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)
($5)
$0
$5
$10
$15
Ne
t P
rese
nt
Val
ue
($
Mill
ion
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)
($15)
($10)
($5)
$0
$5
$10
$15
Ne
t P
rese
nt
Val
ue
($
Mill
ion
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