Determining optimal fibre-optic network architecture using bandwidth forecast, competitive market, and infrastructure-efficient

models used to study last mile economics.

By

Muhammad Osamah Saeed

Supervised by Dr. Joseph C. Paradi

A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF APPLIED SCIENCE

GRADUATE DEPARTMENT OF CHEMICAL ENGINEERING AND APPLIED CHEMISTRY

UNIVERSITY OF TORONTO

© Copyright by Muhammad Osamah Saeed (JUNE 2011)

II

Abstract

Determining optimal fibre-optic network architecture using bandwidth forecast, competitive

market, and infrastructure-efficient models used to study last mile economics.

M.A.Sc (November 2011)

Muhammad Osamah Saeed

Department of Chemical Engineering, University of Toronto

The study focuses on building a financial model for a telecommunications carrier to guide it

towards profitable network investments. The model shows optimal access-network

topography by comparing two broadband delivery techniques over fibre technology. The study

is a scenario exploration of how a large telecommunication company deploying fibre will see its

investment pay off in a Canadian residential market where cable operators are using competing

technology serving the same bandwidth hungry consumers.

The comparison is made at the last mile by studying how household densities, bandwidth

demand, competition, geographic and deployment considerations affect the economics of fibre

technology investment. Case comparisons are made using custom models that extend market

forecasts to estimate future bandwidth demand. Market uptake is forecasted using sigmoid

curves in an environment where competing and older technologies exist. Sensitivity analyses

are performed on each fibre technology to assess venture profitability under different

scenarios.

III

Acknowledgements

The writing of this thesis took a considerable effort in understanding the background to the

industry and learning the different terminology used. Furthermore, to understand the exact

scope of this work was a niche finding exercise. For the patience, the encouragement, the

guidance, the foresight and the mentorship, my endless gratitude goes to Dr. Joseph C. Paradi.

He has given me the opportunity to work in one of the oldest industries that adapts to changing

environments constantly, making it an interesting one to study.

Further to that I would like to thank our corporate sponsors, who have given me insight into the

industry’s strategic direction and have helped me form the scope of this project suited to study

something that is of real value to network planners. The work in itself is rewarding as I myself

use the Internet avidly and will be able to see the tangible benefits of this study given the

improved access network infrastructure in the coming future.

I would also like to thank all my co-workers in our lab, the CMTE, who have been able to

provide the odd and occasional support that every graduate student needs when working on

such a lengthy project. Finally, I would like to thank my family for having to pull me through

financially and emotionally with strong encouragement to produce quality work.

IV

Table of Contents

Page

Abstract I Acknowledgements III

Reference Information Page

I List of Figures VI II List of Tables VII II Glossary of Terms VIII Abbreviation List VIII Terminology List IX Variable List X

Thesis Page

Executive Summary 1

1 Introduction 3

a Evolution of Telecommunications: A Chronological View 3

b Canadian Market Snapshot 5

c The Competitive Model 7

d Study Motivation 8

2 Literature Review and Scope 9

a Technology and Bandwidth Demand Estimation 9

b Infrastructure Modelling, Planning and Fibre Feasibility 11

c Scope of Study 12

3 Industry Analysis 15

a Consumer Bandwidth Requirements 15

b Canadian Access Technology Landscape 21

4 Methodology 24

a Overall Model Structure 24

b Market Sizing Module 26

c Build Module 30

d Deployment Module 32

e Equipment Inventory Module 37

f Financial Module 39

V

5 Results, Sensitivity and Discussion 43

a Scenario Testing of the Model 43

b Capital Expenditure per Subscriber 44

c Cashflow Diagrams 47

d Net Present Value 49

e Sensitivity Analysis of FTTH and FTTM by variable 51

f NPV Comparison of FTTH and FTTM 56

g Bivariate Factor Exploration of FTTH Feasibility 59

h Breakdown of Network Costs by Test Scenario 62

6 Conclusions 63 7 Future Work 65 8 References 66

Appendices Page

A Calculating Average User Bitrates Using Qualitative Data i

B Forecasting adoption using Fisher-Pry approximations ii

C Base Test Conditions and Sensitivity Results iii

D Bivariate Analysis of FTTH Feasibility v

E Modelling Tool Dashboard vi

F Model Listing xi

VI

I. List of Figures

1.1 Major service providers market share by subscribers per service offering

1.2 Canadian residential broadband market share of TelCo vs. CableCo

3.1 North American consumer internet traffic in petabytes per month

3.2 Canadian internet penetration – projected to 2025

3.3 Canadian internet usage distribution

3.4 Canadian internet usage

3.5 Canadian bandwidth usage projections

3.6 Forecasted growth in access technologies with 20% fibre-optic uptake

3.7 Access network infrastructure levels

3.8 Bandwidth attenuation over copper networks

4.1 Overall process model

4.2 Geometric model of a distribution area

4.3 Overall geometric model of MDU and SFU housing

5.1 CapEx reduction with increasing subscribers - %SFU variation

5.2 CapEx reduction with increasing subscribers - %Aerial variation

5.3 CapEx reduction with increasing subscribers – population density variation

5.4 Cash flow of FTTH for the three test scenarios

5.5 Cash flow of FTTM for the three test scenarios

5.6 Net Positive Value of FTTH for the three scenarios

5.7 Cash flow of FTTM for the three test scenarios

5.8 Sensitivity test on baseline parameters

5.9 Spider plot on FTTH and FTTM respectively at baseline parameters

5.10 Lower feasibility boundary as %SFU is varied

5.11 Lower feasibility boundary as %Aerial is varied

5.12 Lower feasibility boundary as Household Density is varied

5.13 Bivariate exploration of Household Density versus %SFU (completely buried)

5.14 Bivariate exploration of Household Density versus %Aerial (completely SFU)

5.15 Bivariate exploration of Household Density versus %Aerial (1000 LU/km2)

5.16 Network cost breakdown by scenario area

VII

II. List of Tables

4.1 Technology bandwidth capability

4.2 Decision matrix for trenching, micro-trenching, fibre and copper placement

5.1 Test location demographics and modelling results

5.2 Bivariate exploration of Household Density versus %SFU (completely buried)

5.3 Bivariate exploration of Household Density versus %Aerial (completely SFU)

5.4 Bivariate exploration of Household Density versus %Aerial (1,000 LU/km2)

VIII

III. Glossary of Terms

a. Abbreviation List

BW Bandwidth.

CCA Capital Cost Allowance.

CO Central Office.

CPE Customer Premises Equipment used to convert network signals into usable information.

DA Distribution Area.

DOCSIS Data Over Cable Service Interface Specification. The standard technology used to deliver high-speed Internet over co-axial cable and used by cable companies.

FTTX,H,M Fibre optic technology till the “X” (X=H: Home, X=N: Node, X=M: Micro-Node).

LAS Large Area Splices.

LU Living Unit.

MDU Multi-Dwelling Unit such as an apartment complex.

NPV Net Present Value.

PPV Pay-Per-View.

SCS Small Consumer Splices.

SFU Single Family Unit such as (fully/semi)-detached row housing.

IX

b. Terminology List

Bandwidth Connection speed available to the consumer, usually in multiple offerings (Very Low, Low, Medium, High, Very High). Subscribers choose the speed that suits them the best.

Batch Build Style of building where certain number of houses are passed in a certain timeframe to provide a certain proportion of the population with Internet accessibility.

CableCo Cable Company.

Capital Cost Allowance

A percentage of capital invested that can be used for depreciation purposes.

Central Office Central hub location which distributes all network architecture.

Conduit Housing for cables dug into the ground.

Continuous Build Style of building where the number of houses passed is dependent only on the incremental demand in a particular year.

Distribution Area Area served by one node, or distribution point in the network’s geography.

Drops Number of final infrastructural connections made to the consumer.

Frontage The perpendicular length in front of a home/building adjacent to laneway.

Large Area Splice Splice made on fibre between CSP and DA.

Living Unit Household unit that subscribes to the Internet.

Micro-Node A point in the network’s geography closer to the customer than a “Node”.

Micro-Trenching Excavation only to the point of existing conduit.

Node A point in the network’s geography where an aggregated signal is split to be distributed to customers.

Small Consumer Splice

Individual splice made for the customer, one per terminal.

TelCo Telecommunications Company.

Trenching Full-scale trenching that includes excavation and directional boring.

X

c. Variable List

%Buried,Aerial Percentage of the build that is desired either as buried or as aerial.

%CCAClass Percentage that applies to the CCA class.

%CorpTaxRate Percentage of income that corporations need to pay.

%SFU,MDU Percentage of Living Units segregated by SFU or MDU.

#t7342 Number of 7342 cards at any time.

#tCoupler Number of couplers at any time.

#tCPE Number of CPE required at any time.

#tCSP Number of CSPs at any time.

#tCO-OPI Cnx Number of CO-OPI Connections at any time.

#tDistArea Number of DAs to be served at any time.

#tDrops Number of drops required at any time.

#tERAM Number of ERAMs at any time.

#tGLB Number of GLBs at any time.

#tGPON Number of GPON cards at any time.

#tLarge Area Splices Number of LAS at any time.

#tOPI Number of OPIs at any time.

#tPedestal Number of Pedestals at any time.

#tRhino Number of Rhino Cabinets at any time.

#tSmall Area Splices Number of SCS at any time.

#tTerminal Number of Terminals at any time.

#tTether Number of Tethers at any time.

#tVSEM Number of VSEMs at any time.

Growth function parameter calculated using two points in time. For more information, refer to derivation of sigmoid function in the appendix.

Area The area containing the population to be served.

Actual Cashflow (After Taxes)

tFTTH,FTTM

Actual Cashflow after taxes at any time.

XI

Actual Cashflow (Before Taxes)

tFTTH,FTTM

Actual Cashflow before taxes at any time.

AllowancetFTTH,FTTM CCA Allowance value at any time.

AssetClasstFTTH,FTTM Value of total assets that can contribute towards CCA Allowance.

Bannual Annual build schedule computed for batch builds.

bt1, bt2 Major Infrastructure Build start and end dates.

Bt Number of houses built at any time.

Btcum Cumulative number of houses built.

Build Batch/Continuous Whether batch build (major infrastructure build) is desired or not.

BWt% of Total Effective market share on each bandwidth offering.

C%ofValueInsurance Insurance cost as a percentage of the network value.

C$/kWh Cost of electricity at any time.

CCustCare Cost of customer care per year.

CdiscNewSubs Discount offered to new subscribers.

CdiscTP

Discount offered to consumers for subscribing to all voice, video and data.

CEquip Cost of any piece of equipment.

Clabour Cost of labour per hour.

CLength Cost of fibre, copper and trenching per meter.

CtPower Cost of powering at any time.

Cap7342Cards Capacity of one 7342 card.

CapCoupler Capacity of one Coupler.

CapCSP Capacity of one CSP.

CapDistArea Number of LUs served by one DA.

CapDist.Splice Number of terminals to be served in one DA.

CapERAM(VSEM Slots) Capacity of one ERAM in terms of VSEMs it can support.

CapGPONCards Capacity of one GPON card.

CapMDU Average number of LUs per building.

XII

CapOPI(VSEMs) Capacity of one OPI in term of VSEMs.

CapTerm Capacity of one terminal in a FTTH build.

CapVSEM Capacity of one VSEM.

CapVSEM(ports) Carrying capacity of a VSEM.

CapitalInjtFTTH,FTTM Capital Injection required at any time.

ConsumptionFTTM Power required per subscriber.

Dtotal Total Internet demand over study outlook period.

Dtrem Remaining Internet demand at any time.

DtSFU,MDU Internet demand at any time, segregated by SFU or MDU.

Field Green/Brownfield Whether the build is a new, or an over-build.

FloorsMDU Average number of floors per building.

fPPV Average frequency of PPV events.

GtBW Growth of each bandwidth offering independent of any market factors.

GtTech

Growth of each technology offering independent of any market factors

(exception: xDSL which has been adjusted to account for shift to FTTx).

h Average size per household.

LDistArea Perpendicular length of one DA adjacent to laneway/highway.

LFrontage The perpendicular length infront of a home adjacent to laneway.

LFrontageMDU The perpendicular length infront of a building adjacent of laneway.

LtCO-DistArea Total length traversed at any time from the CO to the DA.

LtCO-Home Total length traversed at any time from the CO to the home.

LtCopper (DistArea-Home) Determination of Copper required in a FTTM environment.

LtDistArea-Home Total length traversed at any time from the DA to the home.

LtFibre (CO-Home) Length of Fibre required at any time.

LtFibre (CO-DistArea) Determination of Fibre required in a FTTM environment.

LtTrench The total trenching length required at any time.

LUFloor Average number of LUs per floor.

XIII

LUtBW Living Units on each bandwidth offering.

LUtSFU,MDU Living Units at any time, segregated by SFU or MDU.

LUtTech Living Units on each technology offering.

LUtTelCo|BW TelCo living units segregated by bandwidth offering.

MEquip%ofCost Maintenance cost as a percentage of the cost of the equipment.

MtCableCo Cable company market size.

MtTelCo Telecommunication company market size.

MTBREquip Maintenance time required between repairs.

MTTREquip Maintenance time required to repair.

NPVFTTH,FTTM Net Present Value.

p1, p2 Population at two different times.

Pt Population at any time.

PtInternet Population subscribing to the Internet at any time.

rinflation Rate of inflation.

rPower Rate of increase in the cost of electricity.

rt2 Percentage of market that should be Internet accessible at time t2.

RBW Price of each BW offering.

RPPV Average Price per PPV event.

Rvideo Average Price of standard video line.

Rvoice Average Price of standard voice line.

RealCashflow (After Taxes)

tFTTH,FTTM

Real Cashflow after taxes at any time.

RealCashFlow (Before Taxes)

tFTTH,FTTM

Real Cashflow before taxes at any time.

RevenuetFTTH,FTTM Revenue generated at any time.

t1, t2 Two timeframes provided by users in different modules to estimate module functions where two values of time are required.

XIV

T0 Growth function parameter calculated using two points in time. For more information, refer to derivation of sigmoid function in the appendix.

TaxestFTTH,FTTM Total value of taxes that need to be paid at any time.

Techt% of Total Effective market share on each technology offering.

ValuetFTTH,FTTM Cumulative value of the network at any time.

1

Executive Summary

Human communication has come a long way and is ever evolving. It not only changes the way

people interact with one another, but also the world around them. The Internet, a virtual mesh

that facilitates nearly every aspect of our daily lives, is maturing to the point where the existing

telecommunications structure can no longer support it. Besides our dependence on it for

paying our bills and reading the daily news, we look to it for sources of entertainment such as

instantly streamed videos, songs and other bandwidth heavy applications straight to our

personal computers and mobile devices. Consistent with other estimates, this thesis predicts

demand for bandwidth to exceed 100 Mbps by the year 2012, for high-end, power users, and

that demand for different bandwidth offerings is converging towards higher speed offerings.

For an Internet Service Provider such as a Telco still operating on last-mile copper, increase in

demand for bandwidth can translate into customer churn and lost revenues due to inadequate

Internet service as copper wiring has inherent capacity limitations to transport data. The case

for access network refurbishment is strong but careful evaluation needs to be carried out to

provide the right solution. Two versions of fibre-optic networks are considered, FTTH and

FTTM. The former is fibre that extends all the way to the customer’s home while the latter is

essentially an extension of fibre to an aggregation point where bandwidth is distributed along

existing copper wiring making it a much more feasible solution but with limited future

bandwidth capacity.

