outdoor small cell deployment with complementary spectrum …1096096/fulltext01.pdf ·...

IN DEGREE PROJECT ELECTRICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2017

Outdoor Small Cell Deployment with Complementary Spectrum Authorizations, Licensed (LSA) and Unlicensed (LAA)

Techno-Economic Analysis

FIKRI FIRMAN

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INFORMATION AND COMMUNICATION TECHNOLOGY

Abstract (English)

The signicant increase in mobile data trac has put a considerable loadon the wireless mobile networks. In the current highly competitive market,Mobile Network Operators (MNOs) have to strive to provide additional ca-pacity of their network, by also considering the cost factor to make theirbusiness sustainable. Along with advances in spectrum-ecient technolo-gies, small cells deployment have provided cost-ecient methods to provideadditional capacity for indoor and outdoor subscribers.

The gain of better spectrum utilization and opportunistic spectrum ac-cess have motivated the deployment of wireless networks utilizing below 6GHz spectrum, where there are opportunities for mobile networks to accessthe spectrum by co-existing with incumbent users and technologies. Twoemerging complementary spectrum authorizations have attracted industryand academia, Licensed Shared Access (LSA) and License Assisted Access(LAA).

In this thesis, the techno-economic aspects of operating under individualauthorization (LSA) and general authorization (LAA) regimes are investi-gated and compared. The dynamics of operating under unlicensed spectrumare represented considering the scenario of two MNOs co-existing followingthe regulatory requirements.

The results show that choosing the appropriate channel selection mech-anism is of high importance when operating under the unlicensed regime(LAA). The results indicate that LAA can be an alternative for cost-ecientdeployment method in some scenarios, for example when there is a low ormoderate availability of LSA bandwidth. For the future work, we suggestan optimized user association to the small cells to provide a better load-balancing mechanism.

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Abstract (Swedish)

Den avsevärda ökningen av den mobila datatraken har skapat stor be-lastning på de trådlösa mobilnäten. I den nuvarande mycket konkur- ren-sutsatta marknaden, måste mobiloperatörerna (MNO) sträva efter att skapaytterligare kapacitet i deras nätverk, samtidigt som de måste tänka på kost-nadsfaktorer för att göra sin verksamhet hållbar. Tillsammans med fram-steg inom spektrumeektiv teknik och driftsättning av små basstationer, harman fått fram kostnadseektiva metoder för att öka kapaciteten för inom-och utomhusanvändare.

Fördelen av bättre spektrumanvändning för frekvenser under 6 GHz ochopportunistiska tillgång av spektrum, har motiverat utbyggnaden av trådlösanätverk. Detta möjliggör för mobila nätverk att använda spektrumet genomatt samexistera med etablerade användare och tekniker. Två nya komplet-terande spektrumtillstånd har lockat industrin och den akademiska världen,Licensed Shared Access (LSA) och License Assisted Access (LAA).

I denna avhandling, har de tekno-ekonomiska aspekterna av LSA ochLAA regimer undersökts och jämförts. Dynamiken av drift i olicensieratspektrum representeras i scenariot av två mobilnätsoperatörer samexisteraroch följer lagkraven.

Resultaten indikerar att valet av lämplig mekanism t.ex. val av rätt kanalär av stor betydelse vid användning av olicensierad regim (LAA). Resultatentyder på att LAA kan vara ett alternativ för kostnadseektiv distributions-metod i vissa scenarier, till exempel när det nns en låg eller måttlig till-gång på LSA bandbredd. För det framtida arbetet, föreslår vi en optimeradanvändarassociation till de små cellerna för att ge en bättre lastbalanseringmekanism.

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List of Figures

1.1 Mobile Data Trac Forecast [2] . . . . . . . . . . . . . . . . . 2

2.1 LTE 3GPP Releases, adapted from [10] . . . . . . . . . . . . . 62.2 Spectrum authorization schemes for wireless networks, adapted

from [20] and [21] . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1 Base Stations deployment realization under hexagonal gridand point process . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 System model and the workow of the thesis . . . . . . . . . 24

4.1 Network dimensioning for LAA system for low activity factor 264.2 SINR for dierent channel selection mechanisms, 25.2 GB

monthly usage, λB = 19, low activity factor . . . . . . . . . . 264.3 Downlink throughput for dierent channel selection mecha-

nism, 25.2 GB monthly usage, λB = 19, low activity factor . . 274.4 PDF of SINR for random channel selection, 25.2 GB monthly

usage, λB = 19, low activity factor . . . . . . . . . . . . . . . 284.5 PDF of SINR for deterministic channel selection, 25.2 GB

monthly usage, λB = 19, low activity factor . . . . . . . . . . 284.6 PDF of downlink throughput for random channel selection,

25.2 GB monthly usage, λB = 19, low activity factor . . . . . 294.7 PDF of downlink throughput for deterministic channel selec-

tion, 25.2 GB monthly usage, λB = 19, low activity factor . . 294.8 SINR for dierent channel selection mechanisms, 210.9 GB

monthly usage, λB = 53 . . . . . . . . . . . . . . . . . . . . . 304.9 Downlink throughput for dierent channel selection mecha-

nisms, 210.9 GB monthly usage, λB = 53 . . . . . . . . . . . 304.10 Network dimensioning for LAA system with high activity factor 314.11 Network dimensioning for LSA system with low activity factor 324.12 Network dimensioning for LSA system with high activity factor 324.13 Incremental TCO of network deployments with dierent cost

assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.14 Normalized NPV for dierent deployment type and cost factor 34

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List of Tables

2.1 Spectrum holding of an MNO in Sweden [11] . . . . . . . . . 72.2 LSA incumbent actors and uses and bandwidth availability in

several European countries [23] . . . . . . . . . . . . . . . . . 11

3.1 Mobile data usage in Swedish market [38] . . . . . . . . . . . 193.2 Average population densities for dierent deployment types [40] 203.3 System Parameters . . . . . . . . . . . . . . . . . . . . . . . . 24

4.1 Cost value and normalized cost . . . . . . . . . . . . . . . . . 334.2 Description of deployment types in gure 4.14 . . . . . . . . . 35

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4.1 Benets, Ethics and Sustainability . . . . . . . . . . . 41.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Background and Related Work 62.1 Wireless Cellular Networks . . . . . . . . . . . . . . . . . . . 62.2 Methods to Increase Wireless Network Capacity . . . . . . . . 72.3 Flexible Spectrum Authorization Scheme . . . . . . . . . . . . 102.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Methodology 143.1 Base Stations and Users Distribution . . . . . . . . . . . . . . 143.2 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 Feasible Load Concept and Target Throughput . . . . . . . . 173.4 Network Dimensioning . . . . . . . . . . . . . . . . . . . . . . 18

3.4.1 Mobile Data Consumption . . . . . . . . . . . . . . . . 183.5 Distributed Channel Selection in LAA . . . . . . . . . . . . . 203.6 Interference and Throughput Calculation in LAA . . . . . . . 213.7 Economic Model . . . . . . . . . . . . . . . . . . . . . . . . . 223.8 System Parameters . . . . . . . . . . . . . . . . . . . . . . . . 23

4 Results 254.1 LAA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1.1 Impact of Channel Selection Mechanism on LAA withLow Activity Factor . . . . . . . . . . . . . . . . . . . 25

4.1.2 Impact of Channel Selection Mechanism on LAA withHigh Activity Factor . . . . . . . . . . . . . . . . . . . 28

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Contents vi

4.2 LSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.3 Techno-Economic Discussion . . . . . . . . . . . . . . . . . . . 31

5 Conclusion and Future Work 365.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Chapter 1

Introduction

1.1 Background

The success of GSM (Global System for Mobile Communications) haspaved the way for developing the next generations of mobile cellular net-works. 3GPP (The 3rd Generation Partnership Project) was establishedwith the goal of developing the standards and specications related to cellu-lar telecommunications technologies [1]. Each release introduces new featuresand improved functionality by oering higher data rates, better Quality ofService (QoS) and cost-ecient techniques.

