arewidely accepted in the scientific community. However, to the best of our knowl-edge, the gain of using CaN in existing cellular system’s mix traffic context were

2to keep the flow of the discussion author presents the research focus before the literaturereview section

3These sets of technologies that are not part of the cellular networks are commonly referredto as capillary networks (CaN) [23]. The network architecture of CaN and Cellular networks arediscussed in Section 2.2


not adequately addressed in literature at the time we published appended paper A.Even the impact of IoT services over cellular networks and CaN was not definedquantitatively in the literature rather being discussed conceptually over-the-top.Hence, in answering to HQ1, a sanity check to assess the severity of the massiveand concurrent access problem with realistic heterogeneous traffic use case was nec-essary. For that, the following research questions are addressed in this dissertationto seek quantitative answers to fortify the problem motivation.

• RQ1: What are the key constraints that can affect the performance of cellularand capillary network’s access control ?

• RQ2: Can capillary networks make existing cellular networks access controladroit to support IoT services with contemporary services?

1.2.3.1 Access Control Performance Enhancement

The second problem addressed in this dissertation is the connection setup cost interms of signalling exchange rate. Usually, MTC applications are uplink limited indata transmission and downlink-limited in terms of cellular control data. Naturally,if a vast number of devices need to transmit small payload like 100 bytes of data,for each transmission the radio and S1 bearers need to re-establish when eachdevice changes from IDLE to CONNECTED mode [24]. When MTDs send smallamounts of data, the Radio Resource Control (RRC) connection, radio bearer,and S1-bearer setup generates more signalling than the size of the actual payload.This is because the existing networks are not designed to support large numbersof connected devices for infrequent small data transmission which generates manysmall sessions burst. This causes excess signalling for setting up the connectionsbefore each transmission as the interval between packet transmissions per deviceis significantly high. According to 3GPP release-12, a device needs 199 bytes ofcontrol data transmission to establish RRC connection, radio bearer and S1 bearerregardless the payloads [25]. Therefore, signalling congestion can occur if a largenumber of MTDs try almost simultaneously to attach to the network or activate,modify or deactivate a connection.

To exemplify, let’s consider 3GPP signalling congestion use case [21], imagine acell with large numbers of event-driven connected devices such as smart grid, earth-quake detector or any other disaster management sensors along with regular traffic.In such scenario, an signalling overload can happen by mass metering devices, ina densely deployed scenario, when sensors become active within a short intervalto synchronize with the server after a power outage4 recovery [21]. Typically, thepayload of the keep-alive message is too small compared to regular payload so the

4Apparently, the example used here is not a problem for developed countries where poweroutage hardly ever occurs in years. But developing countries like India, Bangladesh, Pakistan,and Kenya faces tremendous power crisis that the power outage could occur ten to twelve timesin a day. For such cases, this makes sense to have a system that can avoid such type of overload.


data plane will be free. Even though, devices will be blocked because of the controlplane congestion.

3GPP release-13 reveals some proposed mechanism to reduce the signalling cost.However, the most prominent solution presented in release-13, MTDs always in con-nected mode, have a high risk of single node congestions at the core networks asthey proposed to manage the sensor devices connection centrally and use the pastconnection credential for future transmissions. For this reason, it would be benefi-cial to minimise the signalling and control messages required for MTDs connectionsetup [12]. In this context, the main seen challenge to support IoT services overexisting cellular networks is to serve large numbers of devices concurrent accesswith less signalling cost. In the view of this, the third research-question addressedthe signalling constraints defined as:

• RQ3: Can we improve the access performance of cellular and capillary systemsby enhancing admission control mechanism?

In this study, we evaluate the limitations on the LTE, LTE-A signalling con-straints and admission control issues as a continuation of RQ1. Then, in RQ3, weseek the possible solutions to reduce the signalling cost and enhance the perfor-mance of admission control mechanism.

1.2.3.2 Initial Access Performance Enhancement

Current initial access mechanism does not perform efficiently in high load. Eventhough, we can avoid signalling congestion by using different methods; still initialaccess congestion and collision can occur when more than average attempts takeplace in a random access slot. This can happen because the allocated frequency,signature and time resource for random access is limited per frame.

Existing access control protocol cannot handle collisions in the early stage ofthe initial access. if multiple requests with randomly picked same signatures arrivewithin a certain delay spread interval. In this case, if the BS is able to detecta collision it cannot address the collision with the current scheme. So, collideddevices need to retry again after a certain backoff period. In case the systemcannot detect the collision at the early stage, collision resolution method requiresa few Hybrid ARQ (HARQ) transmissions before the collided devices realise andfallback to restart the access procedure again.

If we consider a dense urban scenario where each cell has 300,000 sensors thatbelong to 17 different service sets with requirements concerning delay, access reli-ability, and transmission frequency. This can generate 2000 request per second inextreme case [18] where current systems capacity in a single PRACH is only 128attempts per second with 1% collision probability. Our study indicates that theinitial access collision in existing cellular systems increases exponentially with thedevice increment rate. In the existing systems, more than ten simultaneous arrivalsof devices have more than 60% chance of having at least one collision. This in-creases the access delay along with the retrial rate. Also, excessive collision reduces


the resource utilisation rate as explained in paper D. These, in turn, can decreasedevices’ battery lifetime as devices retrial rate increases the devices active time. Asa result, the existing initial access mechanism proposed in 3GPP Release 13 cannotmeet the battery-driven MTC devices promised lifetime5.

There are many proposed methods to resolve access congestion dynamically,but none of them can act to solve the occurred collision actively; rather they takepreemptive measure to handle future access overload as discussed in Section 1.3.1.2.Furthermore, all the existing solutions available in literature dealt access collisionproblem by introducing additional delay, resources or increase blocking rate. An-other obvious option is to densify the network with more cells which is not viablefrom cost-efficiency and network dimensioning6 perspective.

Hence, initial access congestion and collision are one of the major obstructionsto support IoT services at a "massive scale" with traditional services. For that, itis essential to find a smart, straightforward and efficient solution that can handlecollision and congestion in early stage and can reduce retransmission rate and initialaccess delay without introducing too much complexity to the system. For that, weseek to address following research question.

• RQ4: Can we provide solutions to enhance the initial access mechanism tosupport IoT services without hampering other services of cellular networks ??

We seek efficient methods that can solve initial access collision and congestionproblem without adding any extra delay resources or device complexity to thesystem. We address this issue by analysing the implications of a large number ofdevices access attempts within a short interval.

During this work we realized that retransmission-reliability analysis for physi-cal random access channel (PRACH) dimensioning is needed when IoT traffic usingthe same base station along with contemporary services. As PRACH has a certaincapacity limitations, allocating insufficient resources, can result excessive collisionsand retransmissions which can hamper other services in the network. Also allo-cating more resources to PRACH will incur resource depletion at the user plane.Scientific papers like [2], [26]-[28] have investigated preamble collision probabil-ity to estimate the device access success rate. However, we noticed that none ofthese papers have considered retransmissions impact on access load in their model.In addition, none of the models, to the best of our knowledge, have consideredthe heterogeneous traffic profile where the MTC traffic is considered as a periodicburst with other service traffic. Hence, in this dissertation, we present a method tofind certain access rate for provided resources and allowed retransmission window.With such model one can understand the intensity of the IoT service impact on thecontrol channel 7 and RACH slots requirements.

510-15 years without replacing the battery [2]6For incumbent operators7as RACH access is directly linked to the PDCCH and PUCCH resource allocation.


As far, we noticed the impact of IoT services on cellular networks PRACHdimensioning remains under-researched. So, we focus our research on to this gap,and we formulate our research question as follow:

• How do access reliability and retransmission restricted IoT services have animpact on PRACH Dimensioning?

Indeed, the challenges are not limited to the above-discussed issues. There areother challenges like devices’ energy efficiency, granular resource management, andsecurity that needs equal attention to support IoT services. However, this dis-sertation focuses and presents solutions which can enhance current systems accesscontrol mechanisms performance without incurring any extra delay to the system.We believe these key steps can improve existing and future cellular networks controlplane’s performance to make the system resilient to a large number of concurrentconnections without vertical spit of infrastructure.

1.3 Related Works & Research Gap

The impact of deploying a massive number of IoT devices over cellular networkshas been widely studied in the literature. Massive MTD deployments could affectthe design of initial access and admission control mechanism, and network dimen-sioning . Large numbers of devices may generate an enormous volume of signallingand access request in comparison to the actual payload to be transmitted. This factcould create congestion both for radio access, and core networks [12], [29]-[30]. Cel-lular networks congestion has been widely studied with most solutions concentratedon random access overload control [30]-[32] and uplink-downlink device scheduling[33]. In this section, we will describe related work leading to the research gap thisthesis seeks to address.

1.3.1 Literature ReviewA variety of techniques have been proposed in literature to overcome signallingoverload, reduce initial access collision and congestion, and improve device batterylifetime. In this section, we summarize the works relevant and significant to themain objective of this dissertation.