This thesis provides a methodology to assess customer demand with historical subscriber data

through sigmoid curves to estimate customer adoption of fibre-optic technologies. Given

demand in a particular area, the model outlines how to dimension the network infrastructure

based on equipment bandwidth capacities. Once both FTTH and FTTM networks have been

dimensioned, network costs can be obtained for both mutually exclusive projects to be

compared and evaluated.

2

In nearly all scenarios tested, FTTM is a more economical solution, but in certain cases it was

revealed that FTTH was also a viable option. Generally speaking, FTTH becomes viable in high

density areas such as those where multi-dwelling-unit (MDU) living is more than 70% of the

population, or when household density is greater than 2,500 LU/km2. Also, where choice exists,

a Telco should deploy at least 50% of its build as aerial but a combination of all three factors

would yield the best results. It is most sensitive to household density, aerial versus buried

deployment, and percentage of MDU living in decreasing order. However, when comparing the

two technologies together, FTTM is almost insensitive to the percentage of aerial versus buried

deployment, while it very strongly affects the FTTH solution's Net Present Value (NPV).

The thesis has also created a software tool that can model the annual investments to show a

comparison between the two technologies. It accepts user input based upon particular

geographies and demographics. The tool then constructs a geographic model based upon

equipment carrying capacities, bandwidth requirements, and estimated technology uptake.

The decision to deploy fibre-optic networks ensures a customer base for the future of the

company but the choice of deploying fibre all the way to the home can be a costly endeavour.

In all deployment scenarios, a network planner should exercise caution and try to model, using

the given software tool developed here, which is the prudent choice in that particular scenario.

This will ensure the best economic option deployed for the best location.

3

1. Introduction

Throughout time human beings have looked for better ways to communicate with one another

over large distances. From messenger couriers on horseback and carrier homing pigeons in the

earliest of days to our instantaneous optical networks of today, communication has facilitated

business, averted disasters, and brought people closer together. Today the primary means of

communication is gravitating towards Internet channels while making other media obsolete. In

fact most other media are now using the power of Internet based networks to operate and

disseminate their own information. Before going into the details of how the communication

networks are changing our lives and how we, in turn, are changing it, it is worth mentioning the

evolution of these networks and how we have come from messages written on scrolls of

parchment to the power of immediately downloading entire libraries of information.

a. Evolution of Telecommunications: A Chronological View

The natural place to start would be to look at the inception of the Morse Code by Samuel F. B.

Morse in 1836, which was essentially the first time information was digitised and transmitted

over wires. Albeit elementary in concept, it gave people over long distances the opportunity to

exchange short messages with one another. About forty years later, Alexander Graham Bell

filed a patent for what would become the world’s first universally adopted medium of

exchange, the telephone. The company, Bell Canada created in 1880, facilitated voice exchange

between people over vast distances through copper wiring. It all started from one phone in one

local Hamilton, Ontario store but its speed and convenience to get information across, made it

the first widely accepted mode of personal communication.

As popularity grew, Bell Canada laid submarine and TransAmerica cables to provide service

accessibility. In 1916, a signal travelling over 6,700 kilometres was used to connect the

Canadian east coast to the west and by the 1920s, people had access to individual lines in

Quebec. This era also saw the entry of new market entrants such as SaskTel and Quebec-

Telephone and it meant that Bell Canada was no longer the sole participant in the

telecommunications market.

4

In 1945, Bell Canada had installed its millionth telephone line and owned significant underlying

copper based network infrastructure at the local loop (at the population community level). A

couple of years later, it introduced the first mobile phone adding a new paradigm to

telecommunication media; the first concept to disruptive technology. By the 1950s, television

was becoming popular and this required significantly larger bandwidths for transmission,

requiring innovative solutions. Thus the Trans Canada Microwave Network was created which

enabled television, teletype messages, and telephone conversations to be carried over 6,400

kilometres. Incidentally during this time, one of the largest competitors, Rogers Cable TV,

entered the market with the most significant competitive technology: the cable network, which

operated on a completely different architecture but would essentially offer the same services.

A few years later, the telephone was used to transmit data over facsimile as the rotary phone

was replaced with Touch-Tone dialling; discretizing the information sent over the telephone

network. The next real stride in communication networks innovation came with the first

packet-switched network in 1972 setting the stage for the modern Internet’s communication

protocol, TCP/IP (Transmission Control Protocol/Internet Protocol). At this point

communication media were starting to converge on a single network infrastructure called the

Access Network, however no one anticipated its potential to support our present demands on

it.

As population densities and the distances between those populations grew, the inherent

limitations of copper to transmit data were becoming apparent. Fibre-optic technology was

gaining traction with field trials being conducted and by 1984 SaskTel laid what was then the

world’s longest fibre-optic network connecting up to a hundred communities. With fibre-optics

backbone networks, communication hardware was also improving and the consumers began to

be able to transfer more information faster over the network. By the 1990s Canada had the

world’s most comprehensive fibre-optic network and this was the tipping point that eventually

gave rise to consumer Internet being launched in 1995. While Bell Canada offered their Internet

through dial-up telephony which was severely speed limited, Rogers Cable used a then up-and-

coming technology called DOCSIS 1. 0 (Data Over Cable Service Interface Specification) to

5

deliver significantly faster speeds. This enabled them to have the first mover advantage, and

the Telcos have been playing catch-up ever since.

b. Canadian Market Snapshot

While many service provider companies started, there remained a few major players in the

market that appropriated the majority of the market share. The telecommunication providers

that have emerged and remained till today are mainly Bell Canada in eastern Canada and Telus

Communications on the west coast. As for the Cable providers, Cogeco and VideoTron and

Rogers Cable are major players. For simplicity, only two of the major players on the East Coast

(Rogers Cable and Bell Canada) are compared.

Over the years Rogers Cable and Bell Canada competed for market share in Ontario and

constantly competed to deliver comparable services to retain their market standing. Figure 1.1

illustrates the market landscape as of 2011 for both residential and business customers. Note,

accurate data on home phone usage could not be obtained.

Figure 1.1: Major service providers market share by subscribers per service offering [BCE11W3],

[ROGE10W3]

0 2 4 6 8 10

Television

Internet

Wireless

Subscribers (Millions)

Bell Canada

Rogers Cable

6

For the metropolitan city of Toronto in Canada, Figure 1.2 shows how the residential market

share is projected to grow for each the TelCo and the CableCo. Note that, in this figure, the

growth of fibre has been taken into consideration as part of the projection with modest growth

parameters. It assumes that after five years of initial deployment, 20% of the Internet

population will be using the new network. This is a conservative estimate considering that even

natural TelCo growth (stemming from existing xDSL services) will be operating on this new

network. Otherwise actual market data is up until the year 2010.

Figure 1.2: Canadian residential broadband market share of TelCo vs. CableCo [CRTC10W3]

It can be observed that while Bell Canada has an edge over Rogers Cable in providing Internet

services to both residential and business users, it lags behind when it comes to only residential

users. This is likely due to the fact that Rogers Cable has more television subscribers and sells

more Internet lines than Bell Canada under the incentive of bundling services. This is an

important concept as bundling of services (for a discounted price to the customer) reduces

customer churn and can leverage popular services (like Rogers Cable Television) to sell its

secondary services (Internet and Telephone). Figure 1.2 demonstrates that fibre-optic adoption

will capture some of the cable customer market, by the year 2017, given a take rate of 20%.

0

1

1

2

2

3

1995 2000 2005 2010 2015 2020

Ho

use

ho

lds

(Mill

ion

s)

Year

Internet Households Telco Cableco

7

c. The Competitive Model

With a strong industry developing around communication, the Canadian Radio-television and

Telecommunication Commission of Canada (CRTC) was formed in 1976 as a regulatory

watchdog for the industry. Consumers do not experience any particular difference in service

regardless of the provider, for the same bandwidth offering and thus the markets are fairly

competitive. Consumers are ultimately attracted by two things: speed and cost. This puts the

pressure on Internet Service Providers (ISPs) to outdo one another and drives infrastructure

advancement.

Governments and service providers often work together to provide their citizens/users the best

service. While the government mandates certain targets and standards (in terms of accessibility

and bandwidth), ISPs strive to find the efficient way to meet those targets with the best return

on their infrastructure investment.

There are two main models that apply to market competition between ISPs: Facilities-based,

and Non-Facilities-based Competition. The former applies to markets where each ISP is

responsible for their own physical network structure, while the latter is for markets where ISPs

share the physical network. In the Ontario market being studied, the physical network is

controlled by two major market dominating players, one a TelCo and the other a CableCo

(really different operators depending on the geographical location being considered, but these

will all be treated as one class), each operating their distinct networks, one through co-axial

cable and the other through traditional copper wires. Smaller ISPs offer minor competition by

renting Unbundled Network Elements (UNE), such as local copper loops, or coaxial cables, from

the TelCo or CableCo respectively. This makes it a UNE based market and an interesting one to

study.

8

d. Study Motivation

One can see how a simple voice service telephone in its most rudimentary form transformed

into the modern, complex and ubiquitous telecommunication networks of today. As

communication improves it improves the quality of our lives, but also raises our expectations of

what we demand of the firms offering such services. It is our sense of technology entitlement

that drives innovation to fuel our desires to have quicker, more accessible access to

information, entertainment and interaction. As ISPs continue to make iterative infrastructure

advancements, our bandwidth demand outpaces the network’s capability at an accelerated

rate, thus the need for a complete overhaul of the existing access network becomes evident to

future-proof the service offering.

In the next chapter, Literature Review and Scope, previously similar studies are discussed and

the contributions that they have made, followed by the thesis explaining the different factors

that went into building the model, sources of primary data, what is considered as in-scope and

as out-of-scope, and what the thesis aims to achieve. Next chapter, Industry Analysis, the thesis

explores two very important considerations at a granular detail: Canadian Bandwidth

Requirements and the Access Technology Landscape. In the chapter following that,

Methodology, the thesis delineates the actual equations and shows a detailed step by step

flowchart of how internal variables are used to formulate the NPV for each deployment option.

The thesis then tests two locations in the Results and Discussion section, while performing a

sensitivity analysis to determine which parameters most affect deployment choice. This leads

to the final chapter, Conclusion, summarising the findings and offering suggestions for future

work.

9

2. Literature Review and Scope

a. Technology and Bandwidth Demand Estimation

As early as 1991, A.M.Noll made one of the first predictions on bandwidth growth [NOLL91]. His

initial bandwidth estimates are only a miniscule fraction of what is used today. This is due to

the advent of graphics rich content being delivered over the Internet, which was not something

foreseen two decades ago.

Cisco conducts an annual review of the last five years of BW growth, called the Cisco Visual

Networking Index [CISC10W3]. In this they estimate how much Internet traffic travels through

different channels delimited by region, country, and the type of traffic, whether is it consumer

or business. Of particular interest, they also estimate the breakdown of consumer traffic by

country by the type of activity (File Sharing, Internet Video, IPTV, Web/Data, Video Calling,

Online Gaming and VOIP). This is particularly useful as it gives somewhat of a trend, even

though some of the forecasts have significant room for interpretation. Similar to Cisco’s annual

review, the University of California at San Diego publishes an annual report (How Much

Information) on the amount of information that Americans are exposed to annually [GIIC09]. Their

methodology for estimating this is interesting and it looks at how information spread is rapidly

changing.

More recently, Mittal in 2001 studied publicly available data from four institutions to predict

Internet traffic growth using two methods: polynomial extrapolation, and rate of annual

growth, till the year 2005. This was however limited to studying the rate of increase in traffic to

and from these institutions. Using the two methods, traffic growth boundaries were

established. Nevertheless, the growth in recent years has exhibited significantly accelerated

growth over these estimates. [MITT01W3]

Analysts have also looked towards Nielsen’s law of Internet to help them predict growth in BW

offering. In 2007, A.Marshall looked at quantifying this rate of growth of BW available to the

customer into the future with more recent data as a conservative assessment. Nielsen’s Law

10

states that “a high-end-user's connection speed grows by 50% per year” [NEIL98W3] and this is

taken as an extremely conservative estimate for what the demand might be in the future. It

explores the topic using three sources: market U.S. provider data, the average U.S. OECD

published data, and the average BW across 30 countries, also from OECD data. The paper

concludes this point will be reached sometime between the years 2010 and 2013. [MARS07]

Another study, written by Thompson in 2001 looked at the increasing “edge demand”, also

known as end-user bandwidth [THOM01]. The paper applied a technological innovation growth

model (using sigmoid curves) to estimate what the adoption rates of certain services such as

telephone, VoIP, video phone etc. have been historically. These are used to predict user growth

in these technologies indicating their maturity in the adoption cycle. This paper uses sigmoid

curves, but instead applies them towards access technologies (dial-up, xDSL, co-axial, fibre)

coupled with the number of customers using bandwidth bands (5 to 9 Mbps, 10 to 15 Mbps

etc.) historically to predict how many people in a controlled Canadian environment will use a

particular access technology, including FTTx technologies.

To understand the market dynamics, Cardona et al. found in their 2007 paper that xDSL

markets are elastic and that cable networks are likely to be in the same market [CARD07]. They

found this using an H-M Test which is used to find market definition and discovered that in

markets where both cable and xDSL technologies are found, their demand is elastic indicating

they constrain each other. Where cable is not found, xDSL and narrowband (or dial-up, modems

etc.) exhibit less constraint on one another. It is because of this finding that we can assume that

cable and xDSL (and through analogy, future access technologies) compete for the same

market, allowing us to use sigmoid growth curves to predict technology adoption.

11

b. Infrastructure Modelling, Planning and Fibre Feasibility

Banerjee and Sirbu [BANE03W3] have studied how competition and industry structure affect fibre

deployment. They have considered which architecture is most suited for deployment by a TelCo

investor, from a competitive perspective. Their study is similar to this thesis except their study

is mainly a comparison between active versus passive networks, where they conclude that

passive networks have the lowest long-term cost structure. They also looked at understanding

how unbundling at the physical plant level can spur competition while driving the lowest cost

structure, modelling deployment costs at a granular level.

In 2003, Weldon and Zane published a paper [WELD07] that explored the architecture from a

geographically efficient standpoint, and compared VDSL, active and passive FTTH networks, and

thus their work is similar to this thesis. They compared network costs, broken down by where

the cost was incurred, for the different network options at different take rates (the speed at

which a technology is adopted within a population). One of the differentiating things they

looked at was to understand how network costs increase with increasing BW provided per

subscriber, and found VDSL costs skyrocket in comparison to FTTH, as is expected. Finally, they

looked at how network costs decrease with increasing take rate, and linear housing density.

Their work has had a significant impact on the literature, as well as this thesis, and has been

cited many times as a source of pertinent information.

Another very influential piece of work on this thesis is that done by Casier in 2010 [CASI10W3]. It

gives an overview of how to perform an economic evaluation of FTTH deployment and gives an

excellent background on the differences between DSL, cable and fibre. It further goes on to

dimension the network and its associated costs, looking at especially the details of operating

expenditures (OpEx). The paper goes on to look at competition from a game theory perspective

and explores the results of a sensitivity analysis on this. They outline many different geographic

and adoption models that one can use as well as discuss the effect of government intervention

with FTTH deployment.

12

c. Scope of study

This section highlights the considerations that went into creating the model. It considers the

literature review performed in the previous chapter and outlines the scope of the thesis. The

work in the thesis looks at fibre-optic technology as an option to upgrade the access network

from a TelCo’s perspective, and thus the thesis is particularly useful to those TelCos that are

facing competition from CableCos as their networks need to keep up with the demand for

bandwidth.