Mobile data trac has increased signicantly and will continue growingfor the next few years. Ericsson in its annual mobility report projected thatthe mobile data trac by 2021 would increase 10-fold with the expected 45%compound annual growth rate (CAGR), as illustrated in Figure 1.1 [2].

Ericsson stated that higher adoption of smartphones and tablets, increasein mobile broadband subscription and higher monthly mobile data usage arethe main factors leading to the growth in global mobile data trac. Ciscoin its Visual Networking Index (VNI) projected that on 2020 the mobiledata trac would increase eightfold compared to 2015 with 53% CAGR [3].Ericsson cited that most mobile video trac came from mobile access tovideo sharing platform such as Youtube, and also noted the increase of videocontent in online application of news, advertisement, and social media.

Internet of Things (IoT) has been taking o for the last couple of years,notably by the introduction of wearable devices and any objects equippedwith sensors and software that are interconnected and able to collect andsend data. Cisco predicted that by the year 2020, there would be 3.1 billionof wearable devices and Machine-to-Machine (M2M) communications thatrely on mobile networks for their connectivity.

The increasing mobile data trac and also new types of communicationsadd signicant load to the mobile network. Mobile operators need to solvethe capacity issue either by increasing the capacity of the mobile network or

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Chapter 1. Introduction 2

Figure 1.1 Mobile Data Trac Forecast [2]

by trac-ooading. Increasing the capacity of mobile networks often impliesincreasing capital and operational expenditure. To make their business moresustainable, mobile operators need to nd cost-eective means in handlingincreasing mobile data trac. The importance of nding such solutionsis emphasized by the trend of wireless service revenue. Ericsson reportedthat revenue growth from mobile services has been slowing down [4]. From2010 to 2014, revenue for mobile services had been increasing with CAGR of2.7%. In 2014, the growth of mobile service revenue was only 1.7%, whichis a steep decline compared to annual growth of 10% to 12% ten years ago.Nevertheless, there is a trend of a healthy growth of mobile data revenuewith CAGR of 34%. These factors further state the importance of increasingnetwork capacity in the most economical way.

Increasing mobile networks capacity can be accomplished by several meth-ods such as network densication, implementing more spectrum-ecienttechniques and by adding more radio spectrum [5, 6]. Each of the meth-ods has its tradeo. Implementing more macro-cell base stations mightraise concern with high costs related to equipment, installations, and siteacquisitions. Heterogeneous networks emerge as a cost-eective solution inproviding additional coverage and capacity by implementing low-power basestations. Adding more spectrum is another straightforward technique to in-crease mobile network capacity. This method is subject to the spectrumavailability, spectrum cost, and regulatory policies. Each country has a reg-ulatory authority that decides how the spectrum divided and allocated todierent applications and services. The Regulator also determines autho-


rization schemes, who can utilize particular part of the spectrum within adened area. Conventionally, spectrum allocation and authorization are im-plemented in a static manner, and there is a presumption that certain partsof the spectrum are underutilized wherein dierent areas of the spectrum(wireless cellular network band) experiencing congestion [7].

Two complementary spectrum authorizations have been proposed for themobile network. They are Licensed Shared Access (LSA) and Licensed-Assisted Access (LAA). Licensed Shared Access (LSA) enables primary spec-trum licensee to grant part of the spectrum to be used by secondary spectrumlicensee with regards to several conditions. Licensed-Assisted Access (LAA)enables utilization of unlicensed spectrum band. The main unlicensed bandconsidered for LAA is the 5 GHz ISM band where the dominant legacy ap-plication is WiFi technology.

1.2 Problem

The introduction of complementary spectrum authorization opens op-portunities for mobile operators to utilize parts of the spectrum that belongto other non-telecommunication actors. This thesis explores the impacts ofexible spectrum authorization on the strategy of mobile operators in pro-viding capacity. The answer to this question is investigated in this thesis:

Under which condition LSA approach is more cost-ecient than LAAapproach for outdoor small cell deployment scenario?

1.3 Purpose

This thesis presents techno-economic study related to the impact of exi-ble spectrum authorization with the strategy of mobile operators. The tech-nical part illustrates gains regarding network capacity and perceived userexperience. The economic part will discuss the cost analysis.

1.4 Goal

The goal of this thesis is the techno-economic discussion of the imple-mentation of dierent spectrum authorizations in wireless cellular networks,namely licensed-operation (LSA) and unlicensed-operation (LAA). The goalis divide into following sub-goals:

1. Investigate the network dimensioning under dierent channel selectionmechanisms when operating in unlicensed spectrum.

2. Investigate the network dimensioning operating under LSA approach,with dierent bandwidth availability.


3. Analyze the capacity and cost aspects for LAA and LSA approach.

The result of this thesis is the strategy that can be adopted by mobileoperators with regards to the opportunities provided by exible spectrumauthorization.

1.4.1 Benets, Ethics and Sustainability

This results of this thesis can be benecial as insight for mobile operatorsin planning for their networks. The operators can decide which method ismore cost-ecient based on the spectrum availability and regulatory frame-works in their respective countries. LSA and LAA enable mobile operatorsto access the part of the spectrum exibly and opportunistically.

LSA and LAA incorporate the spectrum sharing mechanisms where bothapproaches have to be able to co-exist with the incumbent users or the legacyapplications. The introduction of LTE on the unlicensed spectrum raises theethical issue where LTE-LAA will aect the performance of the WiFi users.It is of great importance that LTE-LAA implements co-existence mechanismsthat enable fair access to the unlicensed spectrum, between devices of thesame or dierent technologies. With adequate mechanisms, LTE-LAA BaseStations (BSs) are acting like the good neighbors to WiFi Access Points(APs) [8].

1.5 Methodology

This thesis adopts Quantitative and experimental research method, withdeductive as the chosen research approach [9]. Experimental research methodinspects causes and outcomes. This approach investigates variables andbuilds connections between them [9]. One of the task within the projectis to build a system-level simulator. Data will be collected from simulationsand analyzed to reach the conclusions.

1.6 Delimitations

This thesis investigates the implementation of exible spectrum alloca-tion in the wireless cellular network. The system model designed implementsthe necessary mechanism to ensure co-existence with the legacy systems op-erating on the unlicensed band (WiFi). The performance of such systems isnot within the scope of this thesis. This thesis focuses on outdoor environ-ment, where outdoor subscribers are connected to outdoor small cells (BaseStations). Specically, this thesis examines the scenario with two mobileoperators co-existing within an area.


1.7 Outline

Chapter 2 provides the theoretical background and related works. Chap-ter 3 explains the methodology of the project. Chapter 4 presents the resultsand analysis. Chapter 5 provides the conclusions of the thesis.

Chapter 2

Background and Related Work

This chapter provides the background on Wireless Cellular Network, itscurrent and the envisioned future architecture, mobile trac consumptiontrend, and the emerging complementary spectrum authorization schemes.

2.1 Wireless Cellular Networks

Long Term Evolution (LTE) is 4G wireless standards, successor to 2Gand 3G standards. The 3rd Generation Partnership Project (3GPP) is aStandard Developing Organization (SDO) with the primary task of devel-oping future mobile communication technologies. 3GPP released the rstversion of LTE (Release 8) on 2008. Some of its highlights are new radio in-terface based on Orthogonal Frequency Division Multiple Access (OFDMA)and all-IP network. The 3GPP standards up to 3GPP Release 13 along withtheir main features are shown at gure 2.1.

