[30/1/02 -02] The LRIC model of UK mobile network costs, developed for Oftel by Analysys, September...

[30/1/02 -02]

The LRIC model of UK mobile network costs, developed for Oftel by Analysys, September 2001

A Manual for the Oftel model

Working paper for Oftel, 29 January 2001

2

Executive summary

Background to the Oftel model

Introduction to LRIC modelling

Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:

Services and increments

Economic depreciation

3

Executive summary

This working paper presents a comprehensive description of the long-run incremental cost (LRIC) model of UK mobile network costs, developed for the UK regulator, Oftel, by Analysys, during 2001

The model was made available by Oftel in conjunction with its statement on mobile termination in the UK, and can be downloaded from the Analysys Web site. Although this model is freely available, it is copyright of the UK Crown, and should not be used for any purpose other than the review into mobile termination in the UK

this document does not contain details of the Excel-related mechanics of the model

instead, it provides details of the theory that underlies the model, and details of the nature of calculations employed (but not their Excel implementation)

Executive summary

4

Related documents

The LRIC model of UK mobile network costs, developed for Oftel by Analysys

download from www.analysys.com

Oftel statements related to the review of mobile termination in the UK

download from www.oftel.gov.uk

Executive summary

5

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



6

LRIC modelling is a method of calculating costs which employs a specific set of costing principles

Long-run incremental cost modelling relates to:

a consideration of costs over the economic lifetime of assets (long-run)

the attribution of costs to specific services

Estimates the economic costs of installing, maintaining and operating a mobile network

Estimates the cost to a new entrant of providing the same service as the existing network operator

Identifies the structure of costs – how they vary with the level of demand and range of service offerings

Advantages are:

a good predictor of volume/cost movements

represents an economically rational approach to pricing cost-based services over time

of increasing interest to regulators, especially for validation of interconnect arrangements, because cost-orientated

of paramount interest to new entrants

forward-looking

Introduction

7

LRIC cost modelling is supported by major regulators and other organisations

Supported by the FCC, EC and IRG (Independent Regulators Group) for costing mobile termination

Applied by OFTEL in its current proposals for the regulation of mobile termination rates in the UK

Introduction

8

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



9

Oftel was required to consider a range of issues when setting interconnect prices

Prices

Pricing methode.g. LRIC, LRIC+ Costs

Other factorse.g. externalities

Costing methode.g. forward-looking

economic costs

Datae.g. unit

input costs

Assumptionse.g. demand

forecasts

Background

10

The models developed for Oftel by Analysys only derived the costs of mobile termination, and enabled a number of mark-up regimes to be applied

Prices

Pricing methode.g. LRIC, LRIC+ Costs

Other factorse.g. externalities

Costing methode.g. forward-looking

economic costs

Datae.g. unit

input costs

Assumptionse.g. demand

forecasts

Illustration ofalternativesnot policy

Background

11

Analysys constructed the 1998 and 2001 LRIC models for Oftel

In 1998, Analysys constructed a bottom-up LRIC model for Oftel, to assist Oftel in its 1998 review of the price of calls to mobiles

This model calculated the costs of :

a reasonably efficient new entrant

in a (hypothetically) fully contestable market

with the demand parameters of either Vodafone (GSM 900) or Orange (GSM 1800)

for (year average of) the financial year 98/99

In 2001, Analysys began the process of updating this model to reflect the needs of Oftel for the next review of the price of calls to mobiles (completed September 2001)

Background

12

A number of areas of the model were highlighted for improvement

Enable the model to calculate costs for the years 2000/01–2005/06

Improve specific areas of the model:

update and refine data and assumptions in the model, with the co-operation of the UK operators

review methodological issues, with input from operators, to improve the accuracy and suitability of the network deployment algorithms

make the model algorithms and calculations more explicit

Update the model to reflect the current and expected development of the mobile market:

current: SMS, emerging HSCSD and GPRS services, increased expectations of the “quality of mobile network coverage”

expected: increased take-up of data services (HSCSD, GPRS and latterly UMTS), eventual decline in SMS in favour of packet based messaging services, simultaneous operation of UMTS voice and data networks by the four UK operators

Background

13

In order to calculate costs out to 2005/06, forecasts of the UK mobile market and associated network deployments were required

It was important to establish consistent forecasts, calculations and model algorithms e.g.

the allowance for growth assumed in deploying the network was consistent with the growth in market demand

that the nature of the (hypothetical) competitive market was correctly and consistently represented

Taking into account the (2000/01 real terms) model results, Oftel derived P2000/01, P2005/06

and X

these parameters (P = price; X = percentage price decline) were important in setting the regulated price cap

The UK mobile market was forecasted in terms of:

subscribers

minutes of use (incoming, outgoing, on-net)

data service take-up (subscribers, technologies, megabytes of use)

Network deployment forecasts required time series for:

demand drivers (e.g. busy hour traffic proportions)

network design parameters (e.g. traffic by cell type)

equipment unit costs

Background

14

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



15

The costs calculated by the model developed by Analysys represent a unique implementation of LRIC theory and regulatory policy …

The model developed by Analysys in 1998 calculated the long run costs of:

a reasonably efficient new entrant

in a (hypothetically) fully contestable market*

with the demand parameters of either Vodafone (GSM 900) or Orange (GSM 1800)

for (year average of) the financial year 98/99

However, the model developed by Analysys in 2001 calculated the critically different long run costs of:

a reasonably efficient operator that launched service in 1992/93 (corresponding with the launch of GSM in the UK)*

in a market with the assumed level of contestability*

with 25% share of the total mobile market from 1992/93 to 2002/03, declining to 20% share of the total mobile market by the end of 2009 (corresponding with the entry of the fifth player to the UK market)

for the (year average) of financial years 2000/01 to 2005/06

The model

* See later section on Economic Depreciation for definition of these terms and justification of approach adopted

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… but the cost modelling is still based on sound techno-economic principles

Bottom-up

A ‘scorched-node’ approach was adopted, so that the network design reflects the actual number of base stations and switch sites currently deployed

a scorched-node deployment is one that evolves over time and is constrained by the history of deployments

conversely, a scorched-earth deployment is one which has no historic constraints, and can be deployed in an optimal fashion

Modern technologies (for example, those currently being deployed) are used throughout (MEAs; modern equivalent assets)

Sufficient capacity to meet present (coverage and demand) requirements is provided; plus an allowance for reasonable future growth, but no more

Incorporating (a variant of) economic depreciation for calculating economic costs

Deriving the long run average incremental costs:

average costs are calculated rather than marginal

The model

17

Background to the scorched node approach

Networks develop over time in response to changes in demand (or forecast demand)

As a result of this evolutionary development, networks are rarely truly optimal for current (or currently forecast) market conditions

The location of network nodes is dictated to at least a degree by the availability of suitable sites on the ground

Such sites are rarely in the ideal location from a theoretical perspective – another reason for networks being less than optimal

Radio network design is a complex process, involving a very large number factors and design parameters, not all of which are measurable in advance

To accurately capture every nuance of these algorithms in a predictive cost model would be excessive (and almost certainly impossible given the reliance to some extent on information that can only be measured once the network is in place)

The model Scorched node approach

18

The rationale for the scorched node approach

The scorched node approach accepts that:

these are real processes that increase the cost of providing services, and

that it is impossible to accurately capture the impact of such highly complex processes as these in a purely predictive model.