An integral part of this thesis is to understand how the end user’s Internet bandwidth demands

will increase over the next decade, so that it may be accounted for in capacity planning. There

are several analysts out there that are predicting what this future growth will be, however they

all vary significantly in their estimates. This thesis looks primarily at how demand for BW

offerings will shift from higher disparity between up-take of offerings to lower disparity

between up-take of offerings. See Figure 3.5 for more details on how this is converging.

From a demand perspective, the thesis considers how the market’s Internet penetration would

grow. It also considers how the demand for bandwidth and access technologies will grow using

historical data input into sigmoid functions. This estimates how many users will adopt fibre

optic technology, in a competitive environment where the CableCo growth is an unabated

threat, assuming they deploy the competing technology, DOCSIS 3.0.

The thesis has been taken from a Canadian standpoint around the city of Toronto, which is a

metropolitan city core surrounded by suburbs. Furthermore, for the most part, the Canadian

TelCo network landscape is still running on legacy telecommunications copper loops. This is

common for most large cities in North America, and thus, the context of this thesis could apply

in many similar circumstances.

The work here focuses on the development of a model, which outputs financial indicators that

can be used to assess the financial viability of two alternative network architecture options.

These two options, FTTH and FTTM, both involve fibre optic installations but have significantly

different considerations when it comes to installing them, as they operate on different

13

equipment and differ in the reach of fibre, and as such BW provided. This makes a difference in

the two options’ financial feasibilities. The model is flexible enough to accept various user

inputs such as demographic information, historical Internet usage, building preferences,

equipment and other miscellaneous costs to generate an output indicating the cash flows over

the life of each mutually exclusive project (FTTH versus FTTM) over a definable outlook period.

The work does not include any consideration of upstream investments in fibre backbone or

further than the Central Office (CO), as these are considered common elements of both

network architectures. See Figure 3.7 for the in-scope network.

What is included is specific CO equipment, outside CO equipment and the per-home

installations required to support either technology. The lengths of fibre, and copper required

under each architecture is also modelled for both Greenfield (GF), and Brownfield (BF)

deployments. The model also considers whether the deployment is aerial or buried, as buried

deployments cost magnitudes more than aerial deployment and should the network planner

have a choice, aerial deployments can make FTTH a more viable option over FTTM.

Furthermore, market demographics are explored such as household densities and living

concentrations (percentage of Single-Family-Units versus Multi-Dwelling-Units). The geographic

model considers the distance from the distribution point to the home. This distance depends on

whether it is FTTH or FTTM because as the serving size of both FTTH and FTTM are different,

they will have slightly different lengths of fibre installed up until that point. The greater

difference is from the distribution point to the home, where FTTH will have to extend fibre all

the way to the home, but FTTM will stop and make use of existing copper wires over that

remaining distance.

The demand side and geographic considerations of the model will enable a granular assessment

of the project’s financials over the outlook period. The model only explores the two

architectures from this cost perspective to understand the best alternative given a certain set of

parameters. It also has the ability to change parameters for any given scenario to enable

network planners to make better informed decisions when planning what to deploy in a

particular area. Costs may not be the only consideration for a TelCo deploying fibre. Other

14

factors include enabling future-proof bandwidth to customers so that market share is retained,

or even about maintaining a presence in a particular area. Hence, the model addresses this

need also by offering easy sensitivity exploration of a case to understand how differences in any

particular factor affect overall project feasibility.

The following summarises what is in-scope, and also out-of-scope for this thesis:

In-Scope:

The Greater Toronto Area (GTA) and its inhabitants that are either on cable (DOCSIS 1.0,

2.0, 3.0), xDSL (aDSL, aDSL2+, VDSL2) or dial-up access technologies, and their BW demand.

The comparison between particularly the passive optical network (PON) FTTH and active

optical network (AON) FTTM.

The equipment required inside the CO and outside plant equipment for both FTTH and

FTTM and what needs to facilitate that connection to the home.

The distance between the Central Splitting Point (CSP) and the Home under FTTH, or the

distance between the Remote Power Node (RPN) and the Home under FTTM.

Out-Of-Scope:

Any areas outside the GTA area, or any people who use any wireless technologies

independent of wireline technologies such as WiMax, LTE, 3G/HSPDA/HSPA+ etc. This

market is not modelled for demand.

Comparison or suitability of PONs versus AONs on a wider scale than just FTTH and FTTM.

The equipment or the distance between the CO and the CSP or RPN. This is considered as a

fixed cost ($30,000) and is also known as the fibre backbone.

The effect of distance between the FTTM node and the home in respect to BW delivery.

15

3. Industry Analysis

a. Consumer Bandwidth Requirements

Over the last two decades, the Internet has seen exponential growth in the number of users as

governments mandate higher accessibility standards while pushing for higher speeds. The

Internet has become the medium of choice to source information and conduct business. Most

importantly however, it has evolved into an entertainment hub with the advent of better

compression technologies. Media is increasingly delivered over the Internet through Person-to-

Person (P2P) sharing. As websites such as YouTube, Facebook, Twitter and many other online

content usage sites become more popular, increases in the overall bandwidth (BW) are

required to meet a user demand. Applications emerging today, such as Internet based

television (IPTV), are seen as the next iteration in content delivery and will require

unprecedented BW, especially with 3-D technologies not too far away. Figure 3.1 illustrates this

trend in North American consumer traffic as projected by CISCO. [CISC10]

Figure 3.1: North American consumer internet traffic in petabytes per month [CISC10]

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

PB

/mo

nth

16

Furthermore, the demand for BW is only going to increase as more people rely on the Internet

for much of their communications needs, including telephony, text transmission and TV. Figure

3.2 illustrates the Internet penetration rates amongst Canadians over the last two decades and,

as can be seen, the market is projected to be saturated by the year 2025, but about 90% of the

population will have access by as early as the year 2012. This increase in the number of users,

each requiring more BW, has driven ISPs to constantly keep pace with this surging demand by

investing in newer infrastructure that allows more users to use more of the Internet, faster.

Figure 3.2: Canadian internet penetration – projected to 2025 [WORL10W3]

A study of how per-user BW is growing within Canada reveals that the majority of users are

casual Internet users (using less than 0.025 Mbps on average), while very few are power users

(using more than 0.1 Mbps on average) as is illustrated in Figure 3.3. This was calculated using

the Canadian Internet Usage Statistics (CIUS) survey [STAT07W3], [STAT05W3], where every user’s

consumption was estimated by characterising what their activity on the Internet was (using

bitrates to estimate their daily usage), and their frequency of Internet use. Please refer to the

Appendix A1 to see a sample calculation of how individual Internet data has been calculated

using qualitative and quantitative categorical data.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Inte

rne

t P

en

etr

atio

n R

ate

17

Figure 3.3: Canadian internet usage distribution [STAT07W3], [STAT05W3]

Figure 3.4 is a representation of the peak BW traffic through one uncongested Digital

Subscriber Line Access Multiplexer (DSLAM) unit that serves approximately 850 users. It

illustrates that consumer demand for BW is likely to be exponential in nature, at least in the

next decade or so, however even that is a fairly conservative estimate considering how

multimedia convergence through the Internet is only going to accelerate that demand even

more. As the majority of users are casual Internet users that use an extremely low proportion

of available BW at any one given time, it is viable to assume that the peak represents the usage

requirement of a few power users and as such the standard that should be set in determining

future BW requirements. The trend suggests demand would exceed 100 Mbps per DSLAM by

2012 and as a result the current TelCo network infrastructure’s capacity to support these users

is eventually going to be exhausted. This concept is explained fully in Section 3b (Canadian

Access Technology Landscape).

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2

Nu

mb

er

of

Use

rs

Bandwidth Usage (Mbps)

Users 2005 Users 2007

18

Figure 3.4: Canadian internet usage Personal communications with ISP]

While this offers a broad perspective on how BW is going to grow, it is warranted to see how

the demand for BW offerings is going to grow as it is primarily the growth in the highest

offering of BW that will drive the uptake of fibre-optic technology. Figure 3.5 demonstrates

how the demand for BW will increase only from a data perspective discounting that BW that is

attributed to VOIP, IPTV etc., but as can be seen the general trend is towards higher BW

offering adoption as it becomes available. Traditionally, Internet users have been classified as

casual, regular and power users. An interesting observation is that this classification can be

seen in the demand for BW as well. By the year 2025, demand for BW speeds between 10 and

15 Mbps is the highest amongst the population even though there are higher speeds available.

These users are likely those that migrated from previously lower bands. People demanding

between 51 and 100 Mbps still represent the power users, but this demographic is growing and

will not remain the strata with the lowest population as users subscriber to higher BW. Another

interesting observation regarding how BW is changing is the disparity between the different BW

offerings and how it is decreasing over time.

y = 9E-13e0.0008x R² = 0.8409

0

20

40

60

80

100

120

Mb

ps

Tran

smit

ted

ove

r a

DSL

AM

Peak BW Sent (to customer) Actual (Mbps)

Peak BW Received (from customer) Actual (Mbps)

19

Figure 3.5: Canadian bandwidth usage projections [CRTC10W3]

Once BW adoption has been identified, a good estimate of the number of people who will

subscribe to next-generation technologies (also known as early adopters) is known. Assuming

that of these technologies, fibre-optic (FTTH, FTTM) and cable (DOCSIS 3.0) are the only

contenders for these people, we can estimate using the historical natural growth rates of

existing technologies. This enables the model to forecast what the number of users would be

for both cable and fibre-optic offerings at different uptake rates. Figure 3.6 shows how access

technologies have historically changed and forecasts the change in access technology adoption

given a 20% uptake in fibre-optic technologies. This uptake takes into account the migration of

xDSL customers to fibre-optic technologies by discounting the growth in xDSL by the growth in

fibre (accounting for the customers who have migrated technologies) as fibre is effectively

competing with xDSL. Even though this may seem like the TelCo is eating its own lunch, it is

inevitable. Once the xDSL customer growth is exhausted, fibre grows independently and

competes only with cable technologies, particularly DOCSIS 3.0. This is why TelCo growth will

0%

10%

20%

30%

40%

50%

60%

70% P

erc

en

tage

of

Po

pu

lati

on

(upto 1.4 Mbps) (1.5 to 4 Mbps) (5 to 9 Mbps)

(10 to 15 Mbps) (16 to 50 Mbps) (51 to 100 Mbps)

20

start out slow and Figure 3.6 shows that VDSL2 (the latest in xDSL technology) will decline quite

quickly as people jump to fibre-optic access.

Figure 3.6: Forecasted growth in access technologies with 20% fibre-topic uptake [CRTC10W3]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Inte

rne

t Su

bsc

rib

ers

(M

illio

ns)

DialUp ADSL ADSL2+ VDSL2

FTTx Docsis 1,2 Docsis 3 Telco

21

b. Canadian Access Technology Landscape

Two types of access technologies exist: fixed and wireless. The fixed technology includes fibre-

copper combinations used predominantly by telecommunication companies (TelCo) that have

extended their reach through their telephony network, and the other is fibre-coax, used by

cable companies (CableCo) traditionally involved in providing television services. Of the wireless

technologies, there exist several different standards and technologies but their reach and BW

throughput is sharply limited today in comparison to fixed technologies. Wireless technologies

are not considered here even though they enjoyed rapid adoption, because for the foreseeable

future, the majority of wireless access will predominantly be limited within people’s homes

using residential fixed BW sources. This study focuses on fixed access technologies where fibre

installation is of the primary interest to TelCos that are interested in upgrading their networks

to compete with CableCo speeds.

The TelCo provided access network, the term for the entire underlying telecommunication

infrastructure (see Figure 3.7), in Canada is starting to reach its physical limits in being able to

provide the necessary bandwidth to the consumer. The Central Office (CO) provides an

aggregation point for the Distribution Areas (DAs) served by it, and transmits data between

them and the Internet along the network backbone, which links the CO to the Internet. It serves

the DAs along feeder circuits which, along with the backbone, use fibre-optic cables. The

connection between the DA to the home however, the local-loop, is usually still on copper in

today’s traditional access networks.

Distribution Area

Central Office

Backbone Feeder

Figure 3.7: Access network infrastructure levels

22

The inherent limitations of copper cables within the DA are what constrain the BW capabilities

as speeds attenuate with distance, based on xDSL technology. Figure 3.8 illustrates the speed at

which bandwidth attenuates with distance between the aggregation point and the home. Only

at distances of less than 450 metres does VDSL2 compare as a viable deployment option.

Figure 3.8: Bandwidth attenuation over copper networks [WIKI10W3a]

To stay competitive, Internet Service Providers (ISPs) have been making incremental

improvements to their network backbone by replacing copper with fibre-optic cables.

However, it is now evident that this is not enough. While the Internet continues to morph into a

multi-media rich environment, copper becomes unsuitable at the current distances between

the DA and the home, essentially leaving customers farther from the aggregation point with

lower BW speeds. As making infrastructural improvements to the local-loop (connection

between the home and DA), requires major capital investment or expenditure (CapEx), it is

logical to provision network technology that will provide for future bandwidth requirements for

as long as possible.

Technological advancement using co-axial cables continues to improve while that for copper

seems to be nearing its capacity for further innovation due to technical limitations but also due

to alternative media such as fibre becoming increasingly inexpensive. While CableCos are

0

20

40

60

80

100

Do

wn

load

Rat

e (

Mb

ps)

Distance (m)

VDSL2 ADSL2+ ADSL2 ADSL

23

pushing higher bandwidth throughputs on their coaxial lines under DOCSIS 3.0 technology,

TelCos are looking for effective ways to deliver faster bandwidth on their copper networks.

Over the last decade, TelCos have been moving their distribution points closer to the consumer

to deliver bandwidth over more fibre, that feeds the distribution point, and less copper, that

distributes the fibre feed to the end-customer. BW on co-axial cables is not affected by

distance, and as such can deliver sustained bandwidth over longer distances. However, cable

signals are split over many households, using what cable network planners call a concurrency

ratio, and this is planned to compete with the TelCos advertised bandwidth. This allows cable

companies to deliver higher bandwidth to consumers for a relatively lower infrastructure

investment.

Fibre-optic cables have the ability to deliver virtually unlimited bandwidth, and have today

become inexpensive enough so that they can be used in lieu of copper. However, the

infrastructural investment required to refurbish the access network’s local loops is significant

enough to warrant a closer look at optimising the architecture so that bandwidth throughput is

provided as cost-effectively as possible. For an incumbent fibre-optic investor, especially one

with underlying network assets has a choice to implement either of two structures: Fibre-to-

the-Home (FTTH), and Fibre-to-the-Micronode (FTTM). While the former is a complete overhaul

of the local loop to create an all-fibre network, the latter leverages existing copper to the

extent that the speeds offered are comparable (for the very near future) to an all-fibre network,

but saves on major construction costs involved with an all-fibre network.

In an FTTM environment, this is done by laying fibre to an aggregation point that is generally

less than 450m away from the home to ensure that at least 100Mbps can be provided to

consumers. Although, as figure 3.4 illustrated, this would only be the bare minimum required to

meet consumer demand, and will soon be outpaced. An FTTH deployment however can be

significantly more costly due to trenching required and so the right balance between BW reach

and trenching costs needs to be explored. These considerations form the basis of this thesis to

better understand how demographic factors affect the viability of deploying either FTTH, or

FTTM and where each is more suited.