Figure 2.1 LTE 3GPP Releases, adapted from [10]

Spectrum is one key ingredient for increasing the capacity of the wirelessmobile network. The frequency and the bandwidth of the spectrum beingmade available by the National Regulatory Authority (NRA) at a time aresubject to the availability and the policy of each country. The common

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Chapter 2. Background and Related Work 7

practice of spectrum auctions being used as the method to assign spectrumplays a part in the fragmentation of spectrum, especially for wireless mobilenetworks. This practice leads to spectrum fragmentation, where one operatorhas the license of spectrum with dierent sizes in dierent bands. As anexample, Table 2.1 represents spectrum holding of a wireless operator inSweden.

Table 2.1 Spectrum holding of an MNO in Sweden [11]

Spectrum Bandwidth (MHz)

800 MHz 2Ö10900 MHz 2Ö6 + 2Ö51800 MHz 2Ö5 + 2Ö20 + 2Ö102100 MHz 2Ö19.8 + 1Ö52600 MHz 2Ö40

LTE Release 10 introduces Carrier Aggregation (CA) along with LTE-Advanced. CA enables operators to fully utilize chunks of spectrum thatthey have. LTE-Advanced enables carrier aggregation up to ve componentcarrier, where one component carrier can have 1.4, 3, 5, 10, 15 or 20 MHz ofbandwidth, which allows aggregated maximum bandwidth of 100 MHz [22].Carrier Aggregation can be implemented on contiguous or non-contiguouscomponent carriers. For example given in Table 2.1, the MNO could improvethe performance of the 900 MHz system by implementing CA with otherparts of the spectrum that it holds. This way, CA enables better spectrumresource utilization and improving the data rate.

LTE employs Orthogonal Frequency Division Multiple Access (OFDMA)for downlink transmission [12]. OFDMA is multiple access scheme that uti-lizes Orthogonal Frequency Division Multiplexing (OFDM) modulation tech-nique. OFDM divides the bandwidth into multiple orthogonal sub-carriers.Information-carrying sub-carriers will be sent simultaneously from the trans-mitter. Subsets of sub-carriers can be allocated to multiple users.

Previous works study the eect of Carrier Aggregation (CA) to the per-formance of cellular networks as well as on the cost and revenue sides [13].Previous works investigate the aspects of deploying 4G LTE together withdierent spectrum authorization schemes, where an MNO can deploy LTEon the licensed band and also on license-exempt bands.

2.2 Methods to Increase Wireless Network Capac-

ity

The increasing mobile data trac has lead to many works related to in-vestigating methods to increase the capacity of wireless cellular networks,


with cost-ecient ways. The growing data trac leads to the goal of provid-ing 1000 more capacity for the future wireless mobile networks compared tothe existing wireless cellular networks [5]. In the Introduction, three meth-ods are specied as the means to increase the capacity of wireless mobilenetworks. This section will provide the current trends of each method, andhow they aect the system model that is used in the thesis.

Spectrum Ecient Technology

Spectrum eciency can be described as how big the data rate (bits/second)can be successfully sent per unit spectrum (Hz). Increasing spectrum e-ciency means that more data can be sent for the same amount of spectrum.Examples that fall into this category are related to the implementing multi-antenna on the transmitter and receivers [10]. One advantage that can beachieved from the multi-antenna technique is the spatial diversity, wherethe same information is transmitted through multiple antennas. Spatial di-versity will provide better protection towards the radio channel and leadsto better SINR. Multi-antenna technique called beamforming enables theshaping of the transmitted and received beam for the signal. Beamform-ing will increase received signal gain for the intended user while reducinginterference to the other users. The third advantage of the availability ofmulti-antenna on the transmitters and the receivers is what is called by spa-tial multiplexing. Under certain condition, spatial multiplexing can increasethe channel capacity by a factor of min(NT ,NR), where NT and NR are theavailable numbers of antennas on the receivers and the transmitters. Themulti-antenna technique previously described are often covered as MultipleInput Multiple Output (MIMO) concepts.

Network Densication and Heterogeneous Networks

Network densication is a method to increase network capacity by addingmore base stations within an area. Network densication has been consid-ered as one key solution to achieve the target of 1000 more capacity for thefuture wireless networks [5]. Adding more base stations will have two-foldeects towards the network capacity. First, spatial densication will increasethe capacity per area by reducing the number of users being served by onebase station, allowing the users to receive better performance. Second, spa-tial densication will bring the users closer to the base stations, and thatusually mean better signal strength and better user throughput. Based ontheir coverage and transmitted power, base stations can be divided into dif-ferent types. A Macrocell is a category of high-powered base stations thatable to cover a wide area. It is usually installed on a mast on greeneldsites or at the rooftop of a building. For areas with high trac density (of-ce parks, downtown area), adding more macrocell as mean to add capacity


might face some obstacles. The rst obstacle is related to site acquisition,where it might be dicult to nd a space to lease that is appropriate formacrocell deployment [14]. The second is cost related, due to the high-cost deployment of a macrocell, such as the expenses related to civil works.Heterogeneous Network is a concept of deploying low-powered base stationswithin the coverage of macrocells. The low-powered base stations, sometimesalso called as small cells are installed whether to provide more capacity or toprovide better coverage. Small cells can be further divided, usually based ontheir deployment method and range of coverage. Femtocell or Home eNodeB(HeNB) refers to a small cell that is deployed to provide indoor coverage andcapacity. It requires IP connectivity to provide the connectivity to the oper-ator's network. It has low complexity installation eort, making it possiblefor deployment by users. Picocell and microcell resemble small cells withhigher capability and capacity than femtocells, and they are deployed by theoperators. Picocell and microcell can be implemented to cover public areaslocated outdoor and indoor where microcell has bigger transmission powerthan picocell.Several factors motivate and promote small cell deployment. It has beenmentioned earlier that each 3GPP standard release brings new features andenhancements, some have improved the performance and simplied the de-ployment of small cells. Features such as Inter-cell Interference Coordination(ICIC) and enhanced-ICIC (eICIC) can alleviate the interference experiencedby cell-edge users of the small cells, due to the co-channel operation of themacro-cells and the small cells. The other factor that drives the deploy-ment of small cells is its low deployment cost compared to the deploymentof macrocells [15].

Access to More Spectrum

The third method to increase the capacity is by utilizing more radio spec-trum. The spectrum range between 300 MHz and 3 GHz is considered as the`sweet spot' for wireless networks, due to the propagation characteristics andthe wavelength that enables the antenna to be conveniently designed to t inuser equipment [16] [17]. The rst two mentioned methods heavily dependon the strategy of each MNO, but the last method is more complicated toimplement, mainly because of lack of available spectrum in these sweet spotrange. Obtaining additional bandwidth at similar frequencies might involvemoving the incumbent to new operating frequency (refarming), somethingthat is usually considered as costly and time-consuming eort. There areextensive discussions and studies in making better use of spectrum resourcesthat bring new paradigm in spectrum allocation and authorization schemes.The method and its relation to the theme of the thesis will be explained inthe following section.


2.3 Flexible Spectrum Authorization Scheme

The purpose of provisioning the increasing mobile data trac has led tonew schemes related to spectrum policies and implementations of new tech-nical advances implementation on the mobile networks. This section willpresent earlier works on the subject of the exible use of non-telecommunicationactors spectrum and the technology that can pave the way of utilizing themon the current and future radio access network. Each country has a reg-ulatory authority that decides how the spectrum divided and allocated todierent applications and services. The regulator also determines autho-rization schemes, who can utilize particular part of the spectrum withina dened area. Conventionally, spectrum allocation and authorization areimplemented in a static manner, and there is a presumption that certainareas of the spectrum are experiencing underutilization wherein dierentparts of the spectrum (wireless cellular network) are experiencing congestion[18]. There are two main methods in spectrum allocation and authorization,namely individual authorization and general authorization [19]. In individ-ual authorization, the rights to utilize a part of a spectrum is assigned toone or more actors. In general authorization scheme, the right to use partof the spectrum is granted without any fee, with the requirements that eachtechnology has to co-exist and accessing the channel conforming with thesets of rules. Some examples of the general authorization scheme are the op-eration on the TV White Space and the ISM Band, including the operationof wireless networks on the 5 GHz unlicensed spectrum (LAA).