The scorched node approach therefore relies instead upon actual statistics about the design of operators’ networks as predictors of the aggregate impact that these effects would be likely to have on the network design of an operator, including that of a new entrant.

NB Not because incumbents’ have to continue operating their existing networks:

If the market were contestable (even if not fully contestable) then incumbents’ would have to set prices in line with those that a new entrant would charge;

New entrants would not have to recreate the existing design of an incumbent’s network if that were less than fully efficient, but they could be expected to suffer the same problems as incumbents already have, when rolling out their networks.

NB This does not mean that the modelled operator has to have exactly the same number and distribution of nodes as does a real operator, merely that the relationship between the drivers of node deployment and actual node placement, are similar in the model to those actually seen in the networks of real operators.


19

Notes re implementation in theLRIC Model of UK Mobile Network Costs

Information about the networks of the four UK mobile network operators was collected from a variety of sources – in particular the number of base stations, BSCs and MSCs

Information about the coverage and traffic carried by each of the networks was also obtained or estimated

The network design algorithms and parameters in the model were then fixed at reasonable values (based on general industry data)

A specific parameter of the network design algorithms (the “scorched node utilisation”) was then adjusted for each network element until the number of units of that element predicted by the model was reasonably close, for all network operators, to the actual number of units of that element believed to be in use in the real networks

The resulting value for the scorched node utilisation parameter simply describes how much lower (or higher) than expected (on the basis of the standard network design algorithms and parameters used in the model) the actual utilisation of network elements really is

The model can then predict the number of nodes that a 25% market share operator would be likely to have, with a reasonable degree of accuracy, based on the actual number of nodes in use by the UK operators today


20

The scope and detail of the model is critical

The model aims to capture:

all relevant network elements and business activities

all relevant expenditures:

– capital investment

– operating expenses

– return on capital employed

The level of detail in the model should be sufficient:

for the network design to reflect actual industry practice rather than some hypothetical optimum or simplification

to capture significant factors that influence the total cost of the network, yet should not be more complex than is absolutely necessary

The model

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Key inputs fall into five broad categories

Service demand levels

Network design rules and parameters

Equipment unit costs (and price trends)

Cost of capital

Service routeing factors

The model

22

The key outputs are a number of cost figures

For each year, the model outputs:

total common cost

total incremental costs

unitised, un-marked up incremental cost per service

unitised marked-up cost per service, for a number of alternative mark-up regimes

Unitised costs represent:

total costs associated with an increment divided by number of demand units of that increment

The model

23

Mark-ups

Unmarked-up costs represent the raw incremental cost associated with each increment, without recovery of common costs

Common costs may be recovered by marking-up some or all of the raw incremental costs of services – increasing prices of those services to ensure recovery of the costs common to some or all services

A number of different mark-up regimes are possible – see later for details

In all cases mark-ups are calculated and applied as a percentage increase on raw incremental costs

The recovery of common costs from services is therefore done by reference to incremental costs (possibly more or less weighted according to the service) and not by reference to any common unit of demand or supply (which is typically how such costs would be allocated to services in a fully allocated cost model)

The model

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The model flow consists of six major building blocks; information flows from input, to calculation, to output …

Economic cost

Network design

Network element costing

Service costing

5

2

4

Forecast of demand

2000–2006

B

C E F

D

3

Cost drivers, services and increments

A

1

The model

25

… which are shown in brief in this section

The following slides indicate the main data, assumptions, calculations and information flows associated with each of the:

six building blocks identified

five information flows

Following this section, each section of the model is discussed in greater detail

Information flowData or

assumptionsCalculationsor Outputs

Major elements

Legend

The model

26

A. Cost drivers, services and increments

Define how the increments will interact

Define what the drivers of cost are

Define the associated services

and increments


The model

27

B. Forecast of demand 2000–2006

S-curvepenetration

Minutes per sub

MByte user penetration

2G/3G partition

2/2.5/3G partition

MByte per sub

SMS penetrationSMSs per

user

2G incomingminutes

2G outgoingminutes

SMS volumesGPRS users

HSCSD MBytes

GPRS MBytes

Market shares

Mobile subscribers

Forecast of demand

The model

28

1. Cost drivers and demand forecasts to network design

Year average mobile subscribers

Year total incoming minutes

Year total outgoing minutes

Year total SMS messages

Year average GPRS users

Year total GPRS Mbytes

Year total HSCSD Mbytes

Network design

Select year:00/0101/02

02/03…

Select operator:GSM 900,GSM 1800

Forecast of demand

2000–2006


The drivers of cost

The model

29

C. Network design

Coveragenetworkdesign

Fullnetworkdesign

Incrementalnetworkdesign

Demand inputs

Selected year

Design parameters

Coverage

Selected operator

The model

30

2. Network design to economic cost

Network design

Selected year

Economic costSelected operator

Out-turn utilisation profiles

The model

31

D. Economic cost

Economic lifetime

Annualisationpercentage

Selected year

Selected operator

Out-turn utilisation profiles

* Calculation performed for each item

Economic cost

calculation

00/01 MEA capex

Opex trends

00/01 MEAopex

Capex trends

The model

32

3. Network design to network element costing

Network design

Coverage network deployment


Incremental network deployment

Full network deployment

The model

+

=

33

4. Economic cost to network element costing

Economic cost Economic cost for each item Network element costing

The model

34

E. Network element costing

Coveragenetwork

cost

Incrementalnetwork

cost

Full networkcost


Economic cost for each item



The model

+

=

35

5. Network element costing to service costing


Common costs of coverage

Service costing

Average incremental cost of each network element per unit output

The model

36

F. Service costing

Mark-upsto recovercommon

costs

Unitised incremental

cost per service


Average incremental cost of each network element per unit output

Routeing factors

The model

37

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



38

We assume four primary cost drivers

In a mobile network, the primary drivers of cost are:

the level of coverage required, either geographically, or in terms of quality (in-building penetration, etc.)

the number of customers (subscribers)

the amount of traffic that is carried on the network

the quality of service (QoS) offered to the customers, in terms of blocking or dropping probabilities

In addition, a range of secondary drivers of cost exist, for example:

number of location updates

number of call handovers



Define the associated

services and increments

Cost drivers, services

and increments

The model Cost drivers

39

Coverage requirements are defined in terms of population and area coverage

Coverage is often quoted in terms of percentage of population covered (as per licence obligations)

More useful to a mobile network designer is the geographical area covered (disaggregated by type):

converting population coverage into area requirements usually requires detailed demographics

We define a number of area types that effectively capture the broad range of radio environments in a country. In the UK, we used:

urban, suburban, rural, highway

For example 90% of the population can be covered in 60% of the land area, comprising all urban, all suburban, part rural and part highway coverage

strictly speaking, no-one lives on a highway, and such deployments cover rural motorway-side towns and villages

100%

100%

90%

60%

Urban

Sub

urban

Rural

Highway

Area

Pop

ulat

ion


40

Notes re implementation in theLRIC Model of UK Mobile Network Costs

In-building penetration is not explicitly quantified in the model

The scorched node approach ensures that the level of in-building coverage included in the model is comparable with that typically provided by UK operators

Likewise, the effects of secondary cost drivers, such as the number of location updates and call handovers, are not explicitly quantified in the model

The values of other network design parameters have been set conservatively to provide sufficient capacity to deal with these activities


41

Customer-driven costs are not significant ...