1.3.1.1 Signalling Overload and Admission Control Mechanism

To support a large number of IoT devices, several schemes propose device grouping8

solutions. In most of these proposals, MTDs within a group share the same QoScharacteristics and service requirements. The main objective of device grouping is tosimplify resource management on the cellular network. Considering the trade-offsbetween resource allocation simplification, group size, MTC service performance

8also referred to as clustering

1.3. RELATED WORKS & RESEARCH GAP 13

and delay requirements, a QoS-Specific MTC grouping scheme is evaluated in [29].Another approach of group based MMC is discussed in [24], where authors haveintroduced a single bearer sharing method within a specific group of devices. Thismethod shows a significant uplink (UL) and downlink (DL) performance improve-ment over the network. But, it introduces an additional delay when the systemoperates under low MTC load. Another significant work in this direction proposesMTC device groups associated with QoS profile and controlled by the BS [34]. In adense network, data packets in clusters face the jitter constraints. To overcome suchchallenge, group-based signalling and access policies through a two-level device par-titioning, namely, paging groups and access groups, is presented in [35]. The resultsof the partitioning policies show significant access delay improvement compared tothe conventional access scheme with smaller group size. However, a smaller groupsize introduces a constant access delay, which is larger than the conventional accessdelay. In order to enable the information exchange between local groups and thecellular network, the work presented in [23] suggests the support of MTDs overLTE networks through special gateways called CaN gateways (GWs). Groupingadmission control can also be used to reduce signalling congestion. In this sense,the congestion-aware admission control proposed in [36] selectively rejects signallingmessages from MTDs on the radio access network. This control mechanism followsa probability that is set based on a proportional integrative derivative controller.This controller reflects the congestion level of a relevant core network node. Thisscheme can increase the core network performance, but it limits the radio networkaccess capability.

In [37], a delay registration (DR) algorithm that postpones the registrationto a new cell is described. However, selecting an effective delay timer that cantake into account mobility behaviours of all MTC devices is not straightforward.A longer delay timer gradually increases the delay response of the devices, and ashorter delay timer cancels the signalling overhead reduction gain. In [38], a solutionis presented for GSM networks where devices connecting in a periodical mannerrequest a future network access already during a previous connection. Results haveshown that this technique reduces access channels overload and improves accessdelay that infers dense MTD deployment in GSM network. From the networkperspective, this feature is viable for dense MTD deployment. But, from MTCservice perspective, particularly for delay-intolerant services; this feature introducesan intolerable latency since scheduling could take a few minutes after a sessionrequest received form a MTD.

1.3.1.2 Initial Access Performance

A thorough analysis of random access channel (RACH) limitation for massive MTCand a set of alternatives are presented in [13], [32], [39]. Among the proposed al-ternatives, access class barring (ACB) is a promising approach. ACB has beensuggested by the 3GPP as the most feasible baseline solution and is adapted forradio access overload control to reduce the contention among nodes at the cost of


introducing longer access delays. A capillary gateway-based solutions as in [23],[40], [41]-[42] could be potential solutions, as these can decrease the number of si-multaneous access requests arrival per BS, and hence, reduce the spent time [40],[42] and consumed energy [41] in contention resolution. However, capillary-basedsolutions introduce overhead in group-forming, as well as additional link delay dueto the half-duplex operation along with the access delay, which limits its applicationin practice [43]. To guarantee the QoS of H2H traffic in coexistence scenarios, sep-aration of resources, also referred as virtual resource allocation has been proposedin literature which splits the available preambles between H2H and MMC trafficsin time or frequency domain [13], [44]. However, solutions as mentioned earlier,as well as the other solutions discussed in [23], provide limited benefits; the scarceavailable resources for MTC traffic reduce the success rate in overloaded scenarios.

A contention resolution scheme based on distributed queuing is proposed in[45]. This scheme improves the effective channel utilisation by splitting devicesinto groups for the subsequent transmissions, which in turn reduces the accessdelay and the probability of collision. Nevertheless, this system has been preciselydesigned only for extreme arrival cases that are forecasted for MTC scenarios.

Using fixed timing alignment (TA) information for reducing the RA collision ofstationary devices has been presented in [46]. This promising scheme also yields po-tential benefit on access delay, collision probability, and energy efficiency. However,this scheme is limited to immobile devices and might lose its value in mixed-trafficscenarios. Article [47] extend this idea to use timing advance on top of ACB toreduce access overload. The schemes presented in [46]-[47] have a higher chance toserve devices that are closer to the base station than the distant devices. So accessfairness is an issue for these schemes. In addition, the latter method does not solvethe false detection problem of the former method.

In literature, articles like [48],[49] have studied initial access collision rate toassess device success ratio and network dimensioning. In [10] an analytical model isoffered to evaluate the performance of the physical random access channel (PRACH)mechanisms in case of massive simultaneous arrival of devices. In any case, we sawthat none of these papers have considered retransmissions affect on access loadin their model. The authors in [10] has proposed a model and considered theretransmission effect, yet they neglected to determine a closed-form expression forthe access rate performance.

1.3.2 Research GapNew services are introducing different business opportunities along with new servicerequirements. IoT and MMC services are putting a stringent pressure on support-ing a vast number of devices on the same network. New system like LTE − Aplus

(Release-13) aims at providing high-speed broadband connectivity and MMC inseparate spectrums. However, the crisis of valuable licensed spectrum and serviceuncertainty of unlicensed spectrum may not be able to handle future MMC trafficgrowth. We believe what we see now as MTC traffic is just the tip of the iceberg.

1.4. RESEARCH METHODOLOGY 15

Figure 1.2: Research Approach.

The availability of IoT connectivity service will change the future connection re-quirement radically. We foresee future IoT connectivity will become bursty andrequire more frequent transmission than today in order to collect more granulardata from our surroundings. On top of that, asynchronous transmission from dif-ferent service sets will make a stiff curve of transmission bursts that may lead tomore frequent service interruptions.

There are many proposed solutions available in the literature, but none of themcan address access control problems without introducing any additional delay orresources to the system. There is a clear research gap in studying the simultaneousconnectivity of a large number of devices per cell. Also, retransmission-reliabilityimpact on PRACH dimensioning is missing in literatures.This thesis aims at con-tributing in these areas with the particular focus on performance enhancement ofaccess control plane i.e. admission control and initial access for massive-multiservicetraffic scenario.

1.4 Research Methodology

In the following, the general methodology to conduct this study is presented whichis used to answering the research question throughout the thesis.

Methodology of Technical AnalysisThe technical research followed the approach illustrated in Fig. 1.2. We have donea qualitative literature review to understand the existing cellular technologies stateof the art. Then, we find the research gap and formulate the high-level researchquestions based on our review. To answer high-level questions, we define a uniqueuse case that covers traffic and service heterogeneity in terms of devices duty-cycle,mobility and delay tolerant nature. Then, we subdivide the questions into foursub-questions. The focuses of these questions are on two key challenges whichare signalling overload and access collision. Furthermore, we classify the key per-formance indicators (KPIs) that are needed to be considered for the performanceevaluation of our considered methods (see Section 2.5.0.1). After that, we imple-ment a discrete event network-level simulation setting to evaluate the performanceof the existing cellular networks. Simulations have been carried out using NS-3LENA (release 3.23) [50] model. We have used the existing simulation module


Figure 1.3: Thesis Construction based on Work Contributions.

as the baseline and extended the module to evaluate our proposed methods. Tovalidate the results, we replicate the scenarios from 3GPP [13] [25], and METIS [1].

To answer the sub-questions of RQ1 and RQ2, we evaluate the existing relevantmodels’ performance based on simulation results. Then, we form a scheme. We usemathematical modelling to provide a high-level description of how the proposed sys-tem performs. In such way, we avoid unrelated factors and describe the propertiesof the method using mathematical formulas with sensible simplified assumptions.Then, we calculate the systems’ expected performance characteristics and describethe difference between methods under ideal situations. After that, we test the algo-rithm on the basis of our gathered simulation results. We use Monte Carlo methodto gather the numerical results. Finally, we take a broad view of the result fromthe analysis and decide whether or not the results support the hypothesis. Then,we proposed methods backed with a mathematical model and numerical analysis.

In order to conduct dimensioning study, we categories the metrics those are re-lated to RACH dimensioning and MTC to answer the first research-question. Afterthat, we develop a traffic arrival model based on Quasi-Poisson model that cancapture the heterogeneous traffic scene’s characteristics which are retransmissioncap and reliability. Then, we develop a mathematical formula for access rate ofPRACH considering the parameters mentioned above. For validating our mathe-matical model, we use the simulation results we got from the RA collision studies.

Based on the outcome, we will develop a method for PUCCH and PDCCHdimensioning. This outcome will impact the capacity demand metrics of the over-all network dimensioning formula. Finally, using this result, we will do a cost-performance analysis of future networks. In this dissertation, we present initialwork on the econometric study which is RACH dimensioning. We will provide asimple arithmetic closed-form for network dimensioning and cost assessment as oneof the main outcomes of the final dissertation of this research.

1.5. CONTRIBUTIONS 17

1.5 Contributions

Fig. 1.3 displays the thesis construction based on the appended papers and theirtopic wise association. Our efforts through the papers are merely focusing on twoaspects of control channel’s performance which are network congestion and initialaccess collision. In this section, we state our contributions on these two focusedchallenges below:

1.5.1 Scenarios Building for MTC ServicesDuring thee study author has introduced an extreme mix-traffic scenario of Marathonuse case.This use case can be considered as an operator-centric use case where op-erators will struggle to provide service with promised quality of experience (QoE)in dense network scenario with MTC traffic. the author has introduced heteroge-neous service challenges with MTC devices in mobility. this use case is added tothe METIS 5G project [39] and is published in deliverable D1.5 [51] and availablein public. We have used the assumptions of this use case in our papers but this usecase is planned to be included in the future paper. The deliverable is not includedin this thesis but can be found in [51].

1.5.2 Network Congestion ControlTo the end of this study, we provide a quantification of the performance limita-tions of cellular, capillary, and capillary over cellular system to support IoT trafficbased on two main parameters: success rate and delay. Paper A illustrates thefeasibility of cellular and capillary networks in the parking lot scenario context. Weprovide a detailed analysis of the cellular and capillary gateways performance inheterogeneous traffic context. We presents the performance trade-off of capillaryand cellular networks. To address the identified challenge, in paper B, we adaptand make a performance evaluation of a novel method to facilitate vertical han-dover by enhancing the present admission control mechanism. Also, this proposedmethod can reduce the signalling impact by offloading devices even before going tothe connection setup step.