Figure 2.2 Spectrum authorization schemes for wireless networks, adaptedfrom [20] and [21]

Individual authorization can be divided further into authorized primaryuse and authorized secondary use. In authorized primary use, the licensee


Table 2.2 LSA incumbent actors and uses and bandwidth availability inseveral European countries [23]

Country Main incumbent appli-cations

Spectrum availability for wirelessnetworks

Finland Wireless cameras,PMSE

90% time availability for 85 MHz;15 MHz available within denedarea

France Aeronautical telemetry,PMSE

80 MHz for 80% of population

Ireland Aeronautical telemetry,PMSE

100 MHz for all population

Italy Aeronautical telemetry,PMSE

85 MHz available nationwide,15 MHz available within denedarea

Sweden Aeronautical telemetrywith small utilization

100MHz in all populated area,not applicable in unoccupied area

UK Defence applications 40 MHz exclusively available,with additional 20 MHz consid-ered for future sharing

will be able to use the part of the spectrum exclusively with protection fromharmful interference from other users. Authorized secondary use schemeenables part of the spectrum that has been licensed to a certain licensee,to be used by other actor or entity, with certain technical requirementsto protect the incumbent licensee. This scheme is also known as LicensedShared Access / Authorized Shared Access (LSA/ASA). LSA is comple-mentary spectrum scheme, where QoS guarantee can be achieved as if inusing licensed spectrum. There is a considerable interest within the wirelesscommunication community in using 2.3 GHz band for wireless broadbandnetworks. Incumbent users of 2.3 GHz band within European Conference ofPostal and Telecommunications Administrations (CEPT) countries consist ofProgramme-making and special events (PMSE) applications, telemetry andother governmental use [22]. Authors in [22] conduct feasibility study LSAconcept by measuring interference level between LTE and wireless camera inthe 2.3 GHz band.

Authors in [24] studied and conducted experimental trials in several lo-cations in London, where availability and capacity that achievable in TVWhite Space were investigated. The trials were initiated by UK Telecommu-nications Regulator, Ofcom. To increase the spectrum allocated for wirelessand mobile services, Ofcom devised plans and strategy for future wirelessspectrum. The envisioned strategy was to make almost 80% of new spec-trum below 6 GHz as shared spectrum and will be accessed either via LSA or


opportunistic spectrum access scheme [25]. Capacity, Machine-to-machinecommunications (M2M), coverage and wireless backhaul were identied asprimary drivers of UK future wireless spectrum allocation strategy.

2.4 Related Work

This section explains previous works that are relevant to the topic of thethesis. They are found during the literature review phase of the thesis.

Authors in [26] propose blank sub-frames as co-existence mechanism be-tween LTE-LAA and WiFi system. The studied scenario is for indoor deploy-ment of WiFi, LTE-LAA base stations, and their corresponding users. Theproposed mechanisms is a modied version of Almost Blank Sub-Frames(ABS) that was initially designed to overcome interference for co-channelHetNets. The blank sub-frames allow WiFi Access Points to access thechannel. The main result shows there is a tradeo between the number ofblank sub-frames, with the performance of LTE-LAA. The main issue of thiswork is that it does not introduce LBT that is required in most regions asthe co-existence mechanism in the unlicensed spectrum.

Authors in [8] investigate dierent co-existence mechanisms between LTE-LAA and WiFi systems namely Duty Cycling and Carrier-Sensing AdaptiveTransmission (CSAT). The scenario studied is for Heterogeneous Networkswhere picocells co-exist with WiFi access points. CSAT works by deninga Time-Division Multiplexing (TDM) cycle, where LTE-LAA base stationseither taking on or o state. LTE-LAA base stations occupy the channelduring on state, while during o state, other technologies can occupy thechannel. The authors also show that, by applying co-existence mechanism,LTE-LAA is a good neighbor for WiFi system.

Authors in [27] investigate the performance of two operators deployingtheir networks under LAA using stochastic geometry. The authors showthat by adopting adaptive carrier selection, there is an improvement com-pared to random channel selection. Authors in [28] investigate the gain ofimplementing frequency reuse and LBT for a single channel. They show thatLBT can increase the performance of LAA deployment, especially in highload situation.

Authors in [29] investigate dierent scenarios for the co-existence betweenLTE-LAA and WiFi system, indoor and outdoor scenarios are investigated.The authors adopt LBT as the co-existence scenario and examine multiple-channel utilization and selection methods. They also study the scenariowhere seven operators deploy outdoor LTE-LAA small cells within an area.But, for the outdoor multi-operator scenario, the authors do not investigatethe scenario where deterministic channel selection is used. It is also deservingto mention that the authors use real base-stations instead of random small


cells generated by Point Process. The performance reported is for one userassociated with each LTE-LAA small cells.

The authors in [30] investigate the performance of 'WiFi-like' systemoperating on TV White Space (TVWS). TVWS refers to the unused part ofthe spectrum initially allocated for TV broadcast. The locations of the accesspoints are generated according to Point Process. The paper and [29] providethe model for SINR and throughput for LTE-LAA that will be adopted inthis thesis.

We identify the existing research gaps that motivate the topic of the the-sis. Most of the previous works focus on nding the optimum co-existencemechanism for LTE-LAA and the WiFi system. The following is the rstidentied research gap:No previous work investigates the feasibility of deploying LTE-LAA networksto meet the current and future mobile data demandThe rst contribution of the thesis is the feasibility study of deploying out-door LTE-LAA small cells network, and the network dimensioning accordingto the feasible load concept.

The techno-economic discussion related to the opportunity to increase ca-pacity by exploiting additional spectrum through LSA is done in [31]. Theauthors compare alternatives between implementing LSA and MIMO to ndthe cost-ecient method the MNOs can adopt.Authors in [32] investigate techno-economic aspects of the impact of dier-ent spectrum pricing and spectrum holding for two distinctive circumstances.The authors consider India where the spectrum pricing is high and represent-ing high user density, while Sweden is chosen representing condition wherespectrum price relatively low and low user density.The following is the second research gap that motivates the theme of thethesis.There is lack of work investigating the techno-economic discussion in oper-ating LTE networks on two dierent licensing regimes, namely LTE-LAA asunlicensed regime and LSA as licensed regime

The operation of LAA that being discussed and standardized is basedon the complementary and opportunistic mechanism with the licensed spec-trum. But, in this thesis, the performance that is investigated is for theunlicensed band as if operating in a 'stand-alone.' The motivation is to com-pare the performance of an unlicensed operation, with the licensed (LSA)mode of operation. This thesis will specically study the market conditionin Europe. This thesis will adopt the LSA bandwidth availability and theregulatory requirements regarding the operation of mobile networks in theunlicensed 5 GHz band for European countries.

Chapter 3

Methodology

This chapter will explain methodology adopted for the thesis. The rstpart of the degree project is to make a quantitative analysis of multi-operatorsmall cells network deployment when selecting LAA and LSA spectrum ac-cess method. The primary task for the rst part is to build system-levelsimulator using MATLAB. Section 3.1 explains about stochastic geometryand how the thesis utilizes it for generating Base Stations and User Equip-ment locations. Section 3.2 describes the channel propagation model that isadopted in this thesis. Section 3.3 provides the information regarding feasi-ble load concept and target throughput. Section 3.4 explains the method todo network dimensioning. Section 3.5 and 3.6 describe the channel selectionmethod and the interference modeling in the unlicensed spectrum respec-tively. Finally, section 3.7 provides the cost model that will be used in thetechno-economic discussion. In this thesis, the performance of LTE operat-ing in unlicensed spectrum (LAA) is investigated under harsh environment,where there are two MNOs with each of them deploying outdoor small cellsto serve their outdoor subscribers. The chosen scenario also represent thecase where mobile operators are deploying their networks to cover outdoorsubscribers in a dense urban area.