Mobile networks do not have substantial investments tied up in plant dedicated to individual customers

However, some elements (such as the maintenance of a HLR about status of customers) are sensitive to customer volumes

Hence, the model contains the (year average) number of subscribers as a driver

In addition, each customer requires a mobile handset in order to make or receive calls

The cost/subsidy per handset is the only relevant cost component and in general considered separately from customer-driven network costs

The amount of costs associated with handsets may however be taken into account in the mark-up regime

Similarly, the (year average) number of subscribers is used to drive handset costs

HandsetsInfrastructure related


42

... whereas traffic and quality of service are significant cost drivers

Principal measures used when dimensioning network elements are:

busy hour erlangs (busy hour minutes/60)

busy hour call attempts

Levels of cost drivers are calculated separately for each traffic-related service, based on the annual amount of traffic

the use of appropriate annual traffic and busy hour averaging parameters ensures that the network is also driven by the year average load

Traffic cost drivers (incoming, outgoing and on-net voice, SMS messages, GPRS and HSCSD data traffic) are assumed to be parallel (see next slide for explanation) and hence can be combined into a single increment called traffic

Quality of service is an important driver of cost

However, inverting the relationship between quality of service and cost is a complex transformation, and does not result in a simple increment that is orthogonal or parallel to others

Hence we do not define a service increment called quality of service, with X units:

and cannot determine the cost per unit of quality (whatever unit that may be)

However, the model contains blocking probabilities as inputs, so can be used to investigate the variance of other service unit costs with quality of service

The base case values for these are:

2% blocking on the air interface

0.1% blocking in the core network

Quality of serviceTraffic


43

What is the significance of orthogonal and parallel services?

If the services are orthogonal, then equipment that supports service 1 does not support service 2 and vice versa

no common costs exist (other than the coverage network, if appropriate)

If the services are parallel, then equipment that supports service 1 partially or entirely supports service 2, and vice versa

common costs exist between the services, according to the levels of demand and design algorithms

dedicated costs also occur for each service, where appropriate

For example, two drivers of cost, each with a corresponding service increment:

HLR – for customers only TRX – for traffic only

TRX – for voice traffic TRX – for GPRS traffic

GGSN – for GPRS only


44

Combining service into a single increment simplifies the calculation requirements

Most services exhibit both parallel and orthogonal behaviour, depending on the particular equipment class which they are interacting with:

for example, HLRs are a dedicated resource for customers; however, the MSC processing requirement of location updates (a customer driven cost) is shared with the MSC processing requirements of incoming and outgoing call attempts

Resolving the common and incremental costs associated with each increment absolutely is a complex algebraic calculation and a time consuming process:

such a calculation needs to resolve all combinations of common costs and incremental cost by considering all possible permutations of the increments

Combining services into a single increment for all demand simplifies the model:

orthogonal service costs are resolved without need for complex calculations

parallel service costs are resolved on the basis that any common costs that may arise are automatically allocated on the basis of resource consumption


45

The Oftel model uses a single increment for all traffic demand, representing a single parallel increment for all traffic, plus an orthogonal increment for

customers Total cost of the network is taken to be the

sum of:

the standalone cost of providing a specified level of coverage

the incremental cost of expanding that network to carry a specified volume of traffic

the incremental cost of expanding that network to serve a specified volume of customers

Services

Coverage

Traffic

Busy hour total traffic load

Busy hour call attempts

Peak SMS throughputCustomers

Cos

ts

Number of location updates

Cov

erag

eIn

crem

enta

l


46

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



47






and increments

At one stage the Oftel model contained eight separate services

In general, services should relate to the fundamental services which the subscribers are purchasing

Applications or value-added layered services are not considered:

this simplification is influenced by the fact that the vast majority of current network traffic and costs are due to simple voice communication

data transport is assumed to become more important in later years, however we use a Mbyte data transport service, rather than a range of uncertain data applications

The handset increment can be considered separately from (i.e. is orthogonal to) the other increments

Handsets

Customers

Mobile originated off-net minutes

Mobile originated on-net minutes

Mobile terminated minutes

SMS messages

GPRS Mbytes

HSCSD Mbytes

The model Services and increments

48

Considering all permutations of service demand requires a large number of calculations (16 calculations for 4 increments)

Raw incremental costs

e.g. 80%

Common costs

e.g. 5%

Coverage cost

e.g. 15%

Voice

SMS

GPRSHSCSD

Coverage

Voice + SMS GPRS + HSCSDVoice +… … + GPRS

Voice +… .. + HSCSDSMS + GPRS

SMS +… .. + HSCSD

Voice + SMS + GPRS

SMS + GPRS + HSCSD

Voice + SMS + GPRS + HSCSD

Voice + SMS + … .. + HSCSD… + GPRS + HSCSDVoice +…

1,23

456

8

7

910

11

Areas are not to scaleVoice represents customers and voice minutesFully and separately resolving 8 increments would require 64 separate calculations

Incremental costs using single traffic increment


49

Even when the permutations have been calculated, the mark-up regime becomes horrendous

Each common cost 1–11 needs to be marked-up across the services which it supports

The order and nature in which costs are marked-up must be defined:

for example, equal-proportionate?

mark-up on mark-up?

The sum of all the common costs 1–11 is small in comparison with the raw incremental costs of the major traffic increments (voice and latterly GPRS)

The coverage cost (by far the largest common cost) must also be marked up in some fashion

Voice

SMS

GPRSHSCSD

Coverage

Voice + SMS GPRS + HSCSDVoice +… … + GPRS

Voice +… .. + HSCSDSMS + GPRS

SMS +… .. + HSCSD

Voice + SMS + GPRS

SMS + GPRS + HSCSD



1,23

456

8

7

910

11


50






and increments

Hence, after investigation, we implemented a single increment for traffic in the Oftel model

The model calculates incremental costs for the services using a single increment

This increment resolves the allocation of costs using routeing factors:

shared infrastructure on the basis of demand consumption:

– equivalent voice equivalent erlangs, or other parameter

dedicated infrastructure is still allocated directly to the appropriate service

This model enables:

understanding of the relevant increment calculations

comparatively rapid calculation time

simple (yet automatic) allocation of common costs between services

simplified mark-up step

And produces results for the voice LRICs that are very close (~1% difference) to those of a combinatorial multi-increment model


51

In addition, the definition of the coverage network was altered …

The coverage network is required to:

support at least one incoming or outgoing voice call, anywhere within the coverage area of the network

Such a network, due to equipment divisibility, actually contains enough capacity to support many more voice calls at no additional cost

for example, one TRX has 8 channels

The coverage network was investigated. It was determined that:

a large proportion of the cost of the coverage network was actually equipment which directly supported traffic or customers

only some equipment represented an absolute minimum requirement to provide coverage

– for example, the acquisition and preparation of the 2000–3000 sites required to achieve minimum population coverage


52

Coverage network

Minimum coverage presence

Coverage capacity

… to better reflect the relationship between capacity and cost

The coverage network was broken into two parts:

the minimum coverage presence

– network management system (NMS) and points of presence (macro site acquisition, preparation and rental)

the coverage capacity

– equipment deployed in the coverage network providing more capacity than actually required to support just one voice minute