• Paper A- "On the Benefits of Clustered Capillary Networks for CongestionControl in Machine Type Communications over LTE," The 24th InternationalConference on Computer Communication and Networks; Rachaen M. Huq,Kevin P. Moreno, Hui Zhu, Jue Jhang, Oscar Ohlsson, Mohammad IstiakHossain.

• Paper B- "Reducing Signalling Overload: Flexible Capillary Admission Con-trol for Dense MTC over LTE Networks"; IEEE PIMRC, August 2015; Mo-hammad Istiak Hossain, Andres Laya, Francesco Militano, Sassan Iraji, JanMarkendahl.


In paper A, the author contributed in formulating the problem and providinga custom simulation base. Also, the author contributed in writing the paper fromthe draft versions prepared by co-authors by extending the ideas, simulation resultsand proofreading. The author of this thesis acknowledges valuable efforts of theco-authors on extending the simulation scenario and writing the draft and editingof the papers.

1.5.3 Initial AccessWe provide a quantification of the limitations of the existing initial access mecha-nism and channel of LTE-A system to support IoT traffic. our analysis was bansedon four parameter indicator: delay, success rate, collision rate and resource uti-lization. We further investigate other congestion control methods like barring andbackoff based on the abovementioned parameters.furthermore, we propose a novelinitial access method based on a simple delay estimation process. This gives thedevice cognizance about the possible delay in an early step of access process andhelps the device to avoid any possible collision. our result shows significant im-provement of initial access performance of existing system which we have presentedin Chapter 4. the details of the mechanism and analysis outcome is can be foundin Paper C and D.

• Paper C- "DERA: Augmented Random Access for Cellular Networks withDense H2H-MTC Mixed Traffic"; IEEE GLOBECOM Workshop, 2016; Mo-hammad Istiak Hossain, Amin Azari, Jens Zander.

• Paper D- "Enhanced Random Access: Initial Access Load Balance in HighlyDense LTE-A Networks for Multiservice (H2H-MTC) Traffic"; IEEE ICC,2017; Mohammad Istiak Hossain, Amin Azari, Jan Markendahl, Jens Zander.

1.5.4 Control Plane Dimensioning and ImplicationsIn answering to the last research question, we present a new arithmetical modelfor the coexistence of IoT and H2H heterogeneous traffic. We investigate accessreliability and retransmission centric performance efficiency of current initial accessprocedures of a cellular network in dealing with a heterogeneous network.

• Paper E- "RACH Dimensioning for Reliable MTC over Cellular Networks";IEEE VTC 2017; Amin Azari, Mohammad Istiak Hossain, Jan Markendahl

In paper E the author has contributed in formulating the problem, traffic mod-elling, simulation, and partially on analytical analysis. The author acknowledgesthe valuable contribution of Amin Azari on developing the traffic and analyticalmodel.

1.6. THESIS OUTLINE 19

1.6 Thesis Outline

The rest of the thesis is organized as follows. First we discuss the consideredscenarios, use cases, and system model in Chapter 2. Then we present the resultsof access congestion and signalling overload, and initial access in Chapter 3 and 4,respectively. Finally, Chapter 5 summarizes the notable remarks with our futureresearch direction. The papers included in this thesis are appended to the end ofthis dissertation.

Chapter 2

Scenarios and System Model

In this chapter, we introduce the use cases and network architectures which areconsidered to assess the performance of the current system and proposed schemes.Then, further details on system model and traffic model that have used throughoutthe thesis. At last, we discuss the performance parameters that have considered toperform the simulation.

2.1 Use Cases

In recent years, IoT research has been quite active. Thus, several EU fundedprojects have tried to create founding scenarios for classifying the requirements ofIoT. Similarly, other standardisation and research bodies like NGMN, 3GPP, andITU-R have captured the corresponding requirements to motivate the research tomeet the future IoT service demands [51]. This, in turn, has resulted in a largevariety of scenarios and use cases focusing on diverse service requirements. Thepurpose of this section is to classify the scenes that capture the various service(MBB and IoT) requirements that have used for the performance evaluation of theproposed mechanisms.

Among the avalanche of use cases that define 5G MBB and IoT service trend, wechoose three use cases that cover the two most important services which are xMBBand massive MTC (mMTC). These use cases are: Dense urban information society[51], Massive distribution of sensors and actuators [1], and Marathon [51]. Theseuse cases are the combination of three scenarios defined in [1] which are ubiquitousthings communicating, great service in a crowd, and best experience follows you.The stated three use cases have stringent requirements to support a significantlylarge number of devices per cell whose technical solutions are likely to function forother similar use cases as well. If we consider each service type individually to builda future network in view of that, we would likely end up with very different radionetwork designs and architectures. Besides, only a common radio network thatcan support both service types is an economically and environmentally sustainable

21

22 CHAPTER 2. SCENARIOS AND SYSTEM MODEL

Figure 2.1: Dense Urban Information Society

solution. For this reason, this study is performed specifically towards a set of usecases that typically combine xMBB and mMTC service set.

2.1.1 Dense Urban Information Society

This use case combines three METIS-I scenarios:1) Amazingly Fast, 2)Ubiquitousthings communicating and 3) best experience follows you. But to define the prob-lem more precisely this use case only considers dense urban areas with both IoTand human type traffic(HTC). This use case considers both indoor and outdoorenvironments to provide reliable connectivity to the people at any place and at anytime. As depicted in Fig. 2.1, the major fact in such case is the user’s expecta-tion of getting the same quality of experience no matter whether they are at theirworkplace, enjoying leisure activities such as shopping, or being on the move onfoot or in a vehicle[1]. Traffic offload via short-range in dense urban provides anopportunity to offer certain data rate anywhere but to fulfil the QoE requirementsin such case is an open research challenge. Additionally, a specific aspect risingin dense urban locations is that the users are likely to gather and move in "dy-namic crowds". For instance, the traffic jam could lead to sudden high peaks ofMBB demand on a network which also has a dense deployment of MTDs. In suchcase, mobile technology have to provide service comparable to the wired broadbandservice with optical fibre in terms of data rate, reliable connectivity and latency.

2.1.1.1 KPIs and Requirements

This use case considers both IoT and HTC devices are connecting to the cloudservers and also with other devices or sensors located in close vicinity. In thatview, the essential requirements states in this use case are:

• 95% area coverage• Experienced throughput of 1 Gbps and 50 Mbps for DL and UL, respectively

2.1. USE CASES 23

Table 2.1: KPIs and Requirements for Dense Urban Information Society [1].

Parameter ValuesPerformance targets

Experienced user throughput 300 Mbps in DL50 Mbps in UL

Traffic volume density (busy hour) 750 Gbps/km2 DL and 125 Gbps/km2

ULE2E RTT Latency locally: less than 5 msAvailability and reliability 95% in space and time

ConstraintsEnergy consumption (infrastructure) The network energy consumption

should be comparable to the energyconsumption of today’s metropolitandeployments, despite the drasticallyincreased amount of traffic

Energy consumption (UE or other de-vices)

Energy consumption for sensor devicesshould be less so that the batterydriven devices can sustain for morethan ten years.

Cost (infrastructure) Infrastructure cost should be kept onthe same level per area as today

Cost (UE or other devices) the cost of the radio part of the sen-sor should be within the range of a feweuros

Use case definitionUser/device density up to 200,000 users per km2

Traffic volume/type 500 Gbyte/month/subscriberUser type Mainly human generated and con-

sumed traffic, and sensor device activein longer duty cycle

User mobility Most of the users, devices, have veloci-ties up to 3 km/h, in some cases up to50 km/h


Figure 2.2: Massive MTC [1].

• Average traffic volume of human and IoT devices is 500 Gbyte per device andmonth.

This use case considered a mix of diverse traffic forms like bursty traffic, videostreaming and browsing. Also, the delay is assumed to be less than 10 ms1. Therelevant key performance indications that stated in this use case are listed in Table2.1. For more details one can refer to [1].

2.1.2 Massive Distribution of Sensors and ActuatorsThis use case is based on the scenario of Ubiquitous things communicating. Accord-ing to NGMN 5G initiative project, and METIS I and II, this scenario addressesthe communication requirements of a large deployed varied type of MTC devicesas illustrated in Fig. 2.2. The services range from smart wearables to medical orindustrial services, in turn, results widely varying requirements on KPIs which can-not always be best met by today’s cellular networks [51]. Integration of ubiquitousthings in existing network and the management of this large number of devices aretwo main key challenges pointed in this use case. We believe, the importance ofthis use case will grow with the dense deployment of low cost and battery drivendevices. A range of services examples have offered in [1] [19] [51].

2.1.2.1 KPIs and Requirements

This use case aims at the number of concurrently connected devices, energy con-sumption and cost of MTDs and network equipments. The performance objectivesare defined as below.

• 1 000 000 devices per km2 should be supported by the network.1For future 5G systems.

2.1. USE CASES 25

Table 2.2: KPIs and Requirements for Massive MTC.


Battery life At least 10 years (assuming 5 Watts-hour battery)

Device density 10,00,000 devices per km2

Availability 99.9%Traffic volume per device From few bytes per day to 125 bytes

per secondConstraints

Energy consumption (infrastructure) In principle no specific constraints forthe infrastructure

Energy consumption (UE or other de-vices)

The power supply availability is lim-ited, so low-energy operation is re-quired. For sensor type devices withbattery power supply only, the energy-optimized operation is required.


Cost (UE or other devices) For sensor type devices a significantcost reduction compared to normalhandset devices is needed.

• Payload size is assumed to be very small, up to 125 bytes per message, witha considerably large transmission cycle, which is application specific.

• Long battery life (in the order of 10+ years) of the wireless device.• Minimum possible signalling overhead.• Keeping the UE complexity low to guarantee ultra-low cost devices.• 99.9% access availability.Other KPIs are listed in Table 2.2 [1].