24

4. Methodology

a. Overall Model Structure

The following model (outline depicted in Figure 4.1) evaluates the feasibility of two fibre

network architectures based upon several considerations derived from user input. The first of

these factors are considered in the geographic model that estimates the size of the available

market for the service. These statistics coupled with Internet penetration rates, shifting

bandwidth (BW) demand patterns and trends in technology adoption forecast the Internet

market’s landscape over the study period. This provides an estimate of the future market share,

a measure of fibre take-rate (the percentage of households that subscribe to the deployed

service versus the total number of service-ready homes), and how the shift towards fibre

technology will affect existing VDSL2 subscriptions, and competing cable technology

subscribers. The user also has the option to select one of two building modes: Batch Build, and

Continuous Demand Build; both explained in further detail under the section Deployment

Model and Assumptions. This module determines how many houses must be passed in a

particular year in order to meet Internet demand and is a measure of capital injection

frequency. Both geography and architecture will determine the equipment inventory,

trenching, and the combination of fibre and copper lengths that are required. Once equipment

costs and other financial parameters are defined, the model calculates the Net Present Value

(NPV) for each architecture, indicating the more feasible option as the one with the higher NPV.

Each module’s flowchart is depicted alongside its explanation to show how the data are being

used. For a description of the variables used, please refer to the variable list at the beginning of

the thesis.

25

Figure 4.1: Overall process model

26

b. Market Sizing Module

This module takes user input in the form of population, Internet penetration, housing and

Internet usage metrics to estimate the number of present and future Internet users. These

users are segregated by the type of housing they live in, what bandwidth they demand, and

how the market share will be distributed between the market players.

Everett Rogers’ theory of diffusivity [WIKI10W3b] states that technology adoption comes in stages

where different people will adopt innovation at different points of the technology life-cycle.

This forms the basis of using sigmoid functions (and thus the Fisher-Pry [FISH71]) model to

estimate growth in Internet users, BW offerings and access technology adoption, as it is used

below. For a derivation of how constants within the Fisher-Pry approximation are determined

( and T0), please refer to Appendix A1.

i. Assuming a linear population growth rate (a simplifying assumption for a particular serving

area), population can be determined at any point in time (Pt). Using a statistical Fisher-Pry

model and Internet penetration rates at two times the number of people connected to the

Internet can be extrapolated for the time frame in consideration (PtInternet). These can then

be segregated into the number of Living Units (LUs) that are either Single Family Units such

as fully/semi-detached housing (SFU) or Multi-Dwelling Units (MDU) that are in some sort of

apartment complex. Coupling these statistics with average household size yields the

number of LUs connected to the Internet (LUtSFU,MDU), which is analogous to the demand

(DtSFU,MDU). It is important to know this housing characteristic as it affects the density, overall

engineering and deployment of the network, as will be shown later under the section on

Sensitivity Analysis.

(4.1)

(4.2)

(4.3)

27

ii. Once Internet household demand has been determined, the model estimates the shift in

BW usage (and the relevant access technologies) amongst households. Over the years, BW

available to the consumer has been increasing steadily. Figure 3.5 illustrated that the

number of households subscribing to slower speeds is dwindling in favour of faster ones

giving credibility to the assumption that BW can be modelled as an evolutionary innovation

and sigmoid functions can be used to extrapolate the growth or decline patterns of BW

subscriptions. The growth patterns are modelled independently to estimate their raw

growth potential, as a percentage of the total available market (GtBW). To account for the

shift in BW demanded, each BW offering’s raw data set is normalised by dividing it by the

sum of the raw growth values for each BW offerings. This provides meaningful estimates of

how a particular BW is going to grow in a market with respect to other BW speeds

competing for the same consumers (BWt% of Total). Consequently, the number of LUs

segregated by BW can be determined (LUtBW).

(4.4)

(4.5)

(4.6)

iii. Similarly to estimating BW usage, growth of access technologies can be determined also

using sigmoid functions. There has been a steep decline in the number of households using

Dial-Up technology with the advent of xDSL and Cable technologies. This raises the question

as to how much longer existing technologies will be able to compete with newer installation

such as fibre. It is important to note that since fibre is an access network upgrade and not

simply an iteration of existing copper technology, subscribers will inadvertently use the new

fibre infrastructure, whether they subscribe to the high speeds associated with fibre or not.

It is reasonable to assume that any future growth in VDSL (or the TelCo’s “High Speed

Access”) will be hampered by the growth in FTTx. To account for this new technology’s

impact on previous access technologies, the model adjusts VDSL growth by subtracting from

28

it the growth in FTTx (GtxDSL

adjusted = GtxDSL

unadjusted - Gt

FTTx). This implies that FTTx subscriptions

will come first from existing TelCo customers, and then from the competing CableCo or

brand new customers. To account for the growth within a market with competing

technologies, each technology’s raw data set is normalised by dividing it by the sum of the

raw growth values for each technology, similarly to how growth in BW was calculated.

(4.7)

(4.8)

Once the number of LUs on each bandwidth offering has been determined, and the

percentage of the market on each access technology is known, the number of LUs on each

technology can be computed. This provides good insight into how customers migrate

between technologies. Table 4.1 describes the bandwidth capability of the different

technologies.

0-1.4 Mbps

1.5-4 Mbps

5-9 Mbps

10-15 Mbps

16-50 Mbps

51-100 Mbps

Dial-Up (Modem) x ADSL x x x ADSL2+ x x x x VDSL2 x x x x x FTTx x x x x x x DOCSIS 1.0, 2.0 x x x x x DOCSIS 3.0 x x x x x x

Table 4.1: Technology bandwidth capability

To segregate the LUs that are on each BW into the technology they are using, the model

multiplies the percentage of the market served by a particular technology by the number of

LUs using each BW which that particular technology is capable of serving giving the LUs on

each technology (LUtTech).

(4.9)

29

iv. The market sizing module concludes by calculating the total customer base for each of the

TelCo and the CableCo. Consequently, the number of TelCo LUs segregated by BW can be

determined (LUtTelCo|BW). This is later important as revenues are calculated based on how

many LUs are on a particular BW plan, and different BW plans are priced differently.

(4.10)

(4.11)

30

c. Build Module

Accepting user defined build preferences the module calculates an annual build schedule for

the life of the project, to meet the desired population reach by the specified parameters.

i. Rollout of FTTx deployment can be done in one of two ways: Batch Build, or Continuous

Demand Build. The user specifies their preference in the model and the module calculates

the number of houses that need to be passed annually. For the Continuous Demand Build

option, the module determines how many LUs demand Internet at the current time for any

time after the initial build start date. To simplify the analysis, an implicit assumption is that

all Internet demand comes from within those houses that are being passed. In the Batch

Build option, houses are passed within a fixed timeframe, built evenly every year (Bannual) to

satisfy overall projected demand for the timeframe under consideration. Outside this

considered timeframe, the module continues to pass Bannual houses whenever Internet

demand exceeds the supply, but stops when houses passed exceed the demand in the area.

(4.12)

(4.13)

(4.14)

Batch Build

Continuous Build t > bt1

t < bt1

t < bt1

t > bt2

bt1 < t < bt2

(4.15)

31

ii. The number of fibre drops (final connection to consumer from the street) required are

determined based upon whether the build is Greenfield (new build) or Brownfield (over-

build). Greenfields will require one drop to be placed for every SFU as well as one per MDU.

The rationale behind this is that MDUs are internally connected (by the building developer)

and that only the connection from the street to the building need be made. Additionally, it

is assumed that all Greenfield Deployments will have fibre laid, as opposed to copper,

regardless of what technology, or service the customer demands. This is because all BW

services can be provided on the new fibre architecture.

In the case of Brownfield FTTH deployment, fibre needs to be placed at the same rate as in a

Greenfield. However, in a Brownfield FTTM environment, copper drops already exist and

thus need not be installed.

Finally the number of Customer Premises Equipment (CPE) required will depend on the

number of FTTx subscribers. Both customer drops and CPE are made at the time of

customer subscription to the service.

BF|FTTH

BF|FTTMBF

GF

(4.16)

(4.17)

32

d. Deployment Module

This module dictates a large part of the architecture in terms of the trenching lengths that are

required, as well as providing key input variables for equipment inventory. It accepts

geographic user input and calculates the area that a particular distribution area (the LUs served

by one distribution splice) serves, and how much trenching is required. This geographic model

has been adapted from the geometric grid model proposed by K.Weldon and F.Zane in 2003

(also known as the simplified street Manhattan model) [WELD07], with a few adjustments, namely

it is assumed that all distribution areas are located on either side of a central laneway (as

opposed to a grid) and that the model accounts for Multi-Dwelling-Units (MDU). The adaptation

of having houses distributed homogeneously around a central laneway appears to suit

dwellings more accurately in North America, especially in rural areas where houses are

scattered and the road grid is not evenly distributed. This also adds more flexibility to plan

major dwellings artery by artery.

i. Assuming houses are placed uniformly in a symmetric grid, a reasonable assumption for

most modern dwellings, Figure 4.2 depicts 64 houses, with 8 terminals (each with 8 port

capacity), in the case of FTTH. Alternatively, the VDSL2-Sealed-Expansion-Module (VSEM)

location is shown for a FTTM build (capacity would likely be 48 instead of 64).

Figure 4.2: Geometric model of a distribution area

33

ii. The frontage length (LFrontage, or LF) is a function of housing density and determines the area

that the distribution area is going to have (L2Dist.Area). While this is one distribution area

spliced to 64 houses, a network planner can decide to have many more houses served in a

particular area either by choosing terminals with more ports, or simply have more terminals

per distribution splice.

The frontage length of an SFU is calculated by first accounting for MDUs. This is done so that

MDU buildings’ (living units order(s) of magnitude greater than SFUs) area footprint can be

accurately measured before considering it in a calculation for frontage. This assumes that

the area traversed by each MDU will be equivalent to that of how many LUs it can support

per floor. For example, the footprint of one MDU that houses 16 LU per floor will, from a

geographical perspective, only take up the area of 16 SFU houses (and can be treated as one

quadrant of Figure 4.2 above).

(4.18)

(4.19)

(4.20)

iii. The line running through the large circle in Figure 4.2 (Home Trench) represents the length

required to traverse this Distribution Area (DA) and its total length is LDistArea-Home. It must be

noted that this is but one DA, and that in a particular year, there would be multiple DAs

(#tDistArea), based on the housing build schedule.

(4.21)

(4.22)

34

The geometric models created for SFU and MDU are independent of each other, a

reasonable simplification as most MDUs are located together. It is assumed that each

apartment size is the same as that of a house and so the length that a building traverses

(LfrontageMDU) is based on how many LUs are accommodated per floor. There exists a

Distribution Trench that runs up until the large circle (in Figure 4.2) and is half the length of

the Home Trench for every DA. It represents the length required to reach the DA from the

Central Office (CO) and its total length is LCO-DistArea.

(4.23)

(4.24)

(4.25)

In a FTTH build, there is a Central Splitting Point (CSP) that splices the feeder fibre into

individual customer fibres. This should not make any difference to the length traversed

between the CO and the DA, as it is assumed that there would be maximum trench sharing

and that the CSP will simply be placed along the same trench length. Figure 4.3 depicts both

the SFU and MDU geometric model.

35

Figure 4.3: Overall geometric model of MDU and SFU housing

iv. Table 3.2 outlines the decision matrix for the distances required for each architecture. It is

assumed that in brownfield environments, the necessary conduits would exist in their

totality. Thus, no directional boring would be required, only micro-trenching which is

excavating to open up existing conduits, and is much more cost effective.

FTTH FTTM Trench Micro Fibre Copper Trench Micro Fibre Copper

Greenfield Buried

CO – H CO – H CO – H CO – DA DA – H

Greenfield Aerial

CO – H CO – DA DA – H

Brownfield Buried

CO – H CO – H CO – DA CO – DA

Brownfield Aerial

CO – H CO – DA

Table 4.2: Decision matrix for trenching, micro-trenching, fibre and copper placement

36

If in a Greenfield environment, trenching will be required from the CO to the Home.

However, in Brownfield environments existing conduits are present which were used to

house the previous copper architecture and thus these can be used. These are denoted as

“Micro” for Micro-Trenching, regardless of whether this is FTTH or FTTM deployment,

saving the developer boring costs or full-fledged trenching costs.

BF

GF

BF/FTTH

BF/FTTM

(4.26)

37

e. Equipment Inventory Module

As the geographies of the architectures have been determined, equipment inventory can now

be determined based on equipment carrying capacity, and build schedule. While investments in

CO equipment are made on an annual basis based on the number of incremental subscribers,

network equipment is installed according to the build schedule of how many houses are to be

passed per annum.

i. As for the FTTH inventory, it is assumed that every MDU gets its own Central Splitting Point

(CSP), which is a fibre distribution point. Large Area Splices are made based on the capacity

of a DA while Small Consumer Splices are made for each Terminal. MDUs are not considered

for Small Area Splices as they have their own CSP and it is assumed that the building is

internally spliced. Grade Level Boxes (GLBs), used as housing enclosures, are required for

each DA that serves SFUs as well as one for every CSP, taking into consideration MDUs .

(4.27)

(4.28)

(4.29)

(4.30)

(4.31)

(4.32)

(4.33)

38

(4.34)

(4.35)

Once equipment has been finalised, the length of fibre required is calculated. In the case

that terminals are pre-stubbed (come with attached fibre optic cables), fibre is not required

between the terminal and the home. Alternatively, fibre is strung all the way to the home.

Non-StubbedTerminal

Pre-StubbedTerminal

(4.36)

ii. For the FTTM inventory, calculation of network equipment is made similarly to that of FTTH.

There is one VSEM per DA and all other equipment is calculated based on that value and the

carrying capacity of the equipment.

(4.37)

(4.38)

(4.39)

The amount of fibre required is up until the VSEM Area and from that point until the home,

the architecture operates on copper. In Brownfield areas, the copper drop will already exist;

however, in Greenfield areas, it would have to be installed for each DA.

(4.40)

GF

BF

(4.41)

39

f. Financial Module

This module receives equipment inventory, trenching lengths, market variables and user input

(such as pricing and global economic variables) to return the Net Present Value (NPV) for both

FTTH and FTTM. The procedure is as follows:

i. Calculate the real (before inflation) capital injection required for each FTTH and FTTM. This

is a product of the first cost (FC), which includes both the actual hardware and the

installation expenses, of a particular piece of equipment, by how many pieces of that

particular equipment is required in that year. Similarly, the costs of fibre, copper and

trenching are calculated by the required lengths respectively. It is assumed that the real

cost of the equipment does not change over the life of the project. The annual installation

real costs for all equipment are then summed together with the annual cost of laying fibre,

copper and trenching to get the annual real capital injection required for this annually

deployed network infrastructure. Network Value is calculated as the cumulative sum of

annual real capital injections.

(4.42)

(4.43)

ii. Revenues are calculated from three revenue streams: Voice, Video (including pay-per-view)

and Data (segregated into different BW offerings). When deploying FTTx, it is assumed a

certain percentage of data customers will also subscribe to all three services (they’re known

as “Triple-Play” customers). This is an important measure as it strongly affects revenue and

consequently payback on investment. The model factors in a discount for these Triple Play

consumers as well as considering a first-time discount for new subscribers.

40

(4.44)

iii. Maintenance costs are calculated as the cost of maintaining the equipment itself, as well as

the labour costs associated with it. For this the Mean Time To Repair (MTTR) and the Mean

Time Between Repair (MTBR) is defined for each piece of major equipment. The costs for

each equipment are then summed to yield the total maintenance costs in a year. Operating

costs are calculated as the cost of insuring the network, the per-subscriber cost associated

with customer care, and the cost of power, which is assumed to grow linearly.