BSCBTS

Inter-switchtransmission

BSC–MSCtransmission

Backhaultransmission

Macro-cellsite and TRXs

HLR

MSCVLR

NMS

Macro-cellsite acquisition,

preparation and rental

BSCBTS




Macro-cellBTS and TRXs

HLR

MSCVLR

NMS


53

The two parts of the coverage network are dealt with separately

The minimum coverage presence is used as the mark-up term

The coverage capacity is added to the incremental network capacity:

all capacity-providing elements deployed in the coverage network are considered as incremental to traffic or customers as appropriate

the cost of these capacity elements is allocated according to routeing factors

This definition reduces the amount of cost in the coverage network, and as a consequence, reduces importance of the choice of mark-up mechanism


54

The Oftel model is a good representation of reality and significantly more manageable than possible alternatives

Combinatorial multiple increment Single increment, MCP

Voice

SMS

GPRS

HSCSD

Coverage

Voice + SMS GPRS + HSCSDVoice +… … + GPRSVoice +… .. + HSCSD

SMS + GPRSSMS +… .. + HSCSD

Voice + SMS + GPRSSMS + GPRS + HSCSD



Minimum coverage presence

Voice

SMS

GPRS

HSCSD

Voice represents customers, incoming minutes, outgoing off-net and outgoing on-net minutes

Diagrams not to scale. Total cost is the same in both cases


55

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



56

Demand forecasts are required in order to calculate cost results to 2006

It is important that this forecast is consistent with the methodology used elsewhere in the model for determining the LRICs:

for example, the allowance for reasonable growth which is factored into the LRIC approach should be consistent with the demand growth assumed in the forecasts

We primarily require a set of reasonable forecasts which will enable the model to be run, investigated and produce reasonable information:

the assumption set was tailored to provide the required fidelity in forecasting, yet small enough to be easy to use and modify

The forecasts used in the model were intended to be operator non-biased, for example:

all operators tend to the same market share

all operators are subject to the same rates of long term traffic growth

all operators have identical assumptions concerning HSCSD, GPRS and UMTS demand

historic nature of an operator’s subscriber base persists in the forecast

The model Demand forecasts

57

Base case demand forecast: subscribers


0

10

20

30

40

50

60Jan-92

Jan-94

Jan-96

Jan-98

Jan-00

Jan-02

Jan-04

Jan-06

Jan-08

Jan-10

one 2 one

Orange

BTCellnet

Vodafone

TIW/Hutchison 3G

58

Base case demand forecast: outgoing minutes per subscriber per quarter


0

100

200

300

400

500

600

Oct-99 Oct-01 Oct-03 Oct-05 Oct-07 Oct-09

Vodafone BTCellnet Orange one 2 one

59

Base case demand forecast: incoming minutes per subscriber per quarter


0

50

100

150

200

250

Oct-99 Oct-01 Oct-03 Oct-05 Oct-07 Oct-09

Vodafone BTCellnet Orange one 2 one

60

The forecasts contain a number of inputs, calculations and outputs

S-curves, for parameters which grow to a saturation point

Simple percentages for time dependent shares or divisions

Mobile subscribers, by operator

Incoming and outgoing* voice minutes, on 2G and 3G networks

Demand parameters in each year

Quarterly growth rates, for parameters which increase or decrease in a smooth fashion

SMS messages

HSCSD, GPRS and UMTS transport service users and Mbytes of traffic

Demand parameters in future years, in order to calculate allowances for reasonable growth

Inputs take the form of:

The following demand parameters are calculated:

Outputs of the forecast are:

*outgoing voice minutes forecast includes outgoing on-net minutes


61

S-curves are used for parameters which grow to a saturation point

The inputs required for an s-curve are:

saturation of x

base year

x(A) at time A

x(B) at time B

Used for:

mobile market penetration

migration of voice traffic from 2G to 3G

data transport service penetration

x(t)

tA B

x(A)

x(B)

base

saturation of x


62

Percentage inputs are used for time dependent shares or divisions

A simple percentage is used to distribute a parameter across different categories

Used for:

market shares

Mbytes across GPRS, HSCSD and UMTS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jul-0

0

Jul-0

1

Jul-0

2

Jul-0

3

Jul-0

4

Jul-0

5

Jul-0

6

Jul-0

7

Jul-0

8

Jul-0

9

Pa

rtiti

on

of

Mb

yte

s b

y te

chn

olo

gy

UMTS GPRS HSCSD

Example


63

Quarterly growth rates are used for parameters which increase or decrease in a smooth fashion

Simple exponential growth (or decline) can be specified with a single percentage

Annual growth rates are the compound of quarterly growths:

e.g. 2% per quarter constitutes 8.2% annually

The input of quarterly growth rates are used to forecast:

minutes per subscriber

SMS per user

Mbytes per user

x(t)

tt1 t2 t3t0

grow

th 1

grow

th 2

grow

th 3


64

Various levels of dimensionality are contained in the Oftel model forecasts

Mobile subscribers by year quarters by operator

Incoming voice minutes

by year quarters by operatorby technology

2G/3G

Outgoing voiceminutes

by year quarters by operatorby technology

2G/3G

SMS messages by year quarters by operator

Data transport Mbytes by year quartersby technology

HSCSD, GPRS, UMTS

Data transport users by year quartersby technology

HSCSD, GPRS, UMTSidentical for

each operator

identical for each operator


65

Our technology assumptions by their nature contain implicit consideration of the range of issues that will affect traffic on these networks

For example, the partition of voice traffic across 2G and 3G networks implicitly makes assumptions on:

numbers of subscribers on 2G or 3G plans

operator strategies for 3G voice and data

3G coverage extent or black-spots

high-use 3G early adopters and low-use price-sensitive 2G remaining subscribers

The use of quarterly assumptions assist in defining accurately when services are assumed to be launched


66

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



67

The Oftel model network design algorithms are based on anumber of principles

Reflect industry practice with regard to base station layout, checked against existing networks

this checking is a combination of parameter calibration and application of industry experience

Represent the use of modern technology

Satisfy the requirements of coverage and demand

Allow for reasonable growth (but no more)

Contain the key differences between GSM 900 and GSM 1800 radio network deployment

different cell radii and different radio layer spectral efficiency*

The model contains around 60 different units of equipment, sufficient to capture the required fidelity in network design, yet small enough to be manageable

The model Network design

* Spectral efficiencies vary between GSM 900 and GSM 1800 networks in the UK because the 900MHz spectrum allocation is more fragmented than the 1800MHz spectrum allocation

68

Simplified network diagram

BSCBTS




Macro-cellsite and TRXs

HLR

MSCVLR

NMSBackhaultransmission

Micro-cell or pico-cell site

Internet

PCU

SGSN GGSN

* For the purposes of inter-switch transmission, we assume BSC, SGSN and MSC are co-located

Dedicated GPRS infrastructure


69

Omnimacrocell

Bi-sectoredmacrocell

Tri-sectoredmacrocell

Microcell

Picocell

We define a number of area and cell types

Six cell typesFour area types

Suburban

Urban

Highway

Rural

Tri-sectored GSM 1800 dual spectrum

overlay


70

The area types are based on population densities

It is assumed that population density is a proxy to radio planning area types. Hence the model utilises data from around 9000 postcode sectors to assist in the categorisation of area types in this fashion

Definitions of the area types used are as follows:

Urban – postcode sectors with a population density larger than 8178

Suburban – postcode sectors with a population density between 8175 and 721 per km2