2.1.3 MarathonPrevious use cases consider dense deployment of sensors with human type traffic.But, it does not consider the challenges of IoT devices in mobility or in the crowd.So the purpose of this use case is to cover these contexts by consolidating theMETIS2020 scenario ’Great service in a crowd’. In that view, marathon event isthe best-fitted scene which is a long distance running race with an official distanceof 42.195 kilometres. In recent years, more and more people are participating insuch kind of events where participants run on a bounded track usually in urbanareas. Typically, 4-10 hours long event where tens of thousands of athletes partici-pate and millions of spectators meet in a particular area. It is envisioned that all


Figure 2.3: Marathon.

the participants in the race will carry wearable sensors during the event to trackparticipants’ position, and measure participants’ wellbeing, and performance.

Sony SmartBand, Nike+ FuelBand, Google Glasses, iWatch, Gear Watch andAutographer are some examples of first generation gadgets of Wearable technolo-gies. Still, these technologies draw the attention of customers. A new study fromRackspace titled "The Human Cloud: Wearable Technology from Novelty to Pro-ductivity" reports 18% of the United States and United Kingdom’s population arethe user of current wearable devices [51]. More than eighty percent of these usersin both countries believe that these types of services are helping them to improvetheir daily activities. Different market forecasts on the future IoT and wearabledevices agree on the exponential growth of wearable MTC devices by 2020.

Usually, Professional athletes are anxious to discern about their physical stateand the nearby contestant’s position. On the other hand, amateur contestantswant to make their run memorable. They prefer to upload pictures and live streamvideos using wearable gadgets like Google Glass, Recon Jet, and Epiphany Eyewearto the storage cloud or stream music during the run. Moreover, the spectators onthe roadside and at the finishing point want frequent update about the athletes’performance, position. Also, crowd may be interested in accessing multimediaservices e.g. music, videos. Additionally, at the event, there are hundreds of garbagebins, portable toilets, vending machines, food stands, and other services that willdepend on wireless communication to upkeep pleasant services.

Events like New York, Tokyo, and Berlin marathon, at present, have around50,000 finishers2. Total participant numbers are in tens of thousands of the finisher’snumber. This massive number of devices needs to be supported by cells in thatbounded region3 of cellular networks.

The overall challenge is to offer a reliable and scalable network service to a largenumber of users temporarily positioned and moving in a certain area covered by

2Fig. 2.3 is showing the runners participated in New York marathon.3at least at the starting and finishing points

2.1. USE CASES 27

multiple nodes. The mentioned services traffic and duty-cycle are different fromHTC requirements. So, we envision that all participants in future marathon willuse cloth attached tracking devices, i.e. more than 70,000 tracking devices will bedeployed in future marathon events. Furthermore, more than 40 000 devices willbe used for fitness, and another 15 000 devices will be used for mHealth. In orderto support this large number of varied MTC devices a radio network require highaccess and signalling competence to meet the estimated user capacity and latencyrequirements. Additionally, the service, has to provide for very limited time periods,placing constraints from a cost perspective of the network deployment.

Unlike formerly stated use-cases, in this use-case, we focused on wellbeing andtracking services that entail relatively more frequent transmission of small datacompared to smart home sensors and actuators. Besides, mobility of a massivenumber of devices in a limited area with substantial heterogeneous services intro-duce a distinctive set of traffic scenario for the wireless radio systems.

Table 2.3: Marathon-IoT Service Example.

Type of devices Number of Devices Activity cycleTracking 70000 1x/2 minFitness 40000 1x/30 minmHealth 15000 1x/min

Based on the discussion, we further shortlist potential services envisioned to useby the runners are presented in Table 2.3. In this example we consider the NewYork marathon event where more than 50000 participants finish the race each year.

2.1.3.1 Requirements and KPIs

In this sense, marathon use case can be considered as an operator-centric use case.IoT and wearable IoT services traffic patterns are different from usual HTC traf-fic. Along with aforementioned MTC service requirements wearable devices havethe added requirement which is mobility. Most of the wearable device based ser-vices pointed out in this use case are basically uplink limited services. Regardless,wearable terminals infrequent and small packet burst trends; end users should havesteady, reliable and fast connection where the data rate requirement is very low forMTC devices. But, the connection setup time of packets should be less than 10 msto assure minimal power utilization per transmission period.

Any solution that will be applied in the Marathon use case shall be evaluatedin terms of:• User Capacity (networks scalability),• Lower Latency,• Battery Lifetime,• Energy Efficiency and,• Cost Efficiency.


Table 2.4: Requirements and KPIs for the Marathon Use Case.


Traffic volume density 0.03-1 Mbps/m2 / (cell radius 500 m2)Experienced user throughput Low throughput 0.3-5 Mbps (UL)Latency RAN latency less than 1 msAvailability >95 % within The event areaReliability >95 %

ConstraintsEnergy consumption (infrastructure) In principle no specific constraints for

the infrastructureEnergy consumption (UE or other de-vices)

Low-energy operation is required. ForWearable devices with battery powersupply only, the energy-optimized op-eration is required to extend the bat-tery lifetime.


Cost (UE or other devices) For Wearable devices a significant costreduction compared to normal handsetdevices is required.

Use case definition

User/device density 20-50 thousand participants1-2 million spectators

Traffic volume/type 4.5 Mbyte/hour/deviceUser type UL users mostlyUser mobility High

The requirements and KPIs are summarized in Table 2.4 [1].

2.1.4 Refined RequirementsIt is impractical and impossible to consider all the above mentioned use cases for theevaluation of our proposed solutions. Thus, we identified the similar requirementsand gap between the use cases. This section presents the identified commonal-ties without losing the benefits of the detailed portrayal of considered use casesmentioned in previous section.

• Device density:

– High: ≤300000 devices per km2– Baseline: ≤10000 devices per

2.1. USE CASES 29

– Low: ≤10000 devices per km2

• Mobility

– No: Static devices– Low: 0–3 km/h– Medium: 3–50 km/h– High: > 50 km/h

• Deployment Strategies

– Only Macro cells– Few small cells per Macro cell– Large number of small cells per Macro cell

• Service type

– HTC: Mobile broadband is the key requirement of human type traffic– mMTC: massive connectivity is the key service requirement of the IoT

service traffic.

• Traffic Type

– Continuous– Bursty– Event Driven– Periodic

• User Data Rate

– HTC: 50 Mbps/user– MTC: < 1 Mbps/sensor device

• Latency

– HTC: 50 ms/user– MTC: 1-10 ms/sensor device

• Access Reliability

– HTC: <95%– MTC: >95%∼99.99%

These identified requirements are used to evaluate the result presented in thisdissertation. The system model to evaluate those results is discussed in the nextsection.


Figure 2.4: Cellular Connection.

2.2 Competing Network Architectures

In recent years, a number of potential wireless network deployment strategies wereproposed to support IoT services with exisitng radio access solutions. Two mainapproaches that have been standardised are: i) cellular and ii) capillary networks.In this section, we discuss the benefits and downsides of these two deploymentstrategies.

Cellular NetworksCellular networks for IoT refers to the architecture in which each MTC devices(MTDs) would have a direct connection to cellular networks like GSM, 4G, future5G or LPWA systems. Fig. 2.4 shows a simplified diagram of such systems ar-chitecture. Ubiquitous coverage, mobility, security and device management withcertain quality of service (QoS) assurance are major benefits that this approachoffers. However, a large coverage area also means an increase of devices within thatparticular covered area which can become a hurdle for cellular systems.

Capillary Networks (CaNs)On the other hand, gateway based network is typically an independent local networkthat uses short-range radio access technologies that provides connectivity to groupsof MMC devices (MMDs) [40]. Gateways may or may not connect to the mobilenetwork. If the gateway is connected to cellular networks, it is defined as capillarynetworks. Otherwise, it is called cluster networks. In cluster networks, the gatewaysare connected to the Internet usually via wired LAN i.e. fiber optics. Fig. 2.5 showsa schematic illustration of these two options.

2.3. SYSTEM MODEL 31

Figure 2.5: Capillary Networks.

The main gains of the capillary networks are its low cost and low power con-sumption of MMDs, aggregation of packets and scalability ability. However, lackof mobility makes capillary based service enabling infeasible for many IoT appli-cations, such as logistics, connected vehicles, monitoring and control over largeareas like forests surveillance and security. Additionally, aggregation only worksbetter for a service specific scenario. In the context of multi-service cases, if onegateway has devices that belong to various application owners it could get quitedifficult to aggregate packets and in some cases losses the aggregation gain. Alsocovering large areas will be very costly with such technology deployment and theownership of the devices is still not clear. Finally, interference is a major ob-stacle for dense deployments of gateways as well. It is not clear how to assurethe QoS for different services and who will deploy and maintain the gateways.

For this reason, the cellular network is still a feasible and viable solution fromtechnological and economical perspective. Indeed, some key challenges needed to beanswered to make cellular systems able to handle IoT traffic. Capillary gatewayscan be an option to scale the cellular network but, in such case, QoS should beensured within the capillary network.

2.3 System Model

We consider a LTE-A three cells scenario where HTC and MTC communicationsshare the same radio resources. We assume that only one cell is overloaded withdensely deployed terminals and other cells have fewer devices within the cell range.We assume each device is characterised by different IoT applicability in terms ofmobility. We assume all the devices are capable of operating both in long rangeaccess technology i.e. cellular networks and short-range access technology i.e. groupbased networks. In such scenario, we investigate the parking lot, smart-metering


Figure 2.6: Simulation Scenario.

and video surveillance applications in heterogeneous traffic scenario context whereeach device is characterised by different duty cycle. We consider an event-baseddevice triggering wherein MTC devices, attempts to transmit data towards MTCserver simultaneously. Furthermore, we assume IoT data are typically upstreamfor aforementioned kind of applications, and they are assumed to be similar forall the devices since they should perform similar tasks in this case. We assumethe base station at the centre of the cell, and there is one radio per cell. In suchcase, the relative distances from the base station to the devices do not affect theanalysis and only the congestion affect the system. We assume the devices arerandomly distributed within the cell to create non-uniform heterogeneous trafficfrom any geolocation. We assume the devices usually have low mobility, i.e. theMTDs do not move, move infrequently or move in a predefined region/path. Wefurther assume the MTC devices are battery powered sensors mostly.