(4.45)

(4.46)

(4.47)

41

iv. The real cash-flow before taxes is calculated as the difference between inflows (Revenue)

and outflows (Capital Injection, Maintenance Cost, and Operating Cost). These values are

then converted to actual cash-flows by factoring in the rate of inflation. The model accounts

for depreciation by factoring in the Capital Cost Allowance (CCA) which is a fixed percentage

applied on the adjusted value of all assets and half of any capital investments made in the

previous year. These are then factored into the tax calculations.

(4.48)

(4.49)

(4.50)

(4.51)

(4.52)

v. Finally the after-tax cash-flows are determined by subtracting taxes from the before-tax

cash-flows. These are then brought back to real-values using the rate of inflation, and

subsequently brought back to today’s dollars using the Minimal Acceptable Rate of Return

42

that is specified. The sum of the annual after-tax real cash-flows yields the NPV of the

project over its lifetime.

(4.48)

(4.49)

(4.50)

43

5. Results, Sensitivity and Discussion

a. Scenario testing of the model

The model has been tested for three geographically distinct locations. All areas are modelled as

Brownfield, batch-style build to reach 95% of both the SFU and MDU population at the outlook

period, and as 100% buried deployments as this ensures the most pessimistic case.

Area statistics (from 2006) were used where available [STAT06W3]; however certain assumptions

had to be made regarding the number of floors (taken as the weighted average of the upper

limit of 2, 5 or 20 floors, depending on the percent of houses listed segmented within these

brackets by Statistics Canada), and average LUs per floor (taken as a constant value of 16) in

MDU considerations. It is further assumed that average Canadian Internet usage statistics do

not change from area to area. Finally, the outlook period is taken to the year 2025. Table 5.1

outlines some of the modelling assumptions.

Scenario 1 2 3 City Census Tract Number 0307.02 0376.01 0528.40

Area Description Metro Sub-Metro Sub-Urban

Population (people) 13,501 5,346 5,503 Land Area (km2) 1.42 1.04 3.62 Household Size (people/hh) 2.4 3.4 3.5 SFU (%) 21 70.9 97.1 MDU (%) 79 29.1 2.9 Avg. MDU floors 17.5 8.05 2.02 Avg. LU per MDU floor 16 16 16 Inflation Rate Used (%) 2 2 2 Discount rate (MARR) used (%) 9 9 9

Household Density (LU/km2) 3,962 1,512 434 SFU Frontage (metre) 31 30 48 MDU Frontage (metre) 126 119 193

FTTH Capital Expenditure ($) 8.4 M 5.1 M 9.0 M FTTM Capital Expenditure ($) 7.2 M 2.4 M 2.8 M

FTTH Net Present Value 5.4 M -1.5 M -5.9 M FTTM Net Present Value 6.0 M 1.2 M 0.9 M

Bid Winner FTTH FTTM FTTM

Table 5.1: Test location demographics and modelling results [STAT06W3]

44

The results indicate that in all three scenarios, FTTM came out to be the preferred choice.

However, in the first scenario, FTTH was a viable candidate as it had a positive NPV and this

made it a contender. This is important because the TelCo may choose to implement FTTH over

FTTM given its future-proof characteristics. Additionally another issue that needs to be

discussed is that although FTTM is inherently less expensive since the installation to the house

is not necessary (due to existing copper facility), the TelCo may find that FTTH is desirable as

the capacity of the copper pairs will eventually have run out.

It is important to note that the conditions tested are a unique representation of NPV or cash

flows and that they may be different given different input parameters. It may seem that FTTH is

not a viable solution at all, but caution must be taken when interpreting these results. For

example, all scenarios tested have been for 100% buried fibre. Aerial deployment will

significantly affect the NPV and cash flows and as such should be preferred whenever possible.

These results are for demonstration purposes only and a network planner can use the model

created to further test how changing the amount of aerial fibre can affect the overall feasibility

of the project. Similarly, other parameters can also be changed to achieve very different results.

The following section indicates the nature and cost trends of the project. The base testing

parameters are shown in Table C.1 in the appendix. An optimistic and pessimistic case has also

been developed to explain how the price structure varies, and is also indicated in Table C.1.

b. Capital Expenditures per Subscriber

The TelCo is required to deploy capital in an area without prior knowledge of whom, or even

how much of the population served is going to subscribe to the service. Once a customer whose

house has been passed subscribes to the service, the overall capital expenditure per subscriber

is reduced. Figures 5.1 and 5.2 illustrate this trend, and also show how changing SFU

percentage and Aerial deployment percentage respectively will affect this trend (there is little

difference between FTTM deployed as %SFU or %Aerial changes, but significant difference

between FTTH deployments). Table C.2 outlines these changing parameters for extreme values

between 10% and 90% (of both %SFU living, and %Aerial deployment). Similarly Figures 5.3

shows the effect changing population density will have on total cost per subscriber.

45

Figure 5.1: CapEx reduction with increasing subscribers – %SFU variation

Figure 5.2: CapEx reduction with increasing subscribers – %Aerial variation

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

FTTH

Cap

ital

Exp

en

dit

ure

/ H

om

e P

asse

d

Take Rate

Pessimistic FTTM Baseline FTTM Optimistic FTTM

Pessimistic FTTH Baseline FTTH Optimistic FTTH

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

FTTH

Cap

ital

Exp

en

dit

ure

/ H

om

e P

asse

d

Take Rate

Pessimistic FTTM Baseline FTTM Optimistic FTTM

Pessimistic FTTH Baseline FTTH Optimistic FTTH

Three curves are present here

indicating very little difference

between FTTM as variables change.

Three curves are present here

indicating very little difference

between FTTM as variables change.

46

Figure 5.1 illustrates that changing the percentage of SFU dwelling will significantly change the

CapEx per subscriber on a FTTH network as opposed to a FTTM network. This is understandable

due to the fact that FTTH is inherently more expensive due to trenching costs, something that

will escalate quickly with increasing SFU dwelling. Whereas FTTM is not affected by this as it is

not so concerned about delivering to the final home, as it is about delivering to the distribution

point. It is interesting to note that at 10% SFU dwelling (the optimistic scenario), FTTH becomes

slightly less expensive than the FTTM to deploy indicating that FTTH is a cost efficient

investment with low SFU dwellings (consequently high MDU dwellings). Similarly the same

argument is applied to Figure 5.2, where FTTH is much more sensitive to changes in aerial

deployment, again because FTTM does not need to deploy all the way to the home (existing

infrastructure is in place) while FTTH does.

Figure 5.3: CapEx reduction with increasing subscribers – Population Density variation

When population density is changed we notice that FTTM and FTTH are both quite sensitive;

however, FTTM is less sensitive than FTTH, and is also a comparatively inexpensive option for

any population density.

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

$10,000

FTTH

Cap

ital

Exp

en

dit

ure

/ H

om

e P

asse

d

Take Rate

Pessimistic FTTM Baseline FTTM Optimistic FTTM

Pessimistic FTTH Baseline FTTH Optimistic FTTH

Three curves are present here

indicating very little difference

between FTTM as variables change.

47

c. Cash flow Diagrams

This section looks at the cash flows for the three scenarios mentioned at the beginning of this

section. Cashflows are the difference between revenue and annual expenses that include

operating expenses, capital injections, and maintenance operations. Cashflows discussed here

are tax deducted and real (in terms of dollar values for the particular year of the particular cash

flow). Taxes are calculated using allowances (determined from the cumulative value of the

project, annual capital expenditures, and tax deductible amounts using industry CCA rates). For

a more detailed explanation of how cash flows are determined, refer to Section 4f.

In all scenarios, if cash flow starts positive, it is due to existing service (before fibre) revenues

and indicates a large customer base whose revenues can offset the capital costs incurred in that

particular year.

Figure 5.4: Cash flow of FTTH for the three test scenarios

-$1.4

-$1.2

-$1.0

-$0.8

-$0.6

-$0.4

-$0.2

$-

$0.2

$0.4

$0.6

$0.8

Cas

hfl

ow

($

Mill

ion

s)

Year

FTTH Metro FTTH Sub-Metro FTTH Sub-Urban

48

Figure 5.4 shows how the cash flows of FTTH for the three test scenarios. Cash flows all start

out negative due to the high initial investment required, except for the case of the Metro

deployment where much less capital investment per user is required due to the high number of

MDUs. The cash flows become positive between approximately 5 and 9 years, depending on

the type of location. The reason for multiple troughs in the figure is due to the way the model

builds on infrastructure, waiting for demand before it deploys the necessary equipment. Since

the equipment added accommodates multiple users, the revenue from customers is not fully

appropriated until full utilisation on the equipment occurs, and this is another reason as to why

there are many troughs in the cash flow graphs.

Figure 5.5: Cash flow of FTTM for the three test scenarios

In the case of FTTM, illustrated in Figure 5.5, cash flows start much higher than FTTH and

become positive far quicker (between 0 and 5 years). This is because the amount of capital

investment required for the FTTM per user is much less, with predominantly less digging (less

required fibre). The small investment is quickly offset by the revenues that come in from

existing xDSL and new fibre customers.

-$0.1

$-

$0.1

$0.2

$0.3

$0.4

$0.5

$0.6

Cas

hfl

ow

($

Mill

ion

s)

Year

FTTM Metro FTTM Sub-Metro FTTM Sub-Urban

49

d. Net Present Value

The Net Positive Value (NPV) is a measure of how valuable a project is in today’s dollars. It is

often used as a variable to decide whether to pursue a project or not. Having a positive NPV

indicates that the project is not only viable but, in fact, it returns more than the firm expects.

However, in certain cases if the NPV is negative, it may still be approved as the decision may be

determined by the strategic direction of the company. In the context of deploying fibre-optic

networks, the reason could be to secure a market space in the presence of a competitor, who

may also be deploying their own next-generation networks.

Figure 5.6: Net Positive Value of FTTH for the three test scenarios

In the case of a metro area, FTTH is immediately a viable project showing a positive NPV from

the start and thus for the conditions of the metro area tested, it is a good decision to deploy

FTTH is this environment. However FTTH in Sub-Metro and Sub-Urban areas (for the conditions

tested) is not viable and does not produce a positive NPV.

-$8.0

-$6.0

-$4.0

-$2.0

$-

$2.0

$4.0

NP

V (

$ M

illio

ns)

Year

FTTH Metro FTTH Sub-Metro FTTH Sub-Urban

50

Figure 5.7: Cash flow of FTTM for the three test scenarios

In the case of FTTM NPV illustrated in Figure 5.7, all scenarios are viable, especially in the Metro

area where it is highly profitable. In the Sub-Metro and Sub-Urban areas it achieves a payback

between 5 to 6 years and thus can also be considered.

-$0.5

$-

$0.5

$1.0

$1.5

$2.0

$2.5

$3.0

$3.5

$4.0 N

PV

($

Mill

ion

s)

Year

FTTM Metro FTTM Sub-Metro FTTM Sub-Urban

51

e. Sensitivity Analysis of FTTH and FTTM by variable

FTTH and FTTM were compared in Table 5.1, and it can be observed that the areas chosen all

had different population and consequently household densities. Furthermore, they all differed

in the percentage of SFU/MDU living and had different average number of floors for the

buildings that they did have. All these factors contribute to capital expenditures, and thus NPV

of each project. For the purposes of this feasibility study, Table C.1 (Appendix C) outlines the

base testing parameters used. These are then varied to see their effect on FTTH and FTTM NPV.

Figure 5.8: Sensitivity test on baseline parameters

52

i. Geographic Variables

These are seen as externalities upon which the incumbent operator has little control. Operators

are usually mandated to provide Internet as a means of accessibility dictated by government

guidelines and thus often have little choice in where they would like to deploy. This sometimes

means deployment even in unprofitable areas. It is therefore important to know to what extent

these externalities can affect feasibility.

Population density is the factor that has the most effect on the NPV in both FTTH and FTTM

cases. This is understandable because as the population grows, the equipment utilisation

increases and more revenue is appropriated for the same capital cost.

Next, the proportion of the population that is SFU versus MDU dwelling affects frontage length

and thus the amount of trenching. It offers granularity in understanding how MDU units affect

viability. This is because for places with equal areas and equal populations, but with higher

proportion of MDU living, the trenching length might in fact be substantially less than one

where SFU living is predominant. Thus, as the percentage of SFU decreases (or MDU increases)

the NPV is positively affected. It is not so prevalent in the FTTM case because FTTM required

much less trenching that FTTH does and, thus almost has no effect.

The effect of the number of living units (LU) per floor, or the floors per MDU on NPV is

interesting. While the increase in the number of people per floor, or the number of floors per

building positively affects FTTH NPV, the results seem counter-intuitive for FTTM. To explain

this, one needs to understand how the architecture is built. For FTTH, each building can support

up to 576 tenants. Varying our base of 320 tenants by 20% (256 to 384) does not incur any

additional capital investment and just increases equipment utilisation. However, for FTTM, each

VSEM supports 48 households, thus seven VSEMs are needed for 320 tenants. When tenants

are increased to 384, another VSEM needs to be added, negatively affecting NPV as the capital

added is not offset by the small increase in users. Conversely, when tenants are decreased to

256, six VSEM units are required but the equipment achieves better utilisation, thus positively

affecting NPV. This is known as a step-function where the benefits of adding more equipment

can only be seen in multiple steps.

53

ii. Build Variables

Rollout speed dictates how quickly the operator is going to deploy the network. Should it

deploy too quickly, the network will have poor utilisation. This is undesirable for two reasons.

The first reason is the opportunity cost of capital that could have been invested elsewhere and

the second is that pushing a new network out without revenue generated from it affects cash

flows adversely due to the operating expenses of running the network without the subscriber

revenue. Conversely, not deploying the network fast enough means that competitors can

capture new and migrating subscribers. There is definitely a first-mover advantage when it

comes to deploying this network and this should be available before the customer demands it.

It can be seen that increasing or decreasing the deployment time by one year can have a

significant impact on the NPV of both FTTH and FTTM. However, this is only for this scenario. If

growth in fibre-optic technology is faster than anticipated, increasing deployment time may

adversely affect NPV.

In deploying networks, the incumbent usually has little option to either build aerial, or build

buried infrastructure. Usually, this is a decision where municipalities have a strong say in and

thus may not be entirely in the hands of the operator. Aerial builds are significantly cheaper

than buried builds as the cost of trenching is prohibitively high in most cases. By varying this

parameter, it is possible to see how build viability is affected. It is seen that FTTH is much more

sensitive to percentage of aerial build in contrast to FTTM, and this is understandable because

FTTM requires a fraction of fibre cabling to reach the home in comparison to FTTH.

iii. Cost Variables

While the model does not automatically incorporate a change in equipment pricing over time, it

explores how changing the equipment first costs (that include the physical equipment and cost

of installing it) will affect project viability. Similarly, to asses infrastructure costs further,

Boring/Trenching and Micro-Trenching costs are explored to see how these might affect overall

cost. Other costs such as inflation and insurance costs are investigated to see the extent of their

influence on project feasibility. Naturally, trenching costs affect FTTH much more than FTTM,

again due to more fibre being required for FTTH, while FTTM operates on the existing copper

54

wiring. Increasing the equipment costs has about the same effect on both FTTH and FTTM NPV,

while varying the rate of inflation has a negligible effect on both.

iv. Market Variables

Take-up is a measure of how many people will subscribe to fibre and how quickly. When the

architecture is deployed, it is assumed that existing xDSL customers will migrate to fibre-optic

technology over time, and even if some do not, the architecture will still have to be deployed

and passed in front of their homes. Take-up defines how quickly that migration will take place

and as such, is a measure of the new architecture’s utilisation. It also defines the Telco

subscriber growth as the technology positions the Telco to directly compete with the CableCo’s

DOCSIS 3.0 standard. Otherwise, given the shift in BW demand towards higher offerings, the

Telco will observe a churn rate as people migrate towards the higher capability of cable over

xDSL technology. Another strategy to reduce churn is to “lock-in” the consumer over a certain

timeframe by offering them a discount when they bundle all three (data, voice and video)

services together, also known as a Triple-Play consumer. This models how many data users will

also be voice or video users, and thus, increasing revenue generated. The growth in this metric

is varied and investigated. It is seen however, that varying the take up rate by 20% does not

have an appreciable effect on NPV, as much as other variables have had.