Rural – postcode sectors with a population density less than 721 per km2

Highway – 50% (11 000 km) of the primary roads in the UK

– this area type is actually “rural highways” since urban and suburban road are assumed to be within urban or suburban coverage


71

The six cell types allow differences in network designby area type to be reflected

The model contains a number of inputs for:

the proportion of cells of each macro type, in each area type

However, the inputs currently assigned in the model reflect a simplified situation:

tri-sectored macro sites are deployed in urban and suburban areas

bi-sectored macro sites are deployed in highway areas

omni-sectored macro sites are deployed in rural areas

These cell types are deployed in response to the greater of coverage or traffic requirements

Micro and pico sites (defined as single sector, 2 and 1 TRX respectively) are deployed in response only to traffic requirements, and furthermore, only in urban and suburban areas:

the amount of traffic that is carried on these cell layers is specified by a percentage (by area type) of total traffic in that area type

In the UK, the GSM 900 operators also have GSM 1800 spectrum.. The model deploys a GSM 1800 layer upgrade to these operators’ urban macro sites, and expands this in response to the amount of traffic loaded onto this cell layer


72

The use of a single increment for traffic requiresservice demand drivers to be added together

In reality, the radio interface responds differently to voice circuit, GPRS packet and signalling traffic. However, constructing a complex radio engineering model which separately deals with these traffic types is not recommended in a LRIC costing exercise

Rules are required to combine traffic from voice, SMS, HSCSD and GPRS in a suitable way

The Oftel model contains voice equivalent erlangs:

the amount of traffic equivalent to one voice erlang

rules are defined for converting service demand (eg SMS messages, GPRS Mbytes) into voice equivalent erlangs

Voice equivalent erlangs can then be added to normal voice erlangs, in order to drive the network design algorithms with the aggregate traffic load

It is assumed that all services have a coincident busy hour (which may lead to some overstatement of costs), and highly complex effects (such as different link margins (i.e. cell radii) for GPRS traffic) are neglected


73

SMS and HSCSD voice equivalent erlangs (VEErl)

SMS messages are carried by signalling channels in the radio layer:

the model assumes an average size for each SMS message, and a data rate for a channel

the model assumes the use of SDCCH (synchronous data control channel) for SMS message transfer

User demand represents both up and downlink traffic

HSCSD is a circuit switched dialup data service that enables users to open more than one channel in a particular direction, in order to obtain a higher rate of data transfer:

it is very similar to a circuit switched voice service

we need to assume a channel occupancy and data rate

SMS messages

40 bytes per SMS voice channel rate of

767 bit/s

1 minute 82 SMS

HSCSD mbytes

70% channel occupancy

voice channel rate of 14.4 kbit/s

1 minute 0.135 HSCSD Mbyte

80% of user demand in the downlink


74

GPRS voice equivalent erlangs

User demand represents both up and downlink traffic

GPRS is an IP packet switched service:

hence the model assumes 100% channel occupancy, and 12% overheads for IP protocol

GPRS has variable data rates:

four data rates (CS1–CS4) are available with GPRS

CS4 (around 22kbit/s per channel) represents transmission under idealised conditions, or when the network has a low level of loading

CS1 represents the lowest data rate of transmission, and is the likely rate achieved in the network under busy conditions (from which the model is driven)

An allowance has been made for the ability of the packetised GPRS service to utilise some of the gaps in traffic which occur as a direct result of using the erlang transformation to provision more channels than required. This assumed allowance is calculated to be small

12% additionalIP overheads

an allowance for packetised nature

1 minute 0.09 GPRS Mbyte

100% channel occupancy

voice channel rate of 9.05kbit/s (CS1)

80% of user demand in the downlink


75

GPRS traffic can utilise (to an extent) gaps between voice conversations

A certain amount of ‘under-utilised’ capacity exists as a result of applying the erlang blocking probability formula to the voice calls in a sector:

a BTCellnet paper (obtained from its website) indicates that this spare capacity can in fact be used by GPRS:

– some probability should be applied to this spare capacity, to work out its effective erlang capacity

The model calculates the difference between the number of channels deployed and the number of erlangs supported:

this number of channels is used to determine the relative loading of voice circuits and GPRS packet traffic:

– this factor is calculated to be 95%. i.e. GPRS packet traffic only demands 95% of the capacity for the same amount of voice circuit switched traffic


76

GPRS service demand interacts with a number of dedicated and traditional GSM network infrastructure

Total GPRS BH kbit/s(+12% IP)

Downstream GPRS BH kbit/s

GGSN100

SGSN100

Downstream GPRS voice equivalent BHE

Backhaul

Air interface

IP transmission

PCU

GPRS subscribers

Dedicated GPRS infrastructure

Existing GSMinfrastructure


BH = busy hour

77

The HSCSD service places demands upon all traditional GSM infrastructure

HSCSD voice equivalent erlangs are added to voice circuit switched erlangs (using routeing weighting) and used to drive the deployment of traditional GSM infrastructure, including:

base station sites and TRX

backhaul

BSC switching

interswitch transmission

switch ports

Voice calls require MSC/VLR processing to originate and terminate. This processing includes checking the validity of the subscriber, and locating the mobile handset in the network

HSCSD calls also require processing when they are originated from a HSCSD enabled handset:

we assume an average HSCSD session of 0.25Mbyte

assume 1.1 session attempts per session

assume the same MSC/VLR processing per session as an outgoing voice call attempt (20ms)

Radio and transmission MSC/VLR processing


78

Additional allowance for the distribution of traffic is made,over and above the use of area types

The model currently contains four area types (urban, suburban, rural, highway) in order to distribute traffic load across the country in a sensible fashion

However, within each area type, demand will be distributed non-homogeneously (both in time and space), and an allowance for this is included

to account for this effect, an additional ½ TRX is deployed on each sector

The requirement for half an additional unit of capacity at each point in the network was calculated by Analysys using a network simulation tool

erla

ngs

per

sect

or

Area type

Urb

an

Sub

urba

n

Rur

al

Hig

hway

erla

ngs

per

sect

or

Area type

Urb

an

Sub

urba

n

Simplified average situation Non-homogeneous reality

Rur

al

Hig

hway

Additional capacity requirement over the average

Diagrams not to scale


79

Equipment utilisation is an important input parameterto the network design algorithms

A large number of network design calculations are based upon the following relationship:

number of items required = demand / capacity per item * utilisation

The utilisation parameter contained in the Oftel model is used to reflect the explicit combination of a number of different ‘under-utilisation’ effects:

Design utilisation: most equipment has a (vendor designated) maximum utilisation parameter (for example, 90%). This utilisation parameter ensures that the equipment in the network is not overloaded by any transient spikes in demand

Scorched node utilisation: the deployment of a scorched node network is captured explicitly by the use of additional utilisation parameters. These indicate the degree to which equipment is unable to reach the level of utilisation that would be achieved in a scorched earth deployment, as a direct result of adhering to the scorched node constraint

Reasonable growth utilisation: in a real mobile network, equipment is deployed in advance of expected demand (weeks to years), depending on the equipment modularity and the time it takes to make all the necessary preparations to bring new equipment online. The model explicitly determines the level of under-utilisation in the network, as a function of equipment planning periods and expected demand.