Further of the study, we consider a gateway based group formation where alimited number of MTDs connect through capillary gateways. Each member of agroup will send their data to the gateway using a short-range Radio Access Tech-nology (RAT), and the gateway will collect all the data of its group and will sendthem to the base station periodically. We consider IEEE802.15.4 as the short-rangetechnology. We present the in-group performance evaluation between Wi-Fi andIEEE 802.15.4 in paper A. We assume the group head as a member of the group.As illustrated in Fig. 2.6 the gateways can connect to the Internet via cellular orwired networks. For HetNet scenarios, we further assume the macro BS is respon-sible to distribute system information and control the connection for all gateways

2.4. DEVICE ARRIVAL MODEL 33

in the cell as illustrated in Fig. 2.7.

2.4 Device Arrival Model

To the view of the use cases, we consider event-driven traffic arrival in order toevaluate the network performance. Poisson arrival process is the commonly usedmodel for random and mutually independent message arrivals [52]. The traditionalPoisson arrival model can be used for performance evaluation of human type trafficarrival. But this model cannot fully characterise the coexistence of event-drivenMTC traffic with regular HTC traffic. Most importantly, the bursty arrival of MTCtraffic along with coexistence traffic can not be replicated properly. On the otherhand, Beta arrival model only emphasise the simultaneous arrival characteristics.For that, in this dissertation, we model the device arrival as a two-phase MarkovModulated Poisson Processes (MMPP) also known as a switched-Poisson process(SPP). This model is a good match with the new and infrequent bursty arrivalsof HTC and MTC traffic to the system. With SPP we introduce high and lowarrival rate of devices. The lengths of high and low arrival rate can be modelled asLh = nhτ and Ll = nlτ , where nh and nl is the number of devices for high and lowactive devices, respectively [40].

As we are interested to see the single burst effect, we further use InterruptedPoisson Process (IPP) arrival model and let high state to represent the burst andlow state to silent state [53] as shown in Fig. 2.8. Detailed model can be found inPaper E.

2.5 Performance Evaluation Method

The performance of the cellular and cluster networks are evaluated by tailored NS3simulator where cellular RAN, and Wi-Fi is used as long-range and short-rangetechnologies, respectively. We use a unified three-cell scenario where we assumeone cell has a dense deployment of user and sensor devices. We consider LTE-FDDmode for the considered scenario, in which: (i) PRACH period is set to 20ms,

Figure 2.7: Control Signalling Controlled by LTE-A [2].


Time (hours)

Tra

ffic

de

nsi

ty

(nu

mb

er

of

de

vic

es)

0 6 12 18 24 30 36 42 48

HTC traffic

pattern

MTC traffic pattern

(Event driven)

MTC traffic pattern

(Regular

Measurement

Reporting )

Figure 2.8: HTC and MTC Multiservice Device Arrival Pattern.

(ii) all devices are downlink-synchronized, and (iii) all devices have received therequired configuration parameters related to the initial access procedure during thecell selection procedure. When it comes to the cellular networks control plane’sperformance evaluation, we consider three connection strategies.

• Devices directly communicate to the cellular networks;• Some devices are offloaded to the gateways, and the rest of the devices have

direct cellular connection;• Devices are connected to the network via gateways.

We compare the performance in terms of number of simultaneous devices can behandled within a slot or frame.

2.5.0.1 Simulation verification and validation

We use the existing NS3 module in order to replicate the standard access controlprocedures. To evaluate our proposed model we develop the module on top of theLTE module available in version 3.21.to verify the correct operation of the simula-tion modules, We verified the correct operation of these components using manualsimulation traces. Our simulations contain a substantial amount of debugging codewhich can be turned on or off with a flag. We have generated packet traces toensure the correct control signal flow between server, LTE system and devices. inaddition, we have run each scenario 10000 times and found the confidence interval94% for admission control results and 99% for initial access results. The modulesdeveloped during this study have been made available4 as well.

4https : //bitbucket.org/istiakhossainshanto/rawithrsrpdpd

2.5. PERFORMANCE EVALUATION METHOD 35

Figure 2.9: HTC and MTC Multiservice Device Arrival Pattern.

KPIs of Interest

In order to evaluate the performance of the capillary and the cellular networks weconsider two metrics. These are:

• Delay: this corresponds to the time elapsed between the first access requestsends till the moment payload is available in the IP layer.

• Active device ratio: represents the ratio of the number of devices actuallybeing able to access the network amongst all the devices trying to access inthe system.

Furthermore, two KPIs have taken into account to compare the performance ofcontrol plane’s signalling mechanisms’.

• Average signalling load: Average number of uplink control signal trans-mitted between devices for connection setup.

• Signalling success rate: It relates the percentage of devices that get accessto the data plane and are able to transmit information data.

Also, following performance metrics have taken into account to evaluate the


initial access performance of cellular networks:• Access Success Rate: defined as the probability to complete the random

access procedure in the maximum allowed number of preamble transmissions.• Collision Probability: defined as the ratio between the number of preamble

collisions and the number of preambles transmitted in a PRACH slot.

2.5.0.2 Connection Setup Procedure

Connection setup is a transition period from an IDLE to CONNNECTED state.The required steps and time to establish a connection is illustrated in Fig. 2.9.Total latency to establish a connection with existing connection setup mechanismis between 56 ms to around 150 ms. All the intermediate steps are consideredduring the calculation. In this dissertation, we focus on this core procedure andimprove the performance of the connection setup procedure in medium and highaccess load.

2.6 Simulation Parameters

The data packet size is considered 100 bytes and 1048 bytes for MTC traffic andMBB traffic, respectively. We evaluate the cellular and Wi-Fi systems performancein frequency band 1.8 GHz and 5 GHz, respectively. All devices are going fromEPS IDLE to EPS CONNECTED mode within a short interval. In this study, weconsider that only one slot is reserved for collision resolution, in order to evaluatesystem performance in an extreme case. It is also assumed that the BS will notbe able to detect the simultaneous transmission of the same preamble to replicatecurrent system. We evaluate up to 300 simultaneous devices attempt using thesimulation parameters stated in Table 2.5. More case specific parameters can befound in the appended papers.

2.6. SIMULATION PARAMETERS 37

Table 2.5: Simulation Assumptions.

Parameter Values UnitNo. of users/cell 1-300 int.

No. of transmissions 4 per deviceNo. of session 3Packet Size 100 Bytes

Simulation time 20 sPath loss model Friis Propagation Loss ModelError Model Link Error

Maximum Transmission 10Bit Error Rate 0.00005

Backhaul Data Rate 1 GbpsBackhaul Delay 0 µsBackhaul MTU 3000

Allocated bandwidth 3 MHzNo. of CaN GWs per cell 3 int.Number of M2M device 30%No. Available preambles 50 int.

Backoff Indicator 0 msPRACH period 20 ms

Max Preamble retransmissions 20 int.RAR window size 3 ms

Connection request timeout 60 ms∆T .05 .1 .25 .5 µs

BS Estimation Error [ -1, 1 ] µsCell radius 2 km

M2M device Ratio 40 %Uplink BW for control channel 60 MHz

RBG for Control channel 50 int.RB per RBG 6 int.

Chapter 3

Enhanced Admission Control

In this chapter, we first discuss control overload triggered by the deployment of amassive number of machines in cellular networks like LTE, LTE-A. Then, discuss thebenefits and tradeoffs of the group based congestion control technique in a specificIoT application scenario1. Finally, we present the notion of dynamic grouping withgranular control over group devices to demonstrate the significant performanceimprovement on cellular networks by using a proposed admission control method.

3.1 Gateway based Congestion Control for CellularNetworks

To study the impact of IoT services on cellular networks we consider a typical MTCuse case of a parking lot. The main goal of this study is to perform a sensitivity testand motivate the fact of massive access impact on capillary and cellular networksto understand the severity of this problem. A parking lot scenario in such case isa realistic example of the future dense urban scene. In such cases, a large numberof MTDs like the cars and the parking space sensors, even the drivers will try toreach the closest base station to transmit (and sometimes receive) data or to reachthe application. During busy hours especially in mornings and afternoons of theweekdays, a parking lot is expected to witness a significant amount of devices arrivalwhere the sensors and smart devices will try to reach the same base station in aclose interval. Hence, it will be interesting to understand how the system behavesunder such conditions, what conclusions can be drawn from such behaviours, andwhat could be the solutions to these problems. In this section, we answer researchquestion one and two by answering which are the major seen parameters that limitcellular and capillary networks performance from our obtained results.

1Parking lot scenario

39

40 CHAPTER 3. ENHANCED ADMISSION CONTROL

Figure 3.1: Considered Cellular and capillary Grouping in Parking Lot Scenario.

3.1.1 IoT Service Impact over Capillary Networks

Consider a three-storied parking premises where the cellular base station is placedon the rooftop of that same building. Let’s assume there are 300 parking spots perfloor equipped with sensors. The sensor devices in that building are connected tothree capillary gateways. The sensors are uniformly distributed among the gatewaysas depicted in Fig. 3.1.