The pricing is investigated to see how changing these can affect the project’s bottom line. The

reason that this is important is because for a new service, the operator should know the

relationship of price to revenue, especially because the consumer’s price elasticity to the new

service is unknown, and thus, pricing may need to be varied. It is found that changing the

product pricing has the most effect on NPV amongst other market variables, in decreasing

order of data, video and voice pricing.

Figure 5.9 shows the almost linear trend that variables have on NPV in both the FTTH and the

FTTM case, in the form of a spider plot.

55

Figure 5.9: Spider plot on FTTH and FTTM respectively at baseline parameters

-20%

-10%

0%

10%

20%

-50.0% -40.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

%C

han

ge in

FTT

H N

PV

% Change in Variables

-20%

-10%

0%

10%

20%

-50.0% -40.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

%C

han

ge in

FTT

M N

PV

% Change in Variables

PD %SFU MDUCap BuildTime %Aerial

56

f. NPV Comparison of FTTH and FTTM

This thesis explores the feasibility of fibre-optic investment from a comparative perspective

between FTTH and FTTM architecture. Therefore, the NPV of both FTTH and FTTM has been

studied in the worst case scenarios to see how feasibility varies for FTTH and where it might

outperform FTTM as an investment. The best case scenario (5,000 LU/km2, 100% Aerial, 0%

SFU) is not shown here as FTTH outperforms FTTM for all variables in that scenario.

In the pessimistic outlook, the conditions chosen are a low population density of 1,000 LU/km2,

0% Aerial deployment, and 100% SFU dwelling. The results are illustrated through Figure 5.10

to Figure 5.12 below. The conditions are shown such that two variables are held constant, while

the one being investigated is varied over its range.

Figure 5.10: Lower feasibility boundary as %SFU is varied

Figure 5.10 illustrates the effect of changing %SFU on NPV of both FTTH and FTTM. It can be

seen that in the worst case, FTTH becomes infeasible beyond 30% SFU dwelling in a low density

(1,000 LU/km2), fully buried environment, and only becomes a more feasible option than FTTM

under 5% SFU living within that pessimistic scenario. This indicates that FTTH is feasible, but not

($20)

($15)

($10)

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t P

rese

nt

Val

ue

($

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s)

%SFU

Worst Case Scenario (1,000 LU/km2, 0% Aerial)

FTTH Worst FTTM Worst

57

the best option for SFU living under 30%. However, as will be seen in Section 5g (Figure 5.13)

this percentage rises quite sharply as population density increases.

Figure 5.11: Lower feasibility boundary as %Aerial is varied

Figure 5.11 illustrates the effect of changing %Aerial on NPV of both FTTH and FTTM. FTTH is a

feasible option in regions where more than 50% of the network can be aerially deployed

considering a low density environment (1,000 LU/km2). However Figure 5.14 in Section 5g will

illustrate that at higher population densities, less aerial deployment is required to remain

feasible. Furthermore, in this pessimistic case, FTTH outperforms FTTM only at a very high

accommodation of aerial deployment (about 85%).

($20)

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s)

%Aerial

Worst Case Scenario (1,000 LU/km2, 100% SFU)

FTTH Worst FTTM Worst

58

Figure 5.12: Lower feasibility boundary as Household density is varied

As is illustrated in Figure 5.12 above, FTTH only becomes feasible at household densities above

4,250 LU/km2, given a completely buried deployment with all SFU dwellings. This, however,

improves as other conditions are relaxed.

It can be noted that FTTM is always a feasible case, considering it is always positive at the worst

conditions, in all the pessimistic scenarios. It is therefore a viable project in all cases. However,

as mentioned before, copper capability will eventually be exhausted, and will not be able to

keep up with demand for bandwidth, and thus, a prudent choice would be to deploy FTTH

wherever feasible.

($20)

($10)

$0

$10

$20

$30

$40

$50

$60

$70

Ne

t P

rese

nt

Val

ue

($

Mill

ion

s)

Household Density

Worst Case Scenario (0% Aerial, 100% SFU)

FTTH Worst FTTM Worst

59

g. Bivariate Factor Exploration of FTTH Feasibility

It can be valuable to determine at what point FTTH becomes feasible with a positive NPV in

different scenarios. The following is an exploration of three particularly important variables:

Population Density, %SFU and %Aerial. The base scenario is a good choice to consider because

it has a density of 2,500 households, representing the average density setting over many areas.

i. Household Density versus %SFU

First, we look at how household density varies with %SFU, and this is taken at our base scenario

but with a completely buried deployment to reflect the worst case scenario. Figure 5.13

illustrates that for the worst case scenario, if there is less than 30% SFU dwelling, FTTH remains

viable, and becomes increasingly viable with more flexibility for accommodating more SFU

dwelling as household density increases. The matrix shown in Table D.1 in the appendix shows

the feasible regions of FTTH and a network planner should avoid regions where the population

is more than 30% SFU based, or less than 3,500 LU/km2, as the worst case scenario.

Figure 5.13: Bivariate exploration of Household Density versus %SFU (completely buried)

-30

-20

-10

0

10

20

30

40

50

60

70

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

NP

V F

TTH

($

Mill

ion

s)

%SFU Dwelling

Household Density Vs. %SFU

5,000 4,000 3,000 2,000 1,000

60

ii. Household Density versus %Aerial

Next we look at how household density varies with %Aerial while keeping %SFU at 100% to

represent, again, the worst case scenario. This relationship is illustrated in Figure 5.14 and the

matrix shown in Table D.2 of the appendix illustrates the feasible regions of FTTH as Household

Density and %Aerial increase at the worst case scenario of a completely SFU based dwelling.

The boundaries to avoid in this situation are those regions that have household densities less

than 2500 and where the deployment is less than 50% aerial. Furthermore, as Household

Density increases, a network can be feasible with decreasing aerial build (conversely increasing

buried build).

Figure 5.14: Bivariate exploration of Household Density versus %Aerial (completely SFU)

-20

-10

0

10

20

30

40

50

60

70

80

90

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

NP

V F

TTH

($

Mill

ion

s)

%Aerial Deployment

Household Density Vs. %Aerial

5,000 4,000 3,000 2,000 1,000

61

iii. SFU versus Aerial in Percentages

Next we look at how %SFU varies with %Aerial in a sparsely populated area with household

density of 1,000 LU/km2, as this will give the worst case scenario. Figure 5.15 illustrates this

relationship and the matrix shown in Table D.3 of the appendix illustrates the feasible regions

of FTTH as %Aerial is varied with %SFU. The boundaries where FTTH becomes infeasible are

those areas where there is greater than 50% SFU living and less than 50% aerial deployment.

Even at this fairly low population density, FTTH NPV remains positive for most scenarios and

only becomes infeasible at very extreme conditions.

Figure 5.15: Bivariate exploration of Household Density versus %Aerial (1,000 LU/km2)

-15

-10

-5

0

5

10

15

20

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

NP

V F

TTH

($

Mill

ion

s)

%SFU Dwelling

% Aerial Vs. %SFU

100% 80% 60% 40% 20%

62

h. Breakdown of Network Costs by Test Scenario

Figure 5.16: Network cost breakdown by scenario area

A breakdown of the network costs by test scenario is shown in Figure 5.16. The figure

demonstrates the proportion of network costs that are attributed to the CO and Feeder, to the

equipment outside the CO, the trenching costs, material fibre and copper costs, and finally the

cost to connect a consumer. It can be seen that the majority of FTTM costs is attributed to

equipment, while for FTTH, it is attributed to Trenching. Another interesting trend is that as an

area becomes less urban, the proportion of the costs becomes increasingly attributed towards

trenching as is expected. However, as an area becomes less urban, greater opportunity to

deploy aerial builds exists.

Su

b-

Ur

ba

n

Are

a

Su

b-

Me

tro

Are

a

Me

tro

Are

a

63

6. Conclusions

In assessing the viability of FTTH versus FTTM when considering an access network overhaul,

many factors need to be taken into consideration that include a high level demand for

bandwidth (BW), the user demographics and geographical details that are used to dimension

the network architecture, building preferences and constraints that ultimately determine the

feasibility of two projects and finally the Telco's strategic company direction that will ultimately

determine what option to take considering the financial parameters.

At the start, BW requirements need to be addressed and this thesis looked at them from the

perspective of how it has grown historically and found that people are shifting from being

casual users to more regular users. As a consequence to that the disparity between offering

subscriptions is narrowing partly because there exist more stratifications in bandwidth offerings

and the rational consumer will pick only the BW that suits their needs. The thesis forecasted

growth in both BW demand, and in novel fibre-optic technology adoption and estimated how it

will perform in a competitive market where cable operators may deploy their own next-

generation access technology called DOCSIS 3.0. Given a growth of 20% within five years of

deployment, the technology matures after about 10 years of deployment and becomes the

more prevalently used access technology, over DOCSIS 3.0, after about 12-15 years. Next a

network planner needs to address user demographics and geographical details to see what the

planned network should look like. The thesis determined that the greater the percentage of

MDU living the more feasible FTTH becomes. For a base scenario representing a sub-metro type

deployment the thesis determined that only at MDU percentages greater than 90% does FTTH

become more feasible over FTTM. However different scenarios will have different cost

structures and thus must be tested separately. Aerial deployment positively affects FTTH very

strongly and should be the preferred alternative whenever possible. As a broad rule of thumb a

network planner should exercise caution when deciding to deploy FTTH over FTTM in situations

where you have: more than 30% SFU deployment, less than 50% Aerial deployment, or less

than 2,500 LU/km2. However, these may be feasible given other parameters being held

constant, and is a decision that needs to be made by the TelCo’s corporate strategy.

64

It is understood that there are many factors that play into whether a particular scenario is

feasible or not, and changing one of them might significantly impact a network’s deployment.

To help facilitate this uncertainty and provide an granular perspective into how feasibility

changes with market, geographic or financial parameters, a software tool was created that can

help network planners make better decisions as to what type of technology they ought to

deploy in a particular area. It allows a network planner to change one or more variables and see

the overall effect on feasibility, which aids in sensitivity planning. Appendix E shows screenshots

of the model’s input pages. The code is also attached in this Appendix.

65

7. Future Work

Almost all studies conducted as of the time of writing this paper have given wireless planning a

cursory look and are only briefly mentioned in regards to their onset in the future. Wireless

technologies are becoming ubiquitous and may well become a substitute service for wireline

Internet access. One needs to look at dimensioning and conducting an economic analysis

between wireline planning versus wireless planning. In the future, market players may emerge

that provide solely wireless services. The benefit of this would be the sheer customer base

considering that smartphones are becoming very popular, and portable tablet devices are

replacing personal desktops, especially for casual users. While this is an important

consideration, wireless technologies will not be the primary bandwidth source especially for

more demanding applications such as IPTV or even entertainment through the computer for at

least the foreseeable future.

Another consideration that warrants a study is the use of powerline technologies to deliver

information. This refers to electricity providers delivering internet services through the home

powerlines. This technology has been around for a while but has not been explored in detail or

implemented within the Canadian context. In the future, these power companies may emerge

as internet providers and as such should be compared with fibre-optic and co-axial cables.

Another consideration that needs to be addressed within wireline planning is understanding at

what point in the geography does FTTM become equivalent to FTTH in terms of bandwidth

reach and delivery capability. This could be useful because even though copper pairs will

eventually run out, FTTM boasts a much more feasible cost structure than FTTH. Exploring this

might be useful for the medium term outlook (over the next 5-10 years) where the increase in

bandwidth demand may still be satisfied over copper loops.

66

8. References

[BANE03W3]

A. Banerjee and M. Sirbu, “Towards Technologically and Competitively Neutral Fiber to the Home (FTTH) Infrastructure, Broadband Services: Business Models and Technologies for Community Networks,” (2003) [Online]; in Proceedings of the TPRC conference,;

http://www.andrew.cmu.edu/user/sirbu/pubs/Banerjee_Sirbu.pdf; [2011, May]

[BCE11W3] BCE reports 2011 second quarter results (2011) [Online]; Bell Canada Enterprises;, http://www.bce.ca/en/news/releases/corp/2011/08/04/76948.html, [2011, May]

[CARD07] M. Cardona, A. Schwarz; “Demand estimation and market definition for broadband services”; (2007)

[CASI10W3] Koen Casier; “Techno-Economic Evaluation of a Next Generation Access Network Deployment in a Competitive Setting”, (2009) *Online+; Faculty of Engineering of the Ghent University, http://ibcn.intec.ugent.be/te/Members/PhD_KoenCasier.pdf, [2010, May]

[CISC10W3] “Cisco Virtual Networking Index: Forecast and Methodology, 2009-2014”; (June 2010) *Online+

http://www.cisco.com/ ; [2011, Jan]

[CRTC10W3] Communications Monitoring Report 2010, (2010) [Online]; CRTC,http://www.crtc.gc.ca/eng/publications/reports/PolicyMonitoring/2010/cmr.htm, [2010, Jan]

[FISH71] J. C. Fisher and R. H. Pry , "A Simple Substitution Model of Technological Change”; Technological Forecasting & Social Change; vol. 3, no. 1 (1971)

[GIIC09] R. Bohn and J. Short; “How Much Information? 2009 Report on American Consumers”; Global Information Industry Center, University of California, San Diego; (2010)

[MARS07] A. Marshall; “White Paper, Future Bandwidth requirements for subscriber and visitor based networks”; Campus Technologies Inc.; (2007)

[MITT01W3]

K. Mittal; “Internet Traffic Growth, Analysis of Trends and Predictions”; (Sept 2001) *Online+; University of Nebraska

http://www.kunalmittal.com/includes/Papers/PredictingInternetTrafficGrowth.pdf; [2011, Feb]

[NIEL98W3] J. Nielsen;” Nielsen’s Law of Internet Bandwidth”; (April 1998) *Online+; http://www.useit.com/alertbox/980405.html; [2011, May]

[NOLL91] A. M. Noll; “Introduction to Telephones and Telephone Traffic”; 2nd

ed. Artech House; (1991)

[ROGE10W3] Rogers Reports Second Quarter 2011 Financial and Operating Results (2011) [Online], Rogers Communications Inc.;http://www.rogers.com/cms/pdf/en/IR/QuarterlyReport/2011-Q2_Results-Release.pdf, [2011, June]

[STAT05W3] Canadian Internet Use Survey (2005) [Online]; Statistics Canada; http://sda.chass.utoronto.ca/sdaweb/html/media.htm; [2009, Sept]

[STAT06W3] 2006 Community Profiles, 2006 Census, (March 2007) [Online] Statistics Canada

<http://www12.statcan.ca/english/census06/data/profiles/community/index.cfm?Lang=E > [2008, Nov 23]

[STAT07W3] Canadian Internet Use Survey (2007) [Online]; Statistics Canada; http://sda.chass.utoronto.ca/sdaweb/html/media.htm; [2009, Sept]

[THOM01] C. Thompson; “Supply and Demand Analysis in Convergent Networks”; Sloan School of Management, Cambridge, MA; (2001)

[WELD07] M. Weldon, F. Zane; “The Economics of Fiber to the Home Revisited”; Bell Labs Technical Journal (2003)

[WIKI10W3a] Wikipedia contributors; “Digital Subscriber Line Access Multiplexer”; (May, 2011) [Online]; Wikipedia; www.wikipedia.org; [2011, May]

[WIKI10W3b] Wikipedia contributors; “Diffusion of Innovations”; (May 2011) *Online+; Wikipedia; www.wikipedia.org; [2011, May]

[WORL10W3] Various internet-based data [Online]; The World Bank; http://data.worldbank.org/; [2011, Jan]

i

Appendix A: Calculating average user bitrates using qualitative data

Step Explanation of Procedure

1

Bitrates were obtained for each Internet based activity in the CIUS survey [STAT07W3,

[STAT05W3]: Browsing: 500 kbps, Email: 500 kbps, IM: 0.25 kbps, Games: 85 kbps, Music: 128 kbps, Software: 1000 kbps, Radio: 128 kbps, TV: 384 kbps, Movies: 2000 kbps, VOIP: 64 kbps

2 Respondents also indicated how long they spend in a particular week on the Internet. It was assumed they spend an equal amount of time, for the time they spend online in a week, on each activity they said they engage in.