80

Reasonable growth utilisation parameters are calculated explicitly

Explicit inputs relating to the provision of a reasonable allowance for future growth enable the effect on average equipment utilisation to be calculated

This is done for a number of asset classes, by choosing:

the key demand driver which is to be used in determining future growth in demand

the point in the future at which demand should be considered

– The future demand point for each asset class is taken to be half of one planning period in the future, based on the simple assumption that some sites will have only just been upgraded (and hence have sufficient capacity to meet demand anticipated one entire planning period into the future) whereas other sites will be about to be upgraded (and therefore are only able to meet current demand), with most sites lying somewhere in between these two extremes (and hence on average the effect is likely to be as if all sites have sufficient capacity to meet demand for about half of one planning period into the future)

The model contains a forecast of demand over time, which is then used in the calculation of the reasonable growth utilisation


81

Calculation method for reasonable growth utilisation

Assign a key driver to each class of infrastructure, e.g. demand:

(demand at time t) = xt

define planning period (2p), and determine demand at time half planning period later:

(demand at time t + p ) = xt+p

Number of elements deployed at time t, if no future growth:

= xt / (capacity * normal utilisation)

Number of elements deployed at time t+p, if no future growth:

= xt+p / (capacity * normal utilisation)

Hence actual utilisation of elements at time t, given forward looking deployment is:

xt+p / (capacity * normal utilisation) = xt / (capacity * actual utilisation)

hence:

– actual utilisation = normal utilisation * (x t / xt+p)

t t+ptime

Dem

and

normal utilisation = design utilisation * scorched node utilisation allowance


82

An example of maximum utilisation

Macrocell BTS:

design utilisation input at 80%

scorched node allowance input at 90%

Reasonable growth driver set to “traffic”

Look-ahead selected as 2 years ahead

Traffic in two years time is 60% higher than today’s traffic, hence

reasonable growth allowance = 1/1.6 = 63%

Calculated maximum utilisation of a macrocell BTS is thus:

80% * 90% * 63%

= 45%

Vendor says “do not run a BTS at more than 80% peak capacity”

Due to the inefficiencies which arise as a result of scorched node (compared to scorched earth) BTSs are not able to

reach their designed utilisation

The main driver of the deployment of BTSs is traffic

BTSs (sites) have a long planning period


83

Key demand drivers

Year average subs

Year total incoming minutes

Year total outgoing minutes

Year total SMS messages

Year total GPRS Mbytes

Year total HSCSD Mbytes

Year average GPRS users

Year total minutes

Year total approx traffic

Asset classes

TRX

BTS – macro, micro and pico

backhaul links

BSC

BSC-MSC transmission

MSC/VLR – CPU and ports

HLR

Inter-switch transmission

SMSCs

PCU

GSNs – connections and peakthroughput

IP transmission

For each asset class, the key demand driver andperiod of planning must be selected

Look-ahead period

Current time

2 weeks ahead

1 month ahead

1 quarter ahead

6 months ahead

1 year ahead

2 years ahead

3 or more years ahead


84

The model also explicitly calculates the output utilisation profiles required for the economic depreciation calculations

The economic depreciation calculations require equipment utilisation profiles (taken into account when calculating economic life and distributing the cost of an asset over its lifetime)

These profiles are calculated for a number of classes of equipment in the model

The reasonable growth utilisation factor is not taken into account in the determination of output utilisation since these assets are deployed in advance of the demand they will support

Scorched node utilisation allowance

100%

y

x(t)

time

100% utilisationDesign utilisation allowance

Actual out-turn utilisation Output utilisation profile for economic depreciation is

x(t) / y


85

Network design flow diagrams

The following slides provide details of the network design algorithms:

flow diagrams

explanatory sections relating to these flow diagrams

Input parameter (data or assumption)

Calculation

Major equipment deployment output


86

Base station sitesSpectrum

Reuse

TRX bandwidth

Utilisation of TRX and BTS

TRX Traffic (BHE)

BTS and TRX unit capacity

Site type proportions

Maximum achievable capacity of a sector

Effective capacity of a sector

Sectors required for capacity

Spectral capacity of a sector

Sites (by type) required for capacity

Maximum cell radii

Area to coverMaximum cell

area

Sites required for coverage

Number of sites (by type)

used in TRX calculations

Non-uniformallowance (0.5 TRX/sector)


87

Base station sites (2) Spectrum

Reuse

TRX bandwidth


TRX Traffic (BHE)








Maximum cell radii


area





Spectrum, reuse and TRX bandwidth are reasonably well defined parameters

The non-uniform allowance is the ½ unit of capacity per sector allowance for the fact that traffic is not evenly distributed (in both time and space) across each area type


88


Reuse

TRX bandwidth


TRX Traffic (BHE)








Maximum cell radii


area





Different cell radii are used for each area type, and for GSM 900 and GSM 1800.

The area to cover is again by area type, and in terms of km2

TRX traffic is the (routeing weighted*) sum of all the traffic types, allocated to each area and cell type using percentage inputs

Site type proportions are simplified assumptions for:

• all urban and suburban as tri-sectored

• all highway as bi-sectored

• all rural as omni-sectored

• micro and pico sites are defined asomni-sectored

* routeing weighted: for example, one on-net mobile-to-mobile minute has two contributions to TRX BHE


89


Reuse

TRX bandwidth


TRX Traffic (BHE)








Maximum cell radii


area





The number of sites deployed (for each area and cell type) is determined as the greater of those required for coverage or traffic


90

Typical results of Base station site calculations


0

2 000

4 000

6 000

8 000

10 000

12 000

93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03

Micro

Urban Macro

Suburban Macro

Highway Macro

Rural Macro

91

TRXs

TRX traffic (BHE)

TRX unit capacity and utilisation

Sectors per site (by site type)

Minimum TRXs per sector

Number of sectors

Traffic per sector (BHE)

TRXs per sector to meet traffic requirements

Number of sites

Number of TRXs per sector Number of TRXs (all sectors)

used in Site–BSC transmission calculations

from Sites calculations

used in BSC calculations

Non-uniformallowance (0.5 TRX per sector)


92

TRXs (2)

TRX traffic (BHE)




Number of sectors



Number of sites






These assumptions are the same as used in the BTS calculations

The minimum TRX deployment is 1 TRX per sector


93

TRXs (3)

TRX Traffic (BHE)




Number of sectors



Number of sites






The final number of TRXs is again calculated in response to coverage requirements (driven by the number of sites) and traffic requirements (driven by the amount of traffic per sector)


94

Typical results of TRX calculations


0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03

Micro

Urban Macro

Suburban Macro

Highway Macro

Rural Macro

95

Base station site – BSC transmission

Required circuits per TRX Required circuits per sector

Link capacity(by link rate)

Links required per site(by link rate)

Number of TRXs per sector

Links required per site(at selected link rate)

Microwave links (by link

rate)

Leased lines(by link rate)

Microwave hops (by link rate)Link type proportions

Hops per link


from TRX calculations

Required circuits per siteSectors per site (by site type)

Link utilisation


96

Base station site – BSC transmission (2)



Links required per site (by link rate)




rate)



Hops per link




Link utilisation

The number of circuits per TRX is a well known network design parameter

A calculation determines the number of links of each type (2, 8, 16, 32 Mbit/s) required to support the demand …

… and then deploys no more than one link per site, selecting the required link capacity


97

Base station site – BSC transmission (3)



Links required per site (by link rate)




rate)



Hops per link




Link utilisation

For example, 80% microwave self provided and 20% leased lines, specified for macro, micro and pico sites in each area type