We consider a short range RATs inside a capillary network. The gateways areconnected to the Internet through the cellular network. We compare the packetloss and delay performance of two short-range radio technologies under differentarrival rate of devices. One of the considered technologies is Wi-Fi which is awell accepted and broadly deployed short range technology. However, from MTCdevice’s battery lifetime perspective Wi-Fi is not a suitable candidate for its highpower consumption and user interface cost characteristics. The other consideredtechnology is 6LoWPAN as it is a strong candidate in favour of MMC because ofits low power consumption, low cost, wide application and acceptance in sensornetworks. 6LoWPAN also supports IPv6 with small data frames and improvedtransmission efficiency with significantly better payloads than other available short-range technologies that can offer similar coverage area as Wi-Fi.

Our results presented in Fig. 3.2 clearly show that if we use technologies like6LowPAN that has a smaller capacity, the group size needs to be significantlysmaller in order to keep the packet loss and delay in acceptable limit. So far we haveonly considered a delay-tolerant service where the performance is acceptable. Now,if we assume other services like the fire alarm sensors, video surveillance cameras

3.1. GATEWAY BASED CONGESTION CONTROL FOR CELLULARNETWORKS 41

(a) Delay

(b) Packet Loss

Figure 3.2: Capillary Networks performance.

using the same gateways; group size plays a vital role as we have to provide anultra-reliable delay-sensitive service for the safety concerns. Furthermore, if webring group dynamicity, and consider solutions like a car theft alarm sensors wherethe car security sensors are connected through the same gateways and transmitmessages more frequently than any other services considered formerly. To meet thestringent service requirements, group size needs to be smaller and large number ofgateways need to deployed to meet the promised QoS. Indeed, we can use differentshort range technologies for different service purpose which is not realistic from thesystem management complexity and cost perspective. For that, group-size is thekey parameter for capillary networks when it comes to heterogeneous services overthe same network.


(a) Delay

(b) Success rate

Figure 3.3: Cellular Networks performance.

3.1.2 IoT Service Impact Over Existing Cellular Networks

Now consider all the sensors are connected to the cellular system directly. Fig.3.3a illustrates the average delay which increases with increased number of devicespresent in the system when the devices are connected to cellular networks. Theaverage delay starts to decline after 225 devices because the base station starts toblock devices to connect in order to maintain existing connection active. Fig. 3.3bfurther support the argument as the success rate graph shows the blocking beginsearlier than the threshold which is 200 active devices per cell in Cellular systems.However, a small increase in delay (Fig. 3.3a) can be observed from around 169 to256 devices even if the corresponding device access rate is decreasing. This happensdue to the contention based random access process in cellular system. Devicessignificant retransmissions increase the delay of successful packet transmission.By

3.2. CELLULAR NETWORK’S SIGNALLING OVERHEAD REDUCTION 43

Figure 3.4: LTE-CaN Flexible Admission Control.

introducing Capillary network, we can see a significant gain on the network capacity.However, this comes with the cost of delay, as illustrated in Fig. 3.3a.

In order to reduce the packet loss with the gateway-based solution, we need todeploy a large number of gateways in hotspot areas as the gateways can cover asmaller area and also the number of the active devices per gateway is a limitingfactor.As we can see from the Fig. 3.2, the delay for a group size greater than 30for IEEE802.15.4 is around 600 ms which is acceptable for a delay tolerant service.However, the devices start experiencing packet loss with such delay may affect otherIoT services connected to the same gateway. This brings the challenge to assureQoS for all type of service traffic. Indeed, these problems are not an issue duringregular operations of the networks as the active device rate in regular traffic scenariois much lower than the overload threshold . But the burstiness of IoT traffic canincrease the occurrence of this type of incident that can interrupt other services.So it will be good to have a dynamic admission control mechanism that can act ondemand and can handle this situation efficiently with the lower number of smallcells to make the small cells deployment cost efficient.

3.2 Cellular Network’s Signalling Overhead Reduction

As we have discussed in Chapter 1, signalling overhead is not effectual enoughto support IoT traffics small payloads. Also, devices’ arrival in close interval cancreate access congestion. For this reason, in this section, we study the possibility toreduce signalling overhead and resolve congestion by offloading traffic to capillary


Figure 3.5: Connection Setup Through CaNs.

networks. We propose an admission control scheme that can provide a group-basedoffloading to support dynamic IoT applications devices under one gateway withguaranteed QoS.

3.2.1 Proposed Solution: Flexible Admission ControlMechanism

We propose a mechanism so that the cellular network can control devices admissionto the gateways. This could be any devices regardless of their service type. Also,this scheme supports to connect through all types of gateways regardless the gate-ways are inside or outside of the network. In such cases, devices get gateway accesscredentials from the core network. We assumed devices connect through gatewaysby using IP-based short-range wireless technologies, e.g., Bluetooth Low Energy,Wi-Fi, 6LoWPAN or D2D technologies. So, we target those devices that have mul-tiple air interfaces or have options to changes between frequencies in half-duplexmanner. Based on devices QoS, mobility, or other device-specific requirements theMME can control the group formation per gateway. When a device appears in acell, it detects nearby CaN GWs while synchronising to the base station. If a de-vice finds multiple gateways in the vicinity, the device lists the best three gatewaysaccording to the received signal strength. Fig. 3.5 shows the flow diagram of theconnection setup procedure between a core and a group device. Before initiatingthis process, a device must be synchronised with the cellular network through thecellular interface of Group 1 as depicted in Fig. 3.4. The details of this procedurecan be found in paper B.

3.2. CELLULAR NETWORK’S SIGNALLING OVERHEAD REDUCTION 45

Figure 3.6: Connection Setup Through OTT Gateways.

Fig. 3.6 illustrates the over the top connection flow of Group 3 as illustratedin Fig. 3.4. Unlike Fig. 3.5, this procedure is to connect to the inter-operatorgateways. In paper B, we propose a new control interface between MME and P-GW, shown in Fig. 3.4 as Sx. If a device is connected via untrusted non-3GPP GWsi.e. a user trusted and accessible private network like Wi-Fi, the device initiatesthe RRC connection along with this connection information. This is an one-bitinformation to let the system aware of this connection. After receiving this, theMME initiate the tunnelling procedure and send tunnel initiation credentials to theP-GW to initiate a tunnel and send that tunnel credential to the device to access thepacket network through an untrusted private network. After the device establishesthe tunnel, it releases the radio connection and transmits data and control packetsvia the same tunnel. One of the core ideas of this scheme is to reuse the samegateways credentials until a device is not connected for a longer period like morethan 10 minutes. This scheme also introduces the novel core controlled verticalhandover possibility between cellular and capillary networks.

This core network assisted admission is a useful method for dense and less loadednetwork scenarios. In case the serving cell is less loaded, and a gateway is over-loaded, the core network can start a capillary detach and assist a device in switchingto cellular interface or vice versa. This can decrease device and gateways powerconsumption but still can increase service accessibility and link reliability. Thismethod supports scalability and dynamic grouping for cellular-capillary networks,achieving significant signalling reduction by using capillary over cellular networkswhich is going to be discussed in the next section.


Number of devices

0 50 100 150 200 250 300

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trol

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(b) Signalling success rate

Figure 3.7: Performance of Flexible Admission Control Mechanism.

3.2.2 Performance Evaluation

This section continues to answer research question one and two by addressing thebottlenecks at the control channel. In addition to that, we answer research questionthree by showing the gains of the proposed core assisted capillary offloading scheme.

Fig. 3.7a shows total signalling load of MTC data transmissions in three ses-sions. From the figure, it is evident that LTE Dedicated connection generates theleast amount of control signalling but at the price of low scalability and resource

3.3. DISCUSSION OF THE RESULTS 47

utilisation. ’Connect when to transmit’ approach, in that view, costs more signallingthan any other connectivity options. However, among the considered cases, staticcapillary requires 35% less signalling than ’connect when to transmit’ approach.Flexible admission control mechanism starts redirecting devices when radio net-work experience more than 5% packet loss with 50 ms latency on the data plane.The cellular network reaches that threshold for more than 50 devices simultaneoustransmission and EPS activates the mechanism that assists some devices to moveto the capillary gateway. This mechanism not only decreases the signalling loadbut also increase the number of simultaneous devices access with packet loss lowerthan 5%.

Fig. 3.7b shows the success rate where it is evident that static capillary andour proposed solution outperform the cellular networks performance. We can see,the system performance starts degrading for more than 275 concurrent connections.This is mainly because of the capacity limitation of the gateways. In order to in-crease the capacity gain, we can offload more devices to the capillary network. Butbecause of the group size limitation, as discussed in Section 3.1.1, we need to den-sify the capillary network with more gateways which will increase the deploymentand maintenance cost of the capillary networks. In addition to that, our resultspresented in paper B show significant improvement over signalling setup delay andsignalling to packet overhead ratio. We can reduce 10% packet overhead and canachieve 50 ms faster connection setup than regular procedure.

If we consider ’massive distribution of sensors’ use case described in Section2.1.2, with our proposed scheme we can manage the sensor devices in a betterway than today’s system. At the same time, we can make sure the utmost possibleresource utilisation of capillary and cellular systems without blocking the new trafficarrival. Massive access of devices due to fault detection services like fire alarm canbe handled without having control channel overload. Also, more heavy duty cycleddevices like tracking sensors can be handled by the same system. Small payloads canbe handled well with such solution, as the signalling cost to establish a connectionis lower. With some software updates existing LTE base station can handle around3,00,000 connections per ENodeB in 5 minutes where current LTE-A system canhandle upto 1,50,000 connections in ten minutes. In turn, this opens a significantscalability opportunity for current cellular systems.