3.1 Base Stations and Users Distribution

Stochastic Geometry is a mathematical tool that investigates the prop-erties of random placements of objects within an area. It has been usedexhaustively in the eld of wireless networks to provide analysis of some keynetwork components such as capacity regarding user throughput, or connec-tivity regarding Signal to Noise and Interference Ratio (SINR). Althoughthis thesis does not provide mathematical analysis, this thesis will adopt keyaspects of stochastic geometry which is the point process or random pointpattern. Point process has been used to model placements of Base Stations(BSs) and mobile users within wireless networks. It provides more realistic

14

Chapter 3. Methodology 15

(a) Hexagonal Grid (b) Point Process

Figure 3.1 Base Stations deployment realization under hexagonal grid andpoint process

modeling of Base Stations where Base Station's placements practically areoften not ideal like the concept of grid-based hexagonal or square lattice. Inheterogeneous networks, dierent base stations (macrocells, microcells, pic-ocells, femtocells, and small cells) dier from each other regarding coverage,inter-site distance, antenna height, etc. In this project, the outdoor smallcells are modeled by Poisson Point Process (PPP), which is the most com-monly used Point Process to model base station placement, with minimuminter-distance from each other. Whereas, the mobile users are independentlygenerated and according to homogeneous PPP.

Red circles on gure 3.1 represent the location of the BSs under dierentrealizations.

3.2 Channel Model

METIS channel model provides various Propagation Scenarios (PSs) fordierent Test Cases (TCs) that are considered to be the feasible scenariosfor the implementation of 5G networks. Extensive measurements developedthe channel model in various environments [33].

This thesis implements METIS channel model for Urban Micro Outdoor-to-Outdoor Propagation Scenario for Line of Sight (LoS) scenario. The chan-nel model itself is a modication of Urban Micro (UMi) path loss model ofIMT-Advanced [34]. For the Non-Line of Sight (NLoS) scenario, the UMipath loss model based on a hexagonal grid is utilized. The probability ofLoS or NLoS condition will be based on UMi LoS Probability. Both channelmodels support frequency range between 0 and 6 GHz, making both eligibleto be utilized in the thesis.

Breakpoint distance is the distance where the path loss shift from one


regime to the other. It is calculated by the following equation:

d′BP = 0.87 exp(−

log10(fc

1GHz)

0.65

) 4h′BSh′UE

λ(3.1)

Where fc is the carrier frequency and λ is the corresponding wavelength,h′BS

and h′UE

are the eective height of the Base Station and the User Equip-ment. In the system model, the actual height of the BS hBS is 10 m, whereasthe actual height of the UE hUE is 1.5 m. The eective height of the BS andthe UE can be calculated as follows:

h′BS = hBS − henv, h′UE = hUE − henv (3.2)

where henv is the environment height in urban case scenario, which will beassumed to be 1.0 m.

For the LoS scenario, the propagation model can be divided based onthe distance between UE and the BS d compared to the breakpoint distanced′BP

:

PLLOS(d)|dB = 10n1 log10( d

1m

)+28.0+20 log10

( fc1GHz

)+PL1|dB (3.3)

when 10 m < d < d′BP

, and

PLLOS(d)|dB = 10n2 log10( d

d′BP

)+ PLLOS(d′BP)|dB (3.4)

when d′BP

< d ≤ 500 m, with n1 = 2.2 and n2 = 4.0.PL1|dB is path loss oset added to the channel model to improve the

consistency of the channel model with the measurement. Path loss osetPL1|dB is calculated as follow:

PL1|dB = −1.38 log10( fc1GHz

)+ 3.34 (3.5)

This thesis adopts the formula in [34] for Urban Microcell (UMi) scenarioto generate LoS probabilities PLOS between transmitter and receiver.

PLOS = min(18/d, 1).(1− exp(−d/36)) + exp(−d/36) (3.6)

Equation 3.6 shows that if the distance between transmitter and the receiverless or equal than 18 m, there will be LoS condition. The greater the distanced, the smaller the probability of LoS will be, which will result in greater pathloss. For NLoS condition, we will adopt the path loss model for hexagonalcell layout provided in [34].

PL = 36.7 log10(d) + 22.7 + 26 log10(fc) (3.7)


3.3 Feasible Load Concept and Target Throughput

The system model implemented on this thesis is assuming an operatordeploying small cells within an area to provide capacity and coverage for itssubscriber. A load of cell i ρi can be considered as the resource utilizationof cell i which is associated with the power needed to activate the ResourceBlock (RB). A load of a particular cell will be inuenced by the numberof user and the service requested by users. For example, video streamingservices will result in higher cell load compared to web browsing, assumingthe same number of active users within a cell. The load vector of the networkρ = (ρ1, ρ2, . . . , ρN ) constitutes the load of all cells within the network,where N is the number of cells in the network. Link gain gij is the path lossbetween user i and cell j. For user i that is associated or served by cell k, thecorresponding signal to noise plus noise ratio (SINR) for user i is calculatedas follows.

γi(ρ) =gikPk∑

j 6=k ρj gij Pj + σ2(3.8)

where ρj is the cell load of the interfering cell j. Pk is the power transmissionof the serving cell k and Pj is the transmission power of the interfering cell j.The additive noise power is represented by σ2. Cell load ρj also representsthe probability of transmission. In this sense, the co-channel interferenceexperienced by one link is not aected by all co-channel transmissions but issubject to statistical interference. To calculate the downlink user through-put, this thesis assumes modied Shannon formula for Single Input SingleOutput (SISO) system as follow [35]

bi(γi(ρ)) = W η log2(1 + β γi(ρ)) (3.9)

where W is the system bandwidth, η is the system bandwidth eciency andβ is the SINR eciency. This thesis assumes SISO system with the systembandwidth eciency η and the SINR eciency β to be 0.57 and 0.8.

Dierent services and applications will require dierent number of re-sources. User utilization is the number of resource needed to be allocated,for the requested data rate. The following equation shows the utilization ofuser i connected to cell j, when requesting data rate of Ω [36] [37]

nji =Ω

bi(γi(ρ))(3.10)

Cell load ρj is the aggregation of users utilization associated with cell j

ρj =∑i∈φj

nji (3.11)

where i ∈ φj is the set of users, associated with cell j. Authors in [37] intro-duces the Feasible Load Concept and Feasible Load Problem. The solution


for the Feasible Load Problem is by nding the load vector that balancesutilization of resources in all cells with the corresponding resource demands.The Feasible Load Concept also states that cell load can take value less orequal to ρmax, the maximum load allowed for each cell.

The primary goal of the technical part of the thesis is to get the minimumnumber of required base stations, which can meet the required throughputtarget Ω.

3.4 Network Dimensioning

Network planning is a challenging task faced by the operators in de-ciding where to deploy base stations and how big the capacity needed foreach base stations to achieve the Key Performance Indicator (KPI) targets.Establishing the right location to deploy base stations ensures the optimalcoverage for subscribers. The proper network dimensioning, deciding theoptimal resources (transceivers, bandwidth) for each base station, providesthe cost-eective method of providing capacity. This process establishes theminimum number of base stations and the minimum resources needed forelements in the network. Proper network dimensioning ensures Mobile Net-work Operators adopt the most economical method in fullling the targetedKPI. In this thesis, the KPI of the network is the 5th percentile of downlinkuser throughput. The objective is to nd the number of BS required whenduring busy hours at the maximum fth percentile of users do not achieve thetarget throughput. For each scenario, the number of BS densities needed fordierent user throughput targets is investigated. The downlink user through-put target is deduced from the mobile data consumption trend and forecast,which will be explained in the following section.