Again, specified for macro, micro and pico sites in each area type


98

BSCs

Number of TRXs (all sectors)



rate)



rate)

Number of BSCs Number of MSC-facing ports

BSC capacity Utilisation

Number of BTS-facing ports

Ports per link (by link rate) Ports per link (by link rate)

used in BSC – MSC transmission calculations

used in MSC calculations

from TRX calculations from Site – BSC calculations from BSC – MSC calculations


99

BSCs (2)

Number of TRXs (all sectors)



rate)

Number of BSCs

BSC capacity Utilisation

Number of BTS-facing ports

Ports per link (by link rate)

used in BSC – MSC transmission calculations

from TRX calculations from Site–BSC calculations

BSC deployments are simply driven by the number of TRXs deployed in the radio network



rate)

Number of MSC-facing ports

Ports per link (by link rate)

used in MSC calculations

from BSC – MSC calculations

The number of BSC ports does not drive the deployment of BSCs, but the number of MSC-facing ports is taken into account in the MSC dimensioning


100

BSC – MSC transmission

BSC–MSC traffic (BHE) Traffic per BSC


Links required per BSC (by link rate)

Link utilisation

Number of BSCs

Links required per BSC(at selected link rate)


from BSC calculations


rate)


Microwave hopsLink type proportions

Hops per link


101

BSC – MSC transmission (2)

BSC – MSC traffic (BHE) Traffic per BSC



Link utilisation

Number of BSCs





rate)



Hops per link

BSC – MSC traffic is again a (routeing weighted) sum of all traffic types passing from BSC to MSCs


102

BSC – MSC transmission (3)

BSC–MSC traffic (BHE) Traffic per BSC



Link utilisation

Number of BSCs


used in BSC calculations to define number of MSC-facing ports required (but not the number of BSCs)



rate)



Hops per link

These calculations are similar to those used for base station site – BSC transmission, though involve different assumptions where appropriate


103

MSCs

Minimum MSCs

CPU capacity (BHms)

CPU utilisation

MSC capacity (CPUs)

Interswitch traffic (BHE)

Switch port capacity

Switch port utilisationProcessing demand (BHms)


Number of MSC/VLRs

Number of interswitch ports

Total number of ports

Number of interconnect-facing ports

Minimum interconnect ports


Switch port utilisation

Interconnect traffic (BHE)

used in MSC transmission calculations


MSC capacity (ports)

Number of MSCs required to meet demand for ports

Number of BSC-facing ports


104

MSCs (2)

Minimum MSCs

CPU capacity (BHms)

CPU utilisation

MSC capacity (CPUs)





Number of MSC/VLRs





Switch portcapacity


Interconnect traffic (BHE)from BSC calculations




MSC/VLRs are deployed in response to the CPU processing requirements of the network, generated by a number of services and processes, including:

• subscriber authentication

• incoming and outgoing circuit switched call set-ups

• SMS message send and delivery

• subscriber location updating



105

MSCs (3)

Minimum MSCs

CPU capacity (BHms)

CPU utilisation

MSC capacity (CPUs)





Number of MSC/VLRs





Switch portcapacity








In addition, the number of MSCs should also have sufficient capacity to support port demands.

However, this link is not automatic in the model, and must be completed with a manual check.


106

MSCs (4)

Minimum MSCs

CPU capacity (BHms)

CPU utilisation

MSC capacity (CPUs)





Number of MSC/VLRs













The number of ports are summed up from the three major types of ports present in the MSC

• each MSC-facing port in a BSC requires a reciprocal port in the MSC

• interconnect ports are driven by interconnect traffic (routeing weighted sum of all relevant traffic types), capacity and utilisation inputs

• there may be a contractual QoS minimum requirement for the number of interconnect ports

• interswitch ports are also driven by interswitch traffic (routeing weighted sum of all relevant traffic types), capacity and utilisation inputs


107

Interswitch transmission

Transmission utilisation Number of interswitch circuits

Number of interswitch ports from MSC calculations


108

Interswitch transmission (2)

Transmission utilisation Number of interswitch circuits

Number of interswitch ports from MSC calculations

The number of interswitch ports is simply driven by the number of interswitch ports (which was in itself driven by the amount of interswitch traffic)


109

HLR capacity

Number of customers

Minimum number of HLRsHLR capacity

Number of HLRs

HLR utilisation

HLR upgrade capacityNumber of HLR upgrades


110

HLR capacity (2)

Number of customers


Number of HLRs

HLR utilisation


HLRs are again driven by a simple calculation involving capacity, demand and utilisation.

However, at least two HLRs are required, at minimum, for redundancy


111

HLR capacity (3)

Number of customers


Number of HLRs

HLR utilisation


Capacity upgrades to the HLRs are deployed, however the full cost of a HLR is assumed in the base HLR, and hence HLR upgrades do not impact the cost results


112

Typical results of BSC, MSC and HLR calculations


0

20

40

60

80

100

120

140

93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03

BSCs

MSCs

HLRs

113

SMS centres

SMSC throughput capacity

Utilisation

Number of SMSCs

SMS throughput demand

Minimum number of SMSCs


114

SMS centres (2)

SMSC throughput capacity

Utilisation

Number of SMSCs

SMS throughput demand

Minimum number of SMSCs

SMS throughput demand is again a routeing weighted sum of all SMS types:

• Mobile originated (MO) off-net

• MO on-net

• MT

• server originated (voicemail, info-service, etc)


115

Dedicated GPRS equipment – PCU boards

PCU throughput capacity

Utilisation

GPRS MB throughput demandNumber of BSCs


Number of PCUs by throughputNumber of PCUs by 1 per BSC

minimum

Number of PCUs


116

PCU boards (2)

PCU throughput capacity

Utilisation

GPRS MB throughput demandNumber of BSCs


Number of PCUs by throughputNumber of PCUs by 1 per BSC

minimum

Number of PCUs

The number of PCUs deployed (packet control unit upgrades to BSCs) is calculated as the greater of capacity demands or one per BSC


117

Dedicated GPRS equipment – GGSNs

GGSN throughput capacity

Throughput utilisation

GPRS MB throughput demand

Number of GGSNs by throughput

Number of GGSNs by PDP contexts

Number of GGSNs

Active GPRS PDP contexts

GGSN PDP context capacity

PDP context utilisation

Minimum number of GGSNs


118

GGSNs (2)

GGSN throughput capacity



Number of GGSNs by throughput

Number of GGSNs by PDP contexts

Number of GGSNs

Active GPRS PDP contexts

GGSN PDP context capacity

PDP context utilisation

Minimum number of GGSNs

The greatest of three requirements are taken into account when calculating the number of GGSNs deployed:

• at least two for redundancy

• throughput traffic requirements

• PDP context (IP address) requirements


119

Dedicated GPRS equipment – SGSNs

SGSN throughput capacity



Number of SGSNs by throughput

Number of SGSNs by subscribers

Number of SGSNs

Connected GPRS subscribers

SGSN subscriber capacity

Subscriber utilisation

Minimum number of SGSNs


120

SGSNs (2)

SGSN throughput capacity



Number of SGSNs by throughput

Number of SGSNs by subscribers

Number of SGSNs

Connected GPRS subscribers

SGSN subscriber capacity

Subscriber utilisation

Minimum number of SGSNs

The greatest of three requirements are also taken into account when calculating the number of SGSNs deployed:

• at least two for redundancy

• throughput traffic requirements

• GPRS subscriber requirements


121

Dedicated GPRS equipment – IP transmission

Transmission utilisation

Number of IP transmission 2Mbit/s links

GPRS IP Mbit/s


122

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



123

The problem

How would an operator set its prices if it were operating in a (hypothetical) fully competitive and partially contestable market?