3.3 Discussion of the Results

From the discussion above, we can say the gateway base solutions have huge po-tential in solving IoT created congestion under certain application limitations. Itis very important to notice that supporting a large number of devices without anyor micro-congestion2 may achieve with the cost of delay using any gateway based

2Micro congestion in this sense refers to the scenario when the distribution of devices is notequal among different gateways. In such scene, congestion could occur in some of the gateways ina geolocation. We called this as micro congestion.


technologies. However, the main seen trade-off of this solution is the scalabilityand group size that can cause congestion in capillary gateways. A proper groupsize is challenging if there is no control over the group. This very much dependson different technologies capacity. According to our results, a proper group size toassure 100% success rate with less than 100 ms delay for 6LowPAN and WiFi is 10and 50 devices3. So, in existing system, it is not possible for the network operator’sto guarantee any QoS over capillary networks. This narrows down the applicabilityof gateway-based solutions to only delay-tolerant systems.

When it comes to cellular network, we can see from our discussion, that existingcellular system’s access control plane is congestion-prone when it comes to smallpayload transmission. From the results, we argue that proper optimisation of cap-illary networks can be useful to avoid radio congestion by offloading control trafficto the capillary network. Our results show that the proposed scheme can assure98% success rate in high access load. Also, can maintain lower signalling load overcellular network control plane. As well, operators can guarantee certain QoS overCapillary network as they can achieve granular control over group devices whichcan assure efficient device management. Our results also indicate that optimumsignalling reduction is possible with dedicated connection type, as least connectionsetup to transmit all the data certainly means better signalling to payload ratio.

However, we noticed that initial access congestion is more severe and sensitivethan control plane congestion when it comes to the concurrent arrival of requests.Devices’ retransmission rate increases before successfully connect to the network.Also, as RACH resources per PRACH slot is limited and conventionally less thanthe access control plane’s resources, PRACH overload can occur even earlier thanthe access control channel congestion. For that, in our next chapter, we focus onthe initial access forged challenges of cellular networks.

3provided that in any incident the CaN doesn’t perform worse that the speculated require-ments.

Chapter 4

Enhanced Initial Access

In the previous chapter, we observe that the access congestion is the rudimentarybarrier even the access control plane is capable of handling a vast number of devicesper cell. For that, we looked into the initial access performance and proposedsome method to overcome the initial access performance. In this chapter, we firstpresent simultaneous devices impact on initial access mechanism. Then we presenta simplistic approach to address this issue.

4.1 Initial Access Performance

In cellular networks, random access procedure is the typical way to reserve radioresources in the data plane and create uplink time-synchronisation among the BSand the device [26]. We can say the RACH performs the role of a gatekeeper toavoid access congestion in the control and data plane. According to the highlighteduse cases in Section 2.1, monitoring services like flood monitoring remote sensors,

Figure 4.1: RA Collision.

49

50 CHAPTER 4. ENHANCED INITIAL ACCESS

Number of preambles detected by BS at one PRACH slot

0 5 10 15 20 25 30 35 40 45 50

Pro

ba

bili

ty o

f D

evic

e C

olli

sio

n

0

0.2

0.4

0.6

0.8

1

Pro

ba

bili

ty o

f C

olli

sio

n O

ccu

rre

nce

0

0.2

0.4

0.6

0.8

1

Collision occurance rate

Device collision rate

Saturation Point

Figure 4.2: Sensitivity Test of Collision Occurrence.

smart metering, and seismograph sensors may have variant data transmission re-quirements. This communication requirement could be as low as one packet per dayto one packet transmission per minute. At the same time, services like vehicular-type communications, video surveillance, and smart grid need at least one packettransmission per minute [54]. Indeed, existing cellular systems are capable of sup-porting small and sporadic transmission per day with few retransmission attempts.However, the system is only capable of handling few concurrent attempts. In turns,when a reasonable number of devices arrive within a short interval the system han-dle this situation by introducing access blocking, delay or by assigning additionalphysical resources to the system. Our studies show that in dense MTC deploy-ment scenarios the access collision rate increases, which in turn increase devices’retransmission rates, and hence, decreases devices battery lifetimes [55]. In short,simultaneous access increases the access congestion as the preamble per PRACHslot is limited. This is true for any existing systems which use signature basedslotted Aloha protocol. As showed in the Fig. 4.1, this happens because the basestation cannot sense a collision in the early phase of the access procedure if severalaccess requests with similar preamble arrive within certain delay spread interval.When such situation occurs, collided devices take a few HARQ retrials before realis-ing the collision and backoff to restart the procedure again. This type of contentionresolution procedure takes a few long moments to take the decision and make someadditional device-battery usage. There are many congestion control methods, butnone can solve the collision problem without introducing extra delay or resourcesto the system as explained in Section 1.3.1.2.

In such context, at first, we discuss the severity of such situation. We showedhow frequent such collisions can occur. Our result in Fig. 4.2 shows that it can

4.1. INITIAL ACCESS PERFORMANCE 51

Figure 4.3: DERA Procedure.

occur regularly as more than ten simultaneous devices arrival gives 60% chance ofcollision among the arrived devices. The collision occurrence rate drives above 95%for more than 15 devices [54]. The collision becomes even critical in contentionbased random access when the set of preambles is further subdivided into twosubgroups to transmit one bit of information relating to the amount of transmissionresource needed to transmit the message at step 3. If we consider the New Yorkmarathon event discussed in Section 2.1.3, only the considered services can generatemore than ten concurrent connection requests per frame where the activity of thedevices’ are uniformly distributed over time. In this case, the chances of collisionoccurrence are more than 60%. If we consider the spectators’ activity, this willbe even higher, and the asynchronous activity will increase the concurrent arrivalpossibility. This astonishing fact motivates us to look into this problem and find away to solve the collision in the early stage of the initial access procedure.

4.1.1 Proposed SchemesIn this section, we present the solution followed by the performance analysis.

4.1.1.1 Delay Estimation Based Random Access (DERA)

A novel initial access method DERA was proposed in [55], which is a delay esti-mation based collision awareness mechanism. Fig. 4.3 illustrated the flow of theproposed initial access procedure. This scheme accelerates the collision detectionand resolution in the early stage of a random access procedure. In large coverage


Figure 4.4: PD Calculation.

area, this scheme can improve the collision cognizance and efficiently decrease theaverage access delay and the consumed energy of devices in RA procedure by re-ducing the retransmission rate. The proposed scheme exploits the fact that for eachdevice encountered propagation delay (PD) is different, which can be well estimatedby the BS and the device [54]. Then using the BSs assistance, one of the collideddevices can complete the initial access successfully; while other devices can realisethe collision and restart a new session in the very early stage.

As illustrated in paper D, If the power delay profile (PDP) detects a RACHpreamble, peak searching function can estimate the preamble. Also, as the peakposition is affected by the cyclic shift and propagation delay, the preamble detectionmodule can calculate the PD if the quantity of propagation delay in temporaldomain is less than the unit cyclic shift value [56], as follows:

TBSP D = Tx − Tref . (4.1)

In this expression, Tref is the reference time at the BS, and Tx is the time atwhich the preamble from device x is received, as depicted in Fig. 4.4. Then, BStransmits a message comprising TBS

P D and BS’s processing period, along with RAR(from this moment we call it enhanced RAR (ERAR) for the convenience of furtherdiscussion), in the second step 2. The BS processing time for each request, i.e. TBS

L ,is defined as

TBSL = |TRACH

RX − TERART X |, (4.2)

where, TRACHRX is the PRACH received time at the BS antenna connector point,

and TRARx is the RAR transmission time at the BS antenna connector. In our

scheme, each device calculates its PD by using the Timing Alignment MSG 2 for-mula [25], i.e.

TDeviceP D = 0.5[(TERAR

Rx − TP RACHT x )− TBS

L ] (4.3)where, TERAR

Rx is the time at which ERAR is received at the device end, andTP RACH

x is the time at which the device sends the preamble. Then the device

4.1. INITIAL ACCESS PERFORMANCE 53

Simultaneous Arrivals

50 100 150 200 250 300

Su

ccess R

ate

0

0.1

0.2

0.3

0.4

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0.6

0.7

0.8

0.9

1

Conv. RA

EAB enabled RA

DERA, " T=0.057s

DERA, " T=0.17s

DERA, " T=0.257s

DERA, " T=0.57s

150 200 250 3000,99996

0,99997

0,99998

0,99999

1

(a) Access Success Rate

Simultaneous Arrivals

50 100 150 200 250 300

Co

llis

ion

Pro

bab

ilit

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

EAB enabled RA

DERA, ∆T= 0.05µs

DERA, ∆T= 0.1µs

DERA, ∆T= 0.25µs

DERA, ∆T= 0.5µs

(b) Collision Rate

Figure 4.5: RA Performance Comparisons.

Compare its own calculated PD with the received PD embedded with the RAR.This provides the device with an awareness of collision for each received RAR.Based on this, each contending device can know the RAR that matched the usedpreamble is intended for it or not. More details can be found in paper C.

4.1.2 Performance EvaluationWe compare three different random access schemes: 1) conventional random access,ii) random access with congestion control mechanism iii) our proposed scheme.


From Fig. 4.5a, one can see the conventional system performs efficiently in lowload. However, the system starts collapsing after it reaches the saturation point of150 simultaneous devices arrival. Although in the simulation, we consider a casethat after each arrival there is no burst in the next slots; devices fail to get accessto the allowed retransmission window which was set to 20 in this case. Congestioncontrol scheme with access barring for different service profile can significantlyimprove the performance but with the cost of delay as illustrated in paper D. Fig.4.5b shows the collision rate where we can see that even with the barring methodcongestion rate is noticeably high. The only way to reduce such effect, in this case,is to increase the waiting time before each retrial.

In such scene, our proposed scheme, DERA, outperforms other two schemes.With this scheme, we can achieve 99.99% success in the worst case with less thanthree retrials on average. In turn, the access delay is significantly less with lessthan 15% success rate in the worst case. More details of the results can be found inPaper C and D. Furthermore, in Paper D, we have shown the resource utilisationperformance among the considered schemes.