3.4.1 Mobile Data Consumption

Previous works have illustrated the method of dimensioning mobile net-works with regards to the mobile data consumption. In this thesis, mobiledata forecast and reports on mobile data usage and pattern are used to derivethe required user throughput.

This thesis assumes the mobile data consumption in Sweden. The SwedishPost and Telecom Authority (PTS) on May 2016 published the SwedishTelecommunication Market report [38]. The report contains data such asmobile market subscriptions, market share, and mobile data usage for theyear of 2015. Private and corporate segments are distinguished in the reportfor mobile data usage. Table 3.1 shows the mobile data usage for dierentsegments.

In the report for mobile data usage sections, PTS divides the mobiledata usage into three categories, namely mobile broadband as a standaloneservice, mobile broadband as additional service and subscription for call


Table 3.1 Mobile data usage in Swedish market [38]

SubscriptionType (GB)

Data usage permonth for PrivateUser

Data usage permonth for Corpo-rate User

Average data us-age per month

Mobile broad-band as a stan-dalone service

11.8 5.9 9.9

Mobile broad-band as anadditional service

3 1.3 2.5

Subscription forcall and dataservices

0.5 0.5 0.5

and data services. The rst type is the subscriber who uses wireless dongleor router to access mobile data network. The dongle is then attached tolaptops or desktops to enable connecting to the Internet. This categoryis specic for data trac, with no support for voice service. The secondcategory is for type of subscription where at least 1 GB of mobile data creditis purchased. The mobile data credit can be purchased as an add-on orcomes with the subscription. This category is the most common subscriptionfor smartphones. The last category is mobile subscription with or withoutdata or voice services. The rst category is not suitable to be consideredconsidering the scope of the thesis. Hence, the monthly mobile data usagefor the year 2015 is assumed to be 3 GB, which corresponds to the mobiledata usage for individual segments.

Section 1.1 briey explains the trac forecast made by Ericsson andCisco. This thesis will assume the optimistic scenario regarding mobile datausage. It is assumed there will be an annual growth rate of 53% that cor-responds with the forecast made by Cisco [3]. Based on these assumptions,for the year 2016, it is assumed that the mobile data usage in Sweden willbe approximately 4.6 GB per month per subscriber.

Energy Aware Radio and Network Technologies (Earth) Project is a con-sortium of academia and industry that studies energy ecient wireless sys-tem [39]. Earth Project divides deployment areas by population densities ofpopulation per square km, as shown on Table 3.2.

In this thesis, we will assume the scenario of High Dense Urban with5000 population per square km, with 100% mobile penetration and eachsubscriber utilizes one User Equipment (UE). In network planning and di-mensioning, it is important to study where the trac is originating. Thecommon denominator made by telecommunication vendors and consultantsis that 80% of data trac are generated from indoor subscribers [41][42][43].


Table 3.2 Average population densities for dierent deployment types [40]

Deployment Type Average Population Density(citizen/km2)

Dense Urban 3000Urban 1000Suburban 500Rural 100Sparsely Populated & Wilderness 25

Previous works have assumed the same value in the eort of investigatingnetwork dimensioning for dierent scenarios [44][45].

This thesis considers that 20% of the subscriber are located outdoor. Therequired user data throughput for each year (Ω in Equation 3.10), will beestimated based on the mobile trac forecast. Moreover, two values of useractivity will be used. Each of them represents low and high activity factor.For the low activity factor, each user will utilize its subscription during 6hours each day. Whereas in high activity scenario, each user will utilize itsdata subscription during 3 hours each day.

3.5 Distributed Channel Selection in LAA

There are 11 non-overlapping channels, each with 20 MHz bandwidth,available for outdoor implementation in LAA in Europe by utilizing thespectrum within the range of 5470 - 5725 MHz [46, 47]. Previous works haveinvestigated the performance of LAA system with dierent channel selectionmethods when assuming dierent numbers of available channels. Impact onimplementing dierent channel selections (random or deterministic) methodwill be investigated in this thesis. The motivation of such investigation isto explore the opportunity to increase further the performance of the LAAsystem, and the additional cost that might be raised due to the increas-ing hardware or software complexity. The algorithm for the deterministicchannel selection adopts the algorithm proposed in [48].

The algorithms proposed on [48] are considered for the thesis. The algo-rithms are as followsAlgorithm 1

1. Calculate temperature parameter: T = Klog(2+t)

2. For BS j, measure the local energy detected on all channels c:

Aj(c) = Nj + 2∑

k∈B;ck=c

Pk(j)


3. Calculate the probability, for all channels c

P(c) = exp−Aj(c)

T /∑c∈C

exp−Aj(c)

T

4. Generate a random value according to probability P and select a chan-nel according to the random value

Where Nj is the thermal noise detected on BS j on a certain channel, andPk(j) represents the level of the signal received by BS j from transmissionof BS k. B comprises of the sets of BSs. Also, t is the age variable, and Kis a constant chosen during initialization.

Algorithm 2Select channel c when c = arg minc∈C(Aj(c))

In words, Algorithm 2 chooses the channel with the minimum detectedenergy. The paper explained that Algorithm 2 could be considered as thespecic case of Algorithm 1 when the temperature is set to 0 during theinitialization step. The model for the thesis will use Algorithm 2 becausethe estimation provides good approximations with less computational com-plexity.The algorithm being discussed requires the capability of each base stationto measure and store the signal received from other base stations on a par-ticular channel. The paper states that the new feature could be realizedthrough a software update. It is important to mention that the algorithmbeing discussed is designed for WLAN devices, where they are intended tobe able to operate within the range of the unlicensed spectrum (2.4 GHzand 5 GHz). To transport these capabilities for the operation of LTE onthe unlicensed spectrum might require additional costs. These costs are re-lated to the necessary hardware or software upgrade to enable LTE-LAAbase stations operate conforming with the regulatory frameworks (LBT, Dy-namic Frequency Selection). The potential additional cost will be factoredinto the economic model, where we will compare the total deployment costof LTE-LAA and LSA.

3.6 Interference and Throughput Calculation in LAA

This thesis will adopt the required co-existence mechanisms for the op-eration of LTE in the unlicensed 5 GHz band (LAA), the Listen Before Talk(LBT) mechanism and channel selection mechanism. This thesis will in-vestigate the co-existence of two MNOs deploying their outdoor small cellsutilizing LAA.


The mechanism of LBT emulates the mechanism of CSMA/CA of WiFisystems. The main idea is that each BS or WiFi Access Point (AP) haveto make clear channel assessment before transmitting data. The channel isconsidered idle if the BS / AP detect that it has lower energy than Energy De-tection (ED) threshold. For multiple BS deployments, a contention domainmay exist consisting sets of BSs where they can sense others transmissionwith the received power greater than Energy Detection threshold. In thisscenario, each BS has the chance to access the channel inversely proportionalto the number of BSs within the contention domain, adopting the approachdened in [29] and [30]. Assuming perfect channel sensing and LBT accessscheme, the downlink throughput experienced by user i associated with BSj can be considered as:

Ri,j = Mjbi(γi) (3.12)

Where Mj is the channel access time for BS j when accessing the chan-nel, and bi(γi) is the downlink user throughput as the function of SINRexperienced by user i.

3.7 Economic Model

Estimating the cost of equipment in making techno-economic analysisis a challenging task. One factor is because pricing is considered to be abusiness secret for vendors. The pricing is sensitive to several factors, forexample, the agreements between vendors and operators and it also varybetween countries. Finding a reference price for technology at its infancyis an arduous task, which is the case for LTE-LAA. There are previousworks that can be used to get the price estimation from existing equipmentthat has similar capabilities. In [49, 15] the author breaks down the costfor dierent wireless network deployments. The data are gathered frominterviews with vendors, operators, and independent research. The authorin [15] estimates the price for outdoor LTE small cells and LTE small cellswith built-in WiFi module. The author assumes the cost of an outdoor LTEbase station equipment with WiFi built-in module to be approximately 20%more expensive than the same equipment without WiFi module. In [50], theauthors assume that the cost for cognitive radio equipment to be twice theprice of legacy equipment. The authors mention the factors that drive thehigh cost, namely the sensing capability, wide bandwidth operation, databasecapability and small scale production.