3 If a respondent answered “Yes” to a particular activity, it was assumed that they took part in that activity and the number of hours were allotted evenly per activity for any given week.

4

Using bitrates, the usage (in Mbps) was estimated for each user by multiplying the bitrates per activity they engaged in with the number of hours they were engaged in that particular activity. All activity usages were summed up for each user’s bandwidth usage in a week and converted to Mbps.

5

This gave a distribution of activity usage for the survey population. Even though this was evened out without consideration for peak usage times or weighting different activities by the number of hours, it gave a good idea of how many users are engaged in heavy versus light Internet usage.

ii

Appendix B: Forecasting adoption using Fisher-Pry approximations

Step Explanation of Procedure Equation

1

Sigmoid Function basic definition P(t) = Penetration variable e: Euler’s number = 2.7182818284… t: time variable

2

The Fisher-Pry model

= slope of curve (pace of adoption) T0 = Inflection point on curve at 50% of total adoption (m) m = market potential of adoption (usually taken as 1)

3

and To need to be determined. Create two finite differentials that will yield two equations in two unknowns. Let each function be called f1 and f2.

4 Rearrange both the equations

5 Subtract both the equations and simplify

6 Isolate for To

=

7 Define values for f1, f2 and t1, t2 so that t1 is the time when the penetration rate is f1 t2 is the time when the penetration rate is f2

iii

Appendix C: Base Test Conditions and Sensitivity Results

Baseline Values

FTTH (% change in NPV)

FTTM (% change in NPV)

Parameter variation

-20% +20% -20% +20%

Geographic Variables

% Single Family Dwelling 50% 6.5% -6.1% -0.5% 0.5%

Living Unit Density 2500 -25.5% 25.0% -20.0% 20.0%

Floors/ MDU 20 -3.0% 1.4% 0.4% -1.8%

LU/ (MDU Floor) 16 -3.3% 3.3% 0.4% 0.9%

Build Variables

SFU Rollout Speed 95% by 5 years -5.7% 5.1% -2.3% 2.6%

MDU Rollout Speed 95% by 5 years -1.1% 0.6% -1.8% 0.3%

% Aerial Build 50 % -11.7% 10.9% -0.4% 0.4%

Cost Variables

Equipment First Costs ($) Multiple Values 6.4% -6.5% 6.1% -6.4%

Boring/Trenching Cost ($/m)

$110, $55 9.9% -10.4% 0.5% -0.5%

Rate of Inflation (%) 2.25% 0.4% -0.4% 0.3% -0.3%

Market Variables

Market Take-up 20 % by 5 years -3.4% 2.1% -3.3% 2.2%

Triple Play CAGR (%/yr) 2% -0.6% 0.6% -0.5% 0.5%

BW Tariff ($) 35-90 -22.7% 22.0% -18.3% 17.7%

Voice Tariff ($) 25 -5.1% 5.1% -4.1% 4.1%

Video Tariff ($) 60 -12.2% 12.2% -10.0% 9.8%

Table C.1

iv

Pessimistic Values Baseline Values Optimistic Values

% Single Family Dwelling 90% 50% 10%

% Aerial Build 10% 50% 90%

Population Density 1000 2500

4000

Table C.2

v

Appendix D: Bivariate Analysis of FTTH Feasibility

% SFU

HH

De

nsi

ty

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

500 2.0 -0.9 -3.5 -5.8 -7.8 -9.7 -11.4 -12.5 -14.6 -15.5

1,000 8.7 4.4 0.5 -2.8 -5.8 -8.0 -11.1 -13.0 -15.2 -16.9

1,500 15.7 10.0 5.1 1.5 -2.3 -5.7 -8.4 -11.5 -14.4 -16.1

2,000 22.8 17.1 11.4 6.4 2.5 -1.6 -5.4 -8.5 -12.0 -14.3

2,500 29.5 23.3 17.5 11.9 7.3 3.1 -1.3 -4.9 -8.9 -12.2

3,000 36.8 30.0 23.3 17.6 12.4 7.2 2.7 -1.4 -5.9 -9.1

3,500 44.1 36.4 30.3 23.5 18.4 12.6 7.6 3.0 -2.0 -5.7

4,000 51.4 43.7 36.4 29.6 23.9 18.2 12.2 7.1 2.2 -2.0

4,500 58.4 50.3 43.0 35.8 29.1 22.9 17.0 11.4 6.0 1.5

5,000 65.7 57.2 50.1 42.7 35.0 28.8 22.5 17.0 10.7 5.7

Table D.1: Bivariate exploration of Household Density versus %SFU (completely buried)

% Aerial

HH

De

nsi

ty

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

500 -13.0 -10.6 -8.2 -5.8 -3.4 -1.0 1.3 3.7 5.8 7.3

1,000 -13.4 -9.9 -6.4 -3.0 0.5 3.9 7.3 10.5 13.0 15.1

1,500 -11.8 -7.4 -3.2 1.1 5.4 9.7 13.8 17.5 20.3 22.9

2,000 -9.2 -4.1 0.9 5.9 10.9 15.9 20.5 24.5 27.7 30.6

2,500 -6.5 -0.8 4.9 10.6 16.2 21.8 26.9 31.2 34.7 38.1

3,000 -2.8 3.5 9.8 16.1 22.3 28.4 33.8 38.3 42.2 45.9

3,500 1.2 8.1 15.0 21.8 28.6 35.0 40.7 45.5 49.6 53.7

4,000 5.5 12.9 20.4 27.7 35.0 41.7 47.7 52.7 57.1 61.5

4,500 9.4 17.4 25.4 33.2 40.9 48.0 54.3 59.5 64.3 69.0

5,000 14.1 22.6 31.0 39.4 47.4 54.9 61.3 66.7 71.8 76.8

Table D.2: Bivariate exploration of Household Density versus %Aerial (completely SFU)

% SFU

% A

eria

l

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

10% 9.5 5.8 2.3 -0.8 -3.4 -5.4 -8.2 -9.9 -11.9 -13.4

20% 10.3 7.1 4.0 1.3 -1.1 -2.8 -5.4 -6.8 -8.6 -9.9

30% 11.1 8.5 5.8 3.4 1.2 -0.2 -2.5 -3.7 -5.4 -6.4

40% 11.8 9.7 7.5 5.4 3.6 2.4 0.3 -0.7 -2.1 -3.0

50% 12.4 10.9 9.2 7.5 5.9 5.0 3.1 2.3 1.1 0.5

60% 13.1 12.0 10.7 9.4 8.2 7.5 5.9 5.4 4.4 3.9

70% 13.7 13.0 12.0 11.2 10.3 10.0 8.7 8.3 7.6 7.3

80% 14.2 13.8 13.3 12.7 12.2 12.0 11.1 11.0 10.5 10.5

90% 14.7 14.7 14.3 14.0 13.7 13.7 13.1 13.2 13.0 13.0

100% 15.3 15.5 15.3 15.2 15.1 15.3 14.8 15.0 14.9 15.1

Table D.3: Bivariate exploration of Household Density versus %Aerial (1,000 LU/km2)

vi

Appendix E: Modelling Tool Dashboard

Demographic Information Population Information 1 2

Year 2001 2010

Area Population (#) 18,050 20,000

Internet Penetration (%) 45% 70%

Avg. LU Size (ppl/household) 2.0

SFU (%) 100.0%

Avg. Floors/MDU (#) 20

Avg. LU/MDUFloor (#) 16

Area (km2) 10.0

Rollout Deployment 1 2

Rollout Method (choose) Batch Cycle Build

Outlook Date 2025

SFU Build Start, Stop (year) 2010 2015

Desired SFU Passrate (%) 95%

MDU Build Start, Stop (year) 2010 2015

Desired MDU Passrate (%) 95%

Aerial (%) 0.0%

New or Over-build (choose) Brownfield

E.1: Model Input – Demographic Information

The user indicates their target area’s living demographics and geographic characteristics. These help in determining the households

that will use the Internet over the course of the project, and determine the overall network structure.

vii

Usage Function Model Bandwidth Usage Growth Rates 1 2

Year 2006 2009

upto 1.4 Mbps (%) 25% 13%

1.5 to 4 Mbps (%) 15% 25%

5 to 9 Mbps (%) 55% 42%

10 to 15 Mbps (%) 5% 19%

16 to 100 Mbps (%) 0% 1%

Very High End Users (as % of High-End Users: 16-100Mbps) 30%

Technology Adoption Growth Rates 1 2

Year 2000 2009

Dial-Up (%) 69% 5%

xDSL (%) 9% 39%

Cable (%) 22% 55%

FTTx Service 1 2

Start, Forecast Dates (year) 2011 2016

Estimated Growth Parameters 1% 20%

E.2: Model Input – Usage Information

The user indicates historical adoption data used to forecast future technology adoption and bandwidth requirements.

viii

Equipment Capacity Model FTTH Build

G-PON cards/7342 Card (#) 18

G-PON ports/G-PON card (#) 4

Customers/G-PON port (#) 32

Preferred CSP Capacity (#) 576

Max Utilisation on CSP (%) 85%

Coupler Split (#) 32

SFU/Terminal (#) 8

Terminals/Distribution (#) 8

Terminal Pre-Stubbed? Yes

FTTM Build

Gig-E slots/ERAM (#) 16

ports(LU)/VSEM (#) 48

VSEM/OPI (#) 14

E.3: Model Input – Equipment Carrying Capacity

The user indicates the capacity that particular equipment can carry and this helps in dimensioning the network.

ix

E.4: Model Input – Financial Model

The user indicates general costs for equipment that is used to estimate the Capital Expenditure (CapEx)

Financial ModelMaterial and Distribution Cost 1 ($) 2 ($)

Digging (Boring/Trenching, Micro-Trenching) (/m) 110 55

Drop (Buried, Aerial) 423 139

Fibre Cable (/m) (Buried, Aerial) 3

Fibre Placing (/m) (Buried, Aerial) 3 4

Spl ice Enclosure (Large, Small) 800 150

Spl ice (Mechanical, Fusion) 25 5

Copper Cable (/m) 10

Copper Placing (/m) (Buried, Aerial) 24

Feeder Materia l + Insta l lation 20,000

Feeder Infrastructure 10,000

FTTH Equipment Equipment ($) Labour ($) Maint Cost (%) MTBR (months) MTTR (hrs) Life (yrs)

7342 Card 22,950 14,000 5% 12 10 10

GPON Card + Insta l lation 2% 12 10 10

CSP 29,600 16,000 5% 6 5 10

Coupler + Insta l lation 2% 12 1 7

GLB 700 800 2% 12 1 7

Buried Terminals 182 600 2% 6 1 7

Pedestals 70 100 2% 6 1 7

Aeria l Terminals 182 40 2% 3 1 5

Tethers + Insta l lation 2% 3 1 5

CPE 371 217

FTTM Equipment Equipment ($) Labour ($) Maint Cost (%) MTBR (months) MTTR (hrs) Life (yrs)

ERAM Bundle (16 Gig-E s lot) 45,336 13,000 5% 6 10 10

VSEM-C 8,294 13,000 5% 12 5 10

Rhino Cabinet 916 4,000 2% 12 5 7

RPN 28,680 12,500 5% 6 5 7

CPE 330 25

Power Consumption (W/subs) 1

7,936

1,200

287

x

E.4: Model Input – Global Parameters

The user indicates general global parameters that are fed into the financial model and generate an NPV for the project.

Global Parameters

Discount Rate (%)

Inflation Rate (%)

Corporate Tax Rate (%)

Capita l Cost Al lowance (%)

Cost of electrici ty (at time of deployment) ($/kWh)

Electrici ty Cost CAGR (%)

Network Insurance (% of Network Value)

Maintenance Labour Cost ($/hr)

Customer Care ($/subs)

Triple Play Subs (at time of service s tart) (% of total)

Triple Play CAGR (%)

Discount for Triple Play Subs (%)

New User One-Year Discount ($/month)

Data (1.4 Mbps) Tari ff ($/subs)

Data (4 Mbps) Tari ff ($/subs)

Data (9 Mbps) Tari ff ($/subs)

Data (15 Mbps) Tari ff ($/subs)

Data (30 Mbps) Tari ff ($/subs)

Data (30+ Mbps) Tari ff ($/subs)

Voice Tari ff ($/subs)

Base Video Tari ff ($/subs)

Video Extra Content Fee ($/instance)

Avg. Extra Content (instances per month/subs)

30%

2

65

90

60

5

73

25

50

34%

15%

5

40

0.0750

5%

35

48

10

5.1%

2%

30%

9%

2%

xi

Appendix F: Model Listing

Geographic Information

Internet Penetration

Overall Population

Internet Population

Internet Households

Internet SFUs

Internet MDUs

% Population that is Triple

Play

Buried

Aerial

2000 42% 44,583 18,814 9,407 6,585 2,822 27%

2001 45% 45,125 20,306 10,153 7,107 3,046 28%

2002 62% 45,667 28,085 14,043 9,830 4,213 28%

2003 64% 46,208 29,527 14,764 10,334 4,429 29%

2004 66% 46,750 30,808 15,404 10,783 4,621 30%

2005 68% 47,292 32,111 16,056 11,239 4,817 30%

2006 70% 47,833 33,627 16,813 11,769 5,044 31%

2007 73% 48,375 35,217 17,609 12,326 5,283 31%

2008 75% 48,917 36,834 18,417 12,892 5,525 32%

2009 78% 49,458 38,429 19,215 13,450 5,764 33%

2010 84% 50,000 42,183 21,091 14,764 6,327 34%

2011 87% 50,542 43,939 21,969 15,379 6,591 34%

2012 89% 51,083 45,534 22,767 15,937 6,830 35%

2013 91% 51,625 46,982 23,491 16,444 7,047 35%

2014 93% 52,167 48,296 24,148 16,904 7,244 36%

2015 94% 52,708 49,492 24,746 17,322 7,424 37%

2016 95% 53,250 50,584 25,292 17,705 7,588 38%

2017 96% 53,792 51,587 25,794 18,056 7,738 38%

2018 97% 54,333 52,514 26,257 18,380 7,877 39%

2019 97% 54,875 53,375 26,688 18,681 8,006 40%

2020 98% 55,417 54,182 27,091 18,964 8,127 41%

xii

Market Internet Bandwidth Usage Growth

(upto 1.4 Mbps)