Our results indicate that with a simple modification, we can mitigate the col-lision affect successfully. In turn, the system can be able to handle large devicesaccess without hampering the usual system performance. As discussed in the pre-vious section, events like a marathon where a certain number of devices and usersgather for a short period, the collision could occur frequently. The occurrence ofthe collision which will increase in the existing random access mechanism can beresolved efficiently with our proposed solution.

4.2 Initial Access Channel Dimensioning

In our previous section and in paper D, we saw the retransmission has impact overthe system performance. This may hamper the performance for a longer time byprolonging the collision incident period. For that, we investigate the current RACHchannel resource requirements in order to meet certain reliable accessibility withparticular retransmission window.

4.2.1 Traffic ModelIn paper E, we introduce a new traffic model that can portray the HTC and MTCtraffic profile within an analytical model. We use switched Poisson process to modelsuch traffic pattern.

While SPP can model the device arrival rate, retransmissions after an unsuc-cessful transmission retain challenges in demonstrating the performance of accessrate. Retransmission adds up a transitory state to the system in which a multi-service set of new arriving and retrial nodes compete to access the network. Forthat, a modified SPP model is proposed in paper E which consists of 4 transientstates. The state transitions of the model are illustrated in Fig. 4.7. As the worst

4.2. INITIAL ACCESS CHANNEL DIMENSIONING 55

time

a) RACH planning at the BS !" PUSCH resources RACH resources

time

b) Poisson process for modeling HTC traffic

Aggregated

Arrival rate: #

time

c) SPP model for modeling IoT and HTC traffic coexistence without retransmission

New Arrival

rate: #$ #% #%

Mode transition

probability: &% &$ &%

New Arrival

rate: #$ #%

time

'( )'*) )'+ )'+,* )'+,-) )'.

d) Proposed model for modeling IoT and HTC traffic coexistence with retransmission

Transient states

Mode transition

probability: &% &$ / &%

Figure 4.6: RACH Resource Planning and Traffic Arrival Modelling.

case access rate is achieved in the case of massive access occurrence, in paper E wefocus on the following transition: LAR→ HAR→ transient states. A detail of thismodel can be found in appended Article 5.

4.2.2 Performance Analysis

Based on our novel traffic model, presented in paper E, a closed-form expressionhas been derived to analyse the access rate performance of the system for delay-critical services, in which the number of retransmission attempts is limited. Wevalidate our analytical estimation with our simulation results. We analyticallyexplore RACH dimensioning based on retransmission and access rate. A Numericaland simulations analysis confirms that the derived expressions can be useful forproper RACH planning in time and frequency domains.

Fig. 4.8 delineates access rate as a component of number of preambles and the


UE arrival

rate = !

UE arrival

rate = "

#"

1$#"

UE arrival

rate = !+ %,!

1$#!

UE arrival

rate = "+ %,"

#!

1 $ #! &

1$&

Figure 4.7: State Transition of the Proposed Model.

time interval between two succeeding random access opportunities (RAOs). It isclear that an access rate can be attained by ample resource provision in time andfrequency domains. For example, one can see that 0.9999 access rate is achievedboth with M = 161 and TRA= 1 ms, and M= 261 and TRA= 2 ms. Then, one mayreduce the bandwidth allocation per RACH slot by increasing the number of RAOsper frame. The yellow-shaded areas in Fig. 4.8 determine the (M; TRA) values thatgratify the access rate constraint. One sees that, given the access rate, the limitvalues of M and TRA that fulfil the access rate requirements make an exponentialcurve. Doing a linear approximation of this curve, one sees that the slope of thiscurve is inversely proportional to the access rate, i.e. by increase in the accessrate requirement the substitution rate of M with TRA increases [27]. Form ouroutcome, we can say that the battery driven devices with limited retransmissionsand stringent success rate prerequisites upsurges both the quantity of preamblesper RAOs and the RAOs per frame.

4.3 Discussion of the Results

In this chapter, we present a novel way to improve the initial access performanceby avoiding a collision in the early stage. Results show that the proposed schemecan considerably improve contention resolution and uphold 99.99% success rate inoverloaded scenarios without adding any extra resources or delay to the system.Some minor upgrade can make the system ready to support events like New Yorkmarathon without having access crunch.

Moreover, a new PRACH dimensioning approach is presented which characterisethe retransmissions effects on initial access rate of cellular systems. Findings of thisstudy show the trade-off between access reliability, retransmissions, and resources.Both the parameters have high impact on the PRACH and control channel resourcerequirements. We present a novel traffic model and a closed-form expression toanalyse the access rate performance of the cellular system for delay-critical services,

4.3. DISCUSSION OF THE RESULTS 57

0

40

0.2

0.4

30 400

Acce

ss R

ate

0.6

300

TRA

(× 1 msec)

0.8

20

M (number of preambles)

1

20010 100

0 0

Figure 4.8: Rach Dimenstioning.

in which the number of retransmission attempts is limited. Our numerical andsimulations results signify that to uphold 99.99% access rate existing access planerequires 20 times more resources (time and frequency) than allocated resource toassure 95% success rate on PRACH channel.

Chapter 5

Conclusions and Future Work

This chapter highlights the important contributions discussed in this thesis and thepossible directions of the future work.

5.1 Concluding Remarks

Cellular networks like 4G and future 5G are extensively designed for MBB ser-vices. The rapid growth of cellular IoT devices can cause many new challenges. Tosolve the problem, although the market is heading towards the hard resource splitdirection;We believe, support of heterogeneous services will be the key focus forthe operators in future. The scarcity of precious ISM spectrum, deployment cost ofsmall cells, a large number of devices management issues, and the need for precisionin IoT user data will trigger the need to use single cell for heterogeneous servicesupport. In that aspect, in this thesis, we have focused on the major challenges tosupport massive IoT devices in conventional cellular data networks.

In the dissertation, we present the quantitative analysis of the massive IoT de-vices access impact over existing cellular systems. The first conclusion that IoTservices bursty traffic pattern profoundly affect the existing cellular network’s per-formance of access control plane. Bustry traffic decreases the devices access rateand increases access latency that hampers the regular operation of other servicesin the network. Small payloads of IoT services make the signalling mechanism in-efficient and admission control mechanism vulnerable. Arrival of moderate amountof connection requests like 200 arrivals within 100 ms can overload the admissioncontrol procedure of existing networks. Furthermore, IoT devices concurrent arrivalhas detrimental effects on initial access mechanism. Only 10 simultaneous arrivalsof devices can lead 60% chance of occurring at least a collision. The trade-off be-tween collision, retransmissions and allocated PRACH resource limits the accesssuccess rate considerably and increase the need of additional resources for PRACH.

In answer to the second high-level research question, ’Can we support massiveIoT services with the existing cellular networks access control plane’s resources and

59

60 CHAPTER 5. CONCLUSIONS AND FUTURE WORK

how?’, we can say the existing network can support the regular sporadic operationof IoT devices. According to our results, existing access control plane can handleupto 150 connection within 250 ms. But, the system is not efficient enough tohandle moderate to high access load as envisioned in the considered use cases inChapter 2. Our study clearly indicates that some minor changes over the admissioncontrol and initial access mechanisms can make the networks capable to handle IoTservices. In order to make the existing networks capable to support massive IoTdevices access, we propose two novel schemes to solve admission control and initialaccess limitations. With our proposed solution existing system’s admission controlplane can handle access of 1100 devices per second with 99.99% success rate andinitial access plane can handle 4200 access per second with 99.99% success rate.

We present a group based admission control solution to avoid network conges-tion while supporting massive-multiservice IoT devices over cellular networks. Ourpresented method can overcome the static load balancing constraints which enablesthe cellular core controlled dynamic access of devices into the gateways. With thisscheme, operators can achieve granular control and knowledge about the devicesconnected to the gateways and can ease the cellular-capillary diverse networks man-agement. Operators can control the gateways accessibility depending on the devicesQoS requirements as well. We believe, this will be helpful to monitor and controlthe dynamic group size of the capillary gateways, which is an enabler to assurecertain QoS through capillary networks by controlling group size of the networks.At the same time, capillary devices can be in mobility by using this scheme. Ourresults show that with such scheme cellular system can reduce signalling load up to35% with only 3 capillary networks per cell, 99.99% success rate could be upholdup to 275 simultaneous access.

Initial access is one of its own kinds of barrier for which one cannot assurethe accommodation of a large number of devices per cell. For that, we proposeda novel random access scheme for densely deployed cellular networks to improvethe performance of slotted Aloha-based initial access’s collision resolution mech-anism. Our proposed method can resolve collisions in the early stage to makethe initial access procedure robust to any traffic load. Our present results showthat our scheme enabled initial access mechanism can assure 99.99% access ratein extreme access conditions like New York marathon event or event-driven accessof earthquake monitoring sensors. On the contrary, barring enabled initial accessmechanism may provide around 90% access rate with the cost of intolerable delayand retrials. The proposed scheme can considerably contribute to contention res-olution in overloaded scenarios at the cost of broadcasting no additional overloadrelated information.

These proposed schemes, in this scene, are believed to have a significant impacton future standardisation discussions on a single access control for Het-Net, and anycellular networks faster congestion resolution. The result of the RACH planningcan be used to calculate the access rate for specific retransmission bar and specifictraffic profile of different IoT services over conventional MBB traffic arrival.

5.2. FUTURE WORKS 61

5.2 Future Works

In this thesis, we present the preliminary study of RACH dimensioning. We are stillworking on the dimensioning framework for ultra-reliable communication services.Also, we are working on the business aspects of such type of services. Here, ourpreliminary work shows the technological benefit of capillary networks. In orderto understand the real profitability of such deployment strategy; we plan to do acost performance analysis of short-range gateways to find the cost-effective solutionfor capillary networks. Also, we plan to extend our work to further improve theproposed initial access mechanism.

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