Total Cost of Ownership for Radio Access Network

One aspect that will be investigated in this thesis is the economic dis-cussion that will point out, under the taken assumptions, which deployment


method (LAA or LSA) is more cost ecient. This subsection will give infor-mation of the expenditures involved to build and operate wireless networks,with the focus of radio access networks.The cost of radio access network can be broken into three components [49]:

Capital Expenditure (CAPEX): Capital Expenditure is one-timeinvestment cost made by the MNOs to deploy the network. It cov-ers the cost of base station equipment, backhaul connection, and alsoaccessories such as the antenna, battery and power supply.

Implementation Expenditure (IMPEX): Implementation Expen-diture covers the expenses to build the network. It covers the cost ofsite acquisition, the civil works, initial planning and network optimiza-tion.

Operational Expenditure (OPEX): Operational Expenditure in-volves all ongoing and recurring expenses that aroused during the life-time of the network. Some examples are site rental fee, electricity cost,operation, and maintenance fee of the base station, backhaul, and site.

The values for the IMPEX and OPEX of small cell networks in the thesiswill adopt the values for low-cost outdoor microcell in [49]. Whereas for theCAPEX for the equipment, we will utilize sensitivity analysis due to theuncertainty of the cost for LTE-LAA equipment. We will assume additionalcost φ for LTE-LAA equipment relative to the cost of low-cost microcellequipment in [49]. The value of φ will take the value between 0 and 1 (0≤ φ ≤1).

3.8 System Parameters

The parameters implemented in the thesis are shown on Table 3.3.In this thesis, each scenario is iterated 1000 times. During each iteration

for each scenario, the locations for the UEs and BSs are generated, and thepath loss, SINR, and UE downlink throughput are calculated based on themethods explain in previous sections. The data is gathered by sampling theusers located within the 100 m x 100 m area on the origin/center, to reducethe edge eects [51].

Figure 3.2 shows the overall workow of the thesis, where the main resultis the deployment cost regarding Total Cost of Ownership (TCO) and theNet Present Value (NPV) for each scenario.


Table 3.3 System Parameters

Parameter Value

Carrier Frequency LSA flsa 2.3 GHzCarrier Frequency LAA flaa 5.6 GHz [46, 47]Bandwidth LSA WLSA 30;40;50 MHz per operatorBandwidth LAA WLAA 20 MHz per channel; 11 non-overlapping channelsThermal Noise -174 dBmPropagation Model METIS modication ITU-R UMi pathloss [33]LOS & NLOS ITU-R UMi LOS & NLOS Probability [34]Energy Detection (ED) Threshold -62 dBm [46]System Area 1 km x 1 kmOutdoor User Density 500 subscribers/km2/OperatoreNB Tx power 30 dBmBS Antenna Type Omnidirectional [46]

Figure 3.2 System model and the workow of the thesis

Chapter 4

Results

This chapter presents the results obtained from applying the models ex-plained in the previous sections. The rst step is to nd Base Stations densi-ties λB needed to provide the capacity each year, for each scenario (LSA andLAA). The second step provides the techno-economic analysis of the datagathered from the rst step. The output is the recommendation of the mostcost-ecient method to be adopted by the MNO.

4.1 LAA

This section presents the required Base Stations (BSs) densities required,in the scenario of two MNOs deploying their network on unlicensed 5 GHZband. The required BS density is investigated when implementing randomchannel selection method and deterministic channel selection method as pre-viously explained on subsection 3.4. In both cases, both mobile operators(Operator A and Operator B), implement the symmetric strategy. Eitherboth of them perform random channel selection or deterministic channelselection.

4.1.1 Impact of Channel Selection Mechanism on LAA with

Low Activity Factor

Figure 4.1 shows the minimum BS densities needed to deploy by eachMNO to provide the trac demand each year.

We can see that by utilizing deterministic channel selection, the numberof base station required to meet the trac demand can be reduced. Figure 4.1also shows the gain of implementing deterministic channel selection is moresignicant in higher trac demand that requires more BS to be deployed.The less number of required BSs can be explained by examining the SINRand downlink throughput distribution on gure 4.2 and 4.3.

25

Chapter 4. Results 26

Figure 4.1 Network dimensioning for LAA system for low activity factor

Figure 4.2 shows the improvement of SINR when implementing determin-istic channel selection for the fth year scenario (25.2 GB monthly usage),for BS density λB = 19. Deterministic channel selection results in the 1.82dB increase of SINR from -1.8494 dB to -0.0233 dB for the cell-edge user.

Figure 4.2 SINR for dierent channel selection mechanisms, 25.2 GBmonthly usage, λB = 19, low activity factor

Figure 4.3 shows the Cumulative Distribution Function (CDF) of user


downlink throughput with the same scenario as mentioned earlier. For ran-dom channel selection, the cell-edge user that is represented by the 5thpercentile can not be served. Deterministic channel selection mechanismimproves the cell-edge user downlink throughput to 3.83 Mbps.

Figure 4.3 Downlink throughput for dierent channel selection mechanism,25.2 GB monthly usage, λB = 19, low activity factor

Figure 4.4 and gure 4.5 shows the Probability Density Function (PDF)of SINR for random channel selection and deterministic channel selectionrespectively.

The two following gures, gure 4.6 and gure 4.7 show the PDF ofdownlink user throughput for random channel selection and deterministicchannel selection.

The following two gures show the SINR and downlink throughput dis-tribution for the nal year within the scope of the thesis. The scenariorepresents the target throughput of the cell-edge user of 2.6 Mbps. Thescenario is when the projected user monthly usage of 210.9 GB and the BSdensity λB is 53. Figure 4.8 shows that by implementing deterministic chan-nel selection, the SINR of the cell-edge user can be improved from 3.3 dB to9.2 dB.

Figure 4.9 indicates the improvement in the downlink throughput for twochannel selection mechanisms. Deterministic channel selection achieves 3.58Mbps downlink throughput for the cell-edge user, whereas, for the randomchannel selection, the cell-edge user can not be served.


Figure 4.4 PDF of SINR for random channel selection, 25.2 GB monthlyusage, λB = 19, low activity factor

Figure 4.5 PDF of SINR for deterministic channel selection, 25.2 GBmonthly usage, λB = 19, low activity factor

4.1.2 Impact of Channel Selection Mechanism on LAA with

High Activity Factor

In this subsection, we will analyze the required base station density tobe deployed for the case of high user activity. Figure 4.10 shows that for thehigh trac activity, the number of base stations needed to meet the demandis greater for each case of monthly subscriber usage compared for the case


Figure 4.6 PDF of downlink throughput for random channel selection, 25.2GB monthly usage, λB = 19, low activity factor

Figure 4.7 PDF of downlink throughput for deterministic channel selection,25.2 GB monthly usage, λB = 19, low activity factor

of low activity factor. Figure 4.10 also shows the similar trend as in gure4.1 for the case of low trac activity. For there is a higher incentive in usingdeterministic channel selection as the subscriber load increases, due to thehigher reduction gain of the number base station.