(1.5 to 4 Mbps)

(5 to 9 Mbps)

(10 to 15 Mbps)

(16 to 50Mbps)

(51 to 100Mbps)

4,046 306 5,038 17 1 0

4,155 428 5,536 32 1 1

5,401 770 7,789 78 3 1

5,257 1,053 8,302 144 5 2

4,987 1,424 8,718 262 10 4

4,623 1,904 9,032 472 17 7

4,193 2,516 9,224 839 29 13

3,680 3,242 9,171 1,443 50 22

3,102 4,033 8,793 2,369 84 36

2,498 4,804 8,070 3,651 135 58

2,029 5,768 7,476 5,506 219 94

1,512 6,339 6,352 7,298 328 141

1,100 6,769 5,260 8,954 480 206

793 7,117 4,308 10,284 693 297

572 7,434 3,522 11,203 992 425

415 7,729 2,884 11,714 1,404 602

301 7,985 2,360 11,868 1,944 833

219 8,178 1,928 11,747 2,605 1,116

159 8,303 1,571 11,448 3,343 1,433

116 8,378 1,280 11,071 4,090 1,753

85 8,436 1,046 10,703 4,774 2,046

xiii

Market Technology Growth Rates Telco

Uptake

DialUp ADSL ADSL2+ VDSL2 FTTx Docsis 1,2 Docsis 3 SFULU MDULU

2,791 1,921

4,695

0 0

2,595 2,292

5,267

0 0

2,942 3,514

7,587

0 0

2,381 4,091

8,292

0 0

1,775 4,694

8,935

0 0

1,219 5,322

9,515

0 0

774 5,981

0

10,058 0 0

454

6,637

0

10,517 0 0

248

7,275

0

10,894 0 0

126

7,887

0

11,201 0 0

64

8,830 108

12,089 6,301 2,701

30

9,365 197

12,379 6,714 2,877

13

9,774 356

12,624 7,100 3,043

6

10,000 643

12,842 7,455 3,195

3

9,954 1,149

13,043 7,774 3,332

1

9,507 2,009

13,229 8,062 3,455

1

8,526 3,374

13,391 8,331 3,570

0

6,962 5,313

13,518 8,593 3,683

0

4,985 7,664

13,608 8,854 3,795

0

2,996 10,013

13,678 9,107 3,903

0

1,395 11,943

13,752 9,337 4,002

xiv

Telco Customer Segmentation Player Share

Deployment

0 to 1.4 1.5 to 4 5 to 9 10 to 15 16 to 50 51 to

100 Telco Cableco

Req'd SFU Build/annum

Req'd MDU Build

70.0% 30.0%

$ 35

$ 40

$ 50

$ 65

$ 73

$ 90

2,026 153 2,523 9 0 0 4,712 4,695 0 0

2,000 206 2,664 16 0 1 4,886 5,267 0 0

2,483 354 3,581 36 1 1 6,456 7,587 0 0

2,304 462 3,639 63 1 2 6,472 8,292 0 0

2,094 598 3,661 110 2 4 6,470 8,935 0 0

1,883 776 3,679 192 3 7 6,540 9,515 0 0

1,685 1,011 3,706 337 5 12 6,755 10,058 0 0

1,482 1,306 3,693 581 9 20 7,091 10,517 0 0

1,267 1,648 3,592 968 15 34 7,523 10,894 0 0

1,042 2,003 3,366 1,523 24 56 8,013 11,201 0 0

866 2,462 3,191 2,350 40 93 9,002 12,089 3,835 6

660 2,768 2,773 3,186 61 143 9,591 12,379 3,835 6

490 3,016 2,343 3,989 92 214 10,143 12,624 3,835 6

359 3,227 1,953 4,662 135 314 10,650 12,842 3,835 6

263 3,419 1,620 5,152 195 456 11,106 13,043 3,835 6

193 3,597 1,342 5,452 280 653 11,517 13,229 3,835 6

142 3,757 1,111 5,584 392 915 11,901 13,391 0 0

104 3,892 917 5,591 531 1,240 12,276 13,518 0 0

77 4,000 757 5,515 690 1,611 12,649 13,608 0 0

57 4,084 624 5,397 854 1,994 13,009 13,678 0 0

42 4,154 515 5,270 1,007 2,351 13,339 13,752 0 0

xv

FTTH CO FTTH Equipment

#7342 Cards/annum

#GPON Cards/annum

CO-CSP Cnxs/annum

#CSPs/annum #

Couplers/annum Splitter

splices/annum DSA

splices/annum #GLB/annum

$ 36,950

$ 7,680 $ 30,000 $ 45,600 $ 1,200 $ 825 $ 25

$ 1,500

$ 150 $ 25

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

1 1 14 14 252 90 479 44

0 1 14 14 252 90 479 44

0 1 14 14 252 90 479 44

0 3 14 14 252 90 479 44

0 3 14 14 252 90 479 44

0 7 14 14 252 90 479 44

1 11 0 0 0 0 0 0

1 15 0 0 0 0 0 0

1 18 0 0 0 0 0 0

1 19 0 0 0 0 0 0

1 15 0 0 0 0 0 0

xvi

FTTH Dist FTTH Drop

# Terminals/annum

#Pedestals/annum #Tethers/annum

Trench length required (CSP-

splice) + (splice-home)

(m)/annum

Buried Dist Fibre length

required (m)/annum

Aerial Dist Fibre length

required (m)/annum

# Drops/annum

#CPE Installations/annum

$ 782 $ 170 $ 110

$ 6

$ 423 $ 588

$ 222 $ 287 $ 55

$ 7

$ 139

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

479 288 192 68,195 6,973 4,649 6,310 108

479 288 192 68,195 6,973 4,649 413 88

479 288 192 68,195 6,973 4,649 387 160

479 288 192 68,195 6,973 4,649 355 287

479 288 192 68,195 6,973 4,649 320 506

479 288 192 68,195 6,973 4,649 289 860

0 0 0 0 0 0 269 1,365

0 0 0 0 0 0 263 1,939

0 0 0 0 0 0 262 2,351

0 0 0 0 0 0 253 2,349

0 0 0 0 0 0 231 1,930

xvii

FTTM CO FTTM Equipment FTTM Dist

#ERAM cards CO-OPI

Cnxs # OPI (RPN)

powering cost

$/subs #VSEMs

# Rhino Cabinet

Trench length required (OPI-

VSEM)+ (VSEM-Home) (m)

Buried Dist Fibre length

required (m)

Buried Dist Copper length

required (m)

$ 58,336

$ 30,000

$ 41,180

$

21,294 $

4,916

$ 110

$ 6

$ 34

$ 55

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

0 0 0 0.0000 0 0 0 0 0

1 1 1 0.6570 120 120 8,025 8,025 0

0 1 1 0.6905 120 120 8,025 8,025 0

0 1 1 0.7257 120 120 8,025 8,025 0

0 1 1 0.7627 120 120 8,025 8,025 0

1 1 1 0.8016 120 120 8,025 8,025 0

1 1 1 0.8425 120 120 8,025 8,025 0

2 1 1 0.8855 0 0 0 0 0

2 1 1 0.9306 0 0 0 0 0

3 1 1 0.9781 0 0 0 0 0

4 1 1 1.0280 0 0 0 0 0

2 1 1 1.0804 0 0 0 0 0

xviii

FTTM Drop Aerial Dist

Fibre length required (m)

Aerial Dist Copper length

required (m) # Drops

#CPE Installations

$ 423 $

355 $ 7

$ 34

$ 139

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

5,350 0 0 108

5,350 0 0 88

5,350 0 0 160

5,350 0 0 287

5,350 0 0 506

5,350 0 0 860

0 0 0 1,365

0 0 0 1,939

0 0 0 2,351

0 0 0 2,349

0 0 0 1,930

xix

FTTH Financial

Real Capital Injection

Real Cumulative Value

Real Revenues Real OPEX Before Tax Real

Cashflow Before Tax Actual

CashFlow Asset Class

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ 7,326,355 $ 7,326,355 $ 6,541,082 $ 488,300 -$ 1,273,573 -$ 1,273,573 $ -

$ 5,453,106 $ 12,779,461 $ 7,114,687 $ 797,655 $ 863,926 $ 883,365 $ 3,663,177

$ 5,487,333 $ 18,266,793 $ 7,668,865 $ 1,108,363 $ 1,073,170 $ 1,122,006 $ 8,953,954

$ 5,567,305 $ 23,834,098 $ 8,184,836 $ 1,422,921 $ 1,194,610 $ 1,277,074 $ 11,737,987

$ 5,685,198 $ 29,519,296 $ 8,657,184 $ 1,742,872 $ 1,229,114 $ 1,343,524 $ 13,743,910

$ 5,914,357 $ 35,433,653 $ 9,094,117 $ 2,074,473 $ 1,105,287 $ 1,235,355 $ 15,246,988

$ 1,007,243 $ 36,440,895 $ 9,514,099 $ 2,131,564 $ 6,375,293 $ 7,285,846 $ 16,472,669

$ 1,373,695 $ 37,814,591 $ 9,936,266 $ 2,207,526 $ 6,355,045 $ 7,426,118 $ 14,991,668

$ 1,638,288 $ 39,452,879 $ 10,365,480 $ 2,297,180 $ 6,430,012 $ 7,682,778 $ 11,684,637

$ 1,642,525 $ 41,095,404 $ 10,786,008 $ 2,387,074 $ 6,756,410 $ 8,254,406 $ 9,685,238

$ 1,358,473 $ 42,453,877 $ 11,173,830 $ 2,461,819 $ 7,353,538 $ 9,186,065 $ 8,420,073

xx

Capital Cost Allowance

Taxable Income Taxes After Tax Actual

Cashflow After Tax Real

Cashflow FTTH After Tax Real

Cashflow PW FTTH NPV

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - -$ 1,273,573 -$ 1,273,573 -$ 1,273,573 -$ 1,273,573

$ 1,098,953 $ - $ - $ 883,365 $ 863,926 $ 792,593 -$ 480,980

$ 2,686,186 $ - $ - $ 1,122,006 $ 1,073,170 $ 903,266 $ 422,286

$ 3,521,396 $ - $ - $ 1,277,074 $ 1,194,610 $ 922,458 $ 1,344,744

$ 4,123,173 $ - $ - $ 1,343,524 $ 1,229,114 $ 870,735 $ 2,215,480

$ 4,574,097 $ - $ - $ 1,235,355 $ 1,105,287 $ 718,361 $ 2,933,840

$ 4,941,801 $ 2,344,046 $ 703,214 $ 6,582,633 $ 5,759,963 $ 3,434,478 $ 6,368,318

$ 4,497,500 $ 2,928,618 $ 878,585 $ 6,547,533 $ 5,603,179 $ 3,065,131 $ 9,433,449

$ 3,505,391 $ 4,177,387 $ 1,253,216 $ 6,429,562 $ 5,381,147 $ 2,700,616 $ 12,134,065

$ 2,905,571 $ 5,348,835 $ 1,604,650 $ 6,649,755 $ 5,442,969 $ 2,506,094 $ 14,640,159

$ 2,526,022 $ 6,660,043 $ 1,998,013 $ 7,188,052 $ 5,754,108 $ 2,430,598 $ 17,070,757

xxi

FTTM Financial

Real Capital Injection

Real Cumulative Value

Real Revenues Real OPEX Before Tax Real

Cashflow Before Tax Actual

CashFlow Asset Class

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ 3,810,104 $ 3,810,104 $ 6,541,082 $ 342,111 $ 2,388,868 $ 2,388,868 $ -

$ 3,744,729 $ 7,554,833 $ 7,114,687 $ 589,344 $ 2,780,614 $ 2,843,178 $ 1,905,052

$ 3,770,090 $ 11,324,923 $ 7,668,865 $ 837,518 $ 3,061,257 $ 3,200,564 $ 5,110,953

$ 3,815,199 $ 15,140,121 $ 8,184,836 $ 1,087,504 $ 3,282,133 $ 3,508,700 $ 7,335,077

$ 3,951,273 $ 19,091,394 $ 8,657,184 $ 1,346,078 $ 3,359,833 $ 3,672,578 $ 8,927,198

$ 4,076,883 $ 23,168,277 $ 9,094,117 $ 1,610,510 $ 3,406,724 $ 3,807,619 $ 10,132,274

$ 642,442 $ 23,810,719 $ 9,514,099 $ 1,653,269 $ 7,218,388 $ 8,249,358 $ 11,106,670

$ 846,280 $ 24,656,999 $ 9,936,266 $ 1,706,185 $ 7,383,801 $ 8,628,260 $ 10,134,331

$ 1,050,630 $ 25,707,629 $ 10,365,480 $ 1,771,633 $ 7,543,217 $ 9,012,871 $ 7,838,393

$ 1,108,593 $ 26,816,222 $ 10,786,008 $ 1,842,170 $ 7,835,246 $ 9,572,436 $ 6,435,330

$ 843,055 $ 27,659,276 $ 11,173,830 $ 1,894,620 $ 8,436,155 $ 10,538,474 $ 5,584,342

xxii

Capital Cost Allowance

Taxable Income Taxes After Tax Actual

Cashflow After Tax Real

Cashflow FTTM After Tax

Real Cashflow PW FTTM NPV

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ - $ - $ - $ - $ - $ -

$ - $ 2,388,868 $ 716,660 $ 1,672,207 $ 1,672,207 $ 1,672,207 $ 1,672,207

$ 571,516 $ 2,271,662 $ 681,499 $ 2,161,679 $ 2,114,111 $ 1,939,552 $ 3,611,759

$ 1,533,286 $ 1,667,278 $ 500,183 $ 2,700,380 $ 2,582,845 $ 2,173,929 $ 5,785,688

$ 2,200,523 $ 1,308,177 $ 392,453 $ 3,116,247 $ 2,915,022 $ 2,250,932 $ 8,036,620

$ 2,678,159 $ 994,418 $ 298,326 $ 3,374,252 $ 3,086,912 $ 2,186,846 $ 10,223,466

$ 3,039,682 $ 767,937 $ 230,381 $ 3,577,238 $ 3,200,599 $ 2,080,170 $ 12,303,636

$ 3,332,001 $ 4,917,357 $ 1,475,207 $ 6,774,151 $ 5,927,546 $ 3,534,402 $ 15,838,038

$ 3,040,299 $ 5,587,960 $ 1,676,388 $ 6,951,871 $ 5,949,199 $ 3,254,416 $ 19,092,454

$ 2,351,518 $ 6,661,353 $ 1,998,406 $ 7,014,465 $ 5,870,675 $ 2,946,294 $ 22,038,748

$ 1,930,599 $ 7,641,837 $ 2,292,551 $ 7,279,885 $ 5,958,743 $ 2,743,571 $ 24,782,318

$ 1,675,303 $ 8,863,171 $ 2,658,951 $ 7,879,523 $ 6,307,638 $ 2,664,414 $ 27,446,733