Figure 4.8 SINR for dierent channel selection mechanisms, 210.9 GBmonthly usage, λB = 53

Figure 4.9 Downlink throughput for dierent channel selection mechanisms,210.9 GB monthly usage, λB = 53

4.2 LSA

This section presents the network dimensioning for the case of LSA. Inthe case of LSA, the available bandwidth is dierent in the various countries.In this thesis, we assume three types of spectrum availability in the 2.3 GHz


Figure 4.10 Network dimensioning for LAA system with high activity factor

band. Low, moderate and high availability represent where the channelavailable for LSA operation equal to 60 MHz, 80 Mhz, and 100 MHz. In thisthesis, we assume two MNOs are utilizing the LSA spectrum. Thus eachMNO will have access to 30 MHz, 40 MHz and 50 MHz of spectrum for eachscenario. Figure 4.11 and gure 4.12 show the required base station densityfor low and high activity factor. From gure 4.12 we can see that acquiringmore spectrum will result in the reduction of the number base stations needto be deployed. The gain is more evident in the case of high activity factorand high usage. Moderate (40 MHz) and high (50 MHz) spectrum holdingwill result in the reduction of number base station by 30% and 40% comparedto low spectrum holding (30 MHz).

4.3 Techno-Economic Discussion

This section will investigate the Total Cost of Ownership (TCO) for eachscenario. The ultimate result of this thesis is the recommendation under theassumptions being made, which deployment type is the most cost-ecient.The method to calculate TCO for the radio access networs will adopt themethod used in [49]

TCO = CAPEX + IMPEX +OPEX (4.1)

The CAPEX for deploying base station in the case of utilizing LSA canbe shown as

CCAPEXLSA= NBS (CBS + CBH) (4.2)


Figure 4.11 Network dimensioning for LSA system with low activity factor

Figure 4.12 Network dimensioning for LSA system with high activity factor

Where NBS is the number of base station deployed, CBS is the cost of a basestation equipment, and CBH is the cost for the backhaul connection. ForLAA, we will use sensitivity analysis due to the unknown cost of a small cellLTE-LAA as has been described on section 3.7. For LAA we can state theCAPEX as follows

CCAPEXLAA= NBS

(CBS (1 + φ) + CBH

)(4.3)

Both LSA and LAA will use the same backhauling type with the same


Table 4.1 Cost value and normalized cost

ParametersCost Value (Euro) Normalized Cost

LAA LSA LAA LSA

Base Station Equipment CBS 4000 (1+φ) 4000 (1+φ) 1

Microwave Backhaul CBH 3500 0.875

IMPEX 2500 0.625

OPEX 2000 0.5

cost value. Table 4.1 shows the breakdown of the cost adopted in the thesis.For the case of LSA deployment, we will also add the spectrum licensing

fee Cspectrum. The spectrum licensing fee will be divided and distributedduring the whole network operation. The value of the spectrum we willassume that the cost should be lower than the standard license paid for the2600 MHz band. The auction result in Sweden will be used as a baseline,where the spectrum license fee is Euro 0.3/MHz/pop [52]. We will varythe values for the LSA license fee, high spectrum cost, and low spectrumcost. For high spectrum cost, we will assume the cost for the LSA spectrumto be 80% of the baseline value, which is Euro 0.24/MHz/pop. For lowspectrum cost, we will assume the price of the LSA spectrum to be Euro0.09/MHz/pop. The total cost (normalized) to meet the demand for eachyear in the case of high activity factor is shown in gure 4.13. From gure

Figure 4.13 Incremental TCO of network deployments with dierent costassumptions

4.13 we can see the incremental cost for each deployment. In the case ofLSA with high bandwidth availability (50 MHz), will always provide the mostcost-ecient deployment choice, regardless the price of the spectrum. For the


rst nine years of observation, deployment of small cells LSA with 40 MHzbandwidth is the next cost-ecient strategy. However, it is interesting to seethat for the nal year of observation, LAA deployment with the assumptionof φ of 0 and 0.1 is more cost-ecient than LSA with 40 MHz bandwidth.As for the LSA with 30 MHz bandwidth, from the 8th year onwards providesthe least cost-ecient deployment option.

The TCO can also be expressed as Net Present Value (NPV) calculatedby the following formula [32]

TCO =T∑t=1

TCOt(1 + r)t

(4.4)

Where TCOt represents the total TCO for the year t, r is the discountrate that we will assume to be 10%, and T is the total period of networkoperation which is ten years. The nal result is represented by gure 4.14.

Figure 4.14 Normalized NPV for dierent deployment type and cost factor

Where the description of each type can be referred to table 4.2From gure 4.14 we can see that LSA case 2 and LSA case 1 are the

most cost-ecient deployment method. The third cost-ecient method isLSA case 4 followed by LAA with φ equal to 0. From these numbers, theMNO can choose the most cost-ecient method according to the spectrumavailability and price. We have mentioned the dierent incumbent users andavailable bandwidth of the LSA band in dierent countries. We also needto discuss the impact of the cost for LTE-LAA equipment. It is mentionedearlier, that additional cost factor φ is added for the projected cost of LTE-LAA base stations. As the cost of an LTE-LAA base station reduced, LTE-LAA will make an interesting solution for MNOs as the strategy to providecapacity for their subscribers. Moreover, the reduction in the equipment cost


Table 4.2 Description of deployment types in gure 4.14

Deployment type Description

LAA, phi = 0 LAA, with zero additional hardware costLAA, phi = 0.1 LAA, with 0.1 additional hardware costLAA, phi = 0.2 LAA, with 0.2 additional hardware costLSA case 1 LSA, 50 MHz bandwidth, high spectrum costLSA case 2 LSA, 50 MHz bandwidth, low spectrum costLSA case 3 LSA, 40 MHz bandwidth, high spectrum costLSA case 4 LSA, 40 MHz bandwidth, low spectrum costLSA case 5 LSA, 30 MHz bandwidth, high spectrum costLSA case 6 LSA, 30 MHz bandwidth, low spectrum cost

will be stimulated by regulatory frameworks that support the operation ofLTE on unlicensed 5 GHz spectrum.

Chapter 5

Conclusion and Future Work

5.1 Conclusion

In this thesis, the techno-economic analysis of LTE outdoor small-celldeployments under dierent spectrum authorization regimes is investigated.The spectrum authorization regimes being investigated are the unlicensedregime by utilizing the unlicensed spectrum on 5 GHz band (LAA) and thelicensed share regime under Licensed Shared Access (LSA) approach. Theresult shows that when operating in unlicensed 5 GHz band which has multi-ple numbers of channels available, the choice of the proper channel selectionmechanism is crucial. The adoption of deterministic channel selection showsa clear advantage over random channel selection method. The gain is evenmore evident in the high load case, during the nal year of the studied periodin the thesis. As for the licensed regime under LSA approach, we showed thedeployment requirements for dierent LSA bandwidth, representing dierentLSA spectrum availability.

The TCO for dierent deployment types is presented as the main resultof the thesis. The TCO discussion takes dierent cost assumptions and dif-ferent spectrum accessibility and spectrum fee. Finally, we can answer theresearch question that motivates the thesis

Under which condition LSA approach is more cost-ecient than LAAapproach for outdoor small cell deployment scenario?

LSA approach provides the most cost-ecient deployment option whenthe ratio between the available LSA bandwidth and the unlicensed spectrumis bigger than 45.4%. The ratio represents condition for high availability ofLSA spectrum.In other cases, LTE-LAA accounts for a promising and cost-ecient deploy-ment method, assuming the ratio between the available LSA bandwidth andthe unlicensed spectrum is lower than 45.4% and high spectrum fee. The ra-

36

Chapter 5. Conclusion and Future Work 37

tio represents condition with a moderate availability of LSA spectrum. Theprevious claim stands when there is small to negligible additional cost forLTE-LAA equipment.

5.2 Future Work

In this thesis, we assume that association of the user equipment is basedon the highest received signal strength. In the case of dense small cell net-works, coverage might not be the main issue. There is a signicant interestin maximizing the overall user experience, for example, to prevent a highnumber of users associated or camping on several cells, it is important tomake the users associated with cells that are underutilized. In the future,it would be interesting to see the performance of small-cell networks withoptimized user association.

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