So as to neither under- nor over-recover costs, since:

– they would not enter if costs could not be fully recovered

– they would be prevented from over-recovery of costs by competition

Consistent with changes in the underlying costs of production and the contestability of the market, since:

– they will set their prices in line with those that a new entrant into the market at each point in time would charge

Traditional depreciation methods, such as straight-line or reducing balance depreciation, can achieve the first of these requirements, but not in general the second.

Economic depreciation can achieve both.

The model Economic Depreciation

124

Competitiveness vs Contestability

Competitiveness describes the extent to which operators already in the market compete with each other (and thereby control each others behaviour):

A fully competitive market is one in which there are at least two (non-collusive) players and no customer switching costs – customers can (and will) instantaneously switch from one provider to another if a better deal is on offer

Contestability describes the ease with which operators can enter (and exit) the market (and thereby control the behaviour of those already in the market):

A fully contestable market has no barriers to entry and exit – a new entrant can enter the market and capture all of an incumbent’s existing customers instantaneously if they offer a better deal

A partially contestable market has barriers to entry and exit – new entrants into the market can only capture customers from the incumbent after some delay (for example the time necessary to roll out their network) and/or at some limited rate (for example because of the need to build up their reputation and brand image)


125

What difference does it make whether a market is fullyor only partially contestable?

In a fully contestable market, incumbents (players already in the market, irrespective of the date they entered, or their scale) can never set prices higher than what it would cost a new entrant to provide the same service, using the most efficient means, since:

If they were to do so, new players would enter, set lower prices, and capture the entire market

(NB This is true even if the incumbent is a monopoly!)

In a less than fully contestable market, incumbents may be able to temporarily sustain prices that are higher than what it would cost a new entrant to provide the same service, using the most efficient means, to the same number of customers as the incumbent, since:

It will take time for the new entrant to be ready to capture all of the incumbents’ customers

And so in the mean time the new entrant’s cost per customer will be higher than it would be if they had instantaneously captured the entire market


126

Why then can’t incumbents in a less than fully contestable marketover-recover their costs?

They can if the market is less than fully competitive!

But if the market is fully competitive (or assumed to be), competition between the incumbents will ensure that prices overall (over the lifetime of the product) are no higher than the costs of production, since if any one incumbent attempted to set a price, at any time, that was higher than the competitive level, they would instantaneously lose all of their customers to their competitors.

In a fully competitive market it is therefore only the timing of the recovery of costs that differs between scenarios of full and partial contestability, not the total amount of cost recovered:

If the market is fully contestable, operators have to recover costs in each year from the customers making use of the service in that year, which in theory would lead to very high prices in the early years of operation

If the market is less than fully contestable then operators can keep prices at a reasonable level in the early years, albeit with a compensatory but small increase in prices in later years


127

Why model a less than fully contestable market?

Mobile markets are in practice less than fully contestable:

Significant up-front investment in network roll-out is required before any customers can be signed up

It took time for mobile operators to build up the market for mobile services

If the mobile operators had set prices commensurate with a fully contestable market in the early years those prices would have been very high, in which case the market would probably have never developed

If mobile operators are now forced to set prices as if the market were fully contestable then they will never fully recover the costs of their initial investments (they will suffer a so-called “windfall loss”)


128

The economic depreciation problem restated

What time-series of prices, consistent with trends in the underlying costs of production and the assumed contestability of the market, yield an expected NPV of zero over the period of interest?

An NPV of zero ensures that the prices are cost-based, as they would have to be in a fully competitive market, neither under- nor over-recovering total costs over the lifetime of the project

Consistency of prices with trends in the underlying costs of production and assumed contestability of the market ensure that those prices are reflective of those that a (hypothetical) new entrant into the market at each point in time would charge


129

The inputs

0

200

400

600

800

1000

0 1 2 3 4 5 6 7 8 9 10 11 12

Year of life

0%

20%

40%

60%

80%

100%

Capital investment

Operating expenses

Output (utilisation)

Underlying cost trend(capital and opex combined)E

xpen

ditu

re


130

First calculate the total expenditure…(We will initially assume a lifetime of 10 years)

0

200

400

600

800

1000

1200

1400

1600

1800

0 1 2 3 4 5 6 7 8 9 10 11 12

Year of life

0

200

400

600

800

1000

1200

1400

1600

1800

Capital investment

Operating expenses

PV of total expenditure(up to year 10)

Exp

end

iture


131

…then calculate the total relative output value(assuming the same lifetime of 10 years)

0%

50%

100%

150%

200%

250%

300%

350%

0 1 2 3 4 5 6 7 8 9 10 11 12

Year of life

0%

50%

100%

150%

200%

250%

300%

350%

Relative output value

PV of total relative outputvalue (up to year 10)

Underlying cost trend(capital and opex combined)

Output (utilisation)


132

Divide one by the other to yield the unit pricefor a relative output value of 100%

1644

492

334%

PV of total expenditures PV of total relative output value Unit price at 100% output value


133

Multiply this by the relative output value in each year to yield annual revenues

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10 11 12

Year of life

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Revenue

Unit price at 100%output value

Relative output value


134

Economic depreciation is then the difference between revenues and operating expenses

-200

-100

0

100

200

300

400

500

0 1 2 3 4 5 6 7 8 9 10 11 12

Year of life


Operating expenses

Revenue

Economic lifetime = last year in which economic depreciation is positive

Check that this matches with earlier assumption


135

Check that everything is consistent!

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

PV of totalrevenues

PV of totalannualised costs

PV of totalexpenditures

Revenues Operating expenses Economic depreciation Capital investment


136

Notes re implementation in theLRIC Model of UK Mobile Network Costs [1]

Model considers a period of interest longer than one asset lifetime:

Includes investment necessary to replace assets at the end of their useful life

Uses perpetuities to model the period beyond the finite horizon of the explicit calculations

This is economically rational in a less than fully contestable market since operators invest for the long term, not merely to obtain customers for the lifetime of each individual asset


137


Model calculates “revenue required” separately for capital investment and operating expenses

Simplifies modelling of separate underlying cost trends for capital costs (MEA prices) and operating expenses

Operating expenses are assumed to vary both with time and age of asset


138


Model computes “revenue required” separately for each of three components of total cost:

Long-run equilibrium costs – based on long-run equilibrium input prices and output

Additional costs of lower output in earlier years

Additional costs of higher input prices in earlier years

Makes it easier to ensure that the “underlying cost trend” is consistent with the assumed evolution of the market


139


The “underlying cost trend” applied in each case is different reflecting the different forces at work in each case:

Long-run costs = long-run input cost trend

Costs of lower output = Extent to which later entrants achieve long-run output more quickly than do earlier ones

Costs of higher input prices = Extent to which earlier entrants have to pay input prices higher than those implied by the long-run trend


140


Model tracks history of UK operators to date, together with a forecast of their likely future development

Would be equally valid to model the future of a new entrant into the market today (or any other date), but:

This would entirely disconnect the model from the reality of the incumbent operators (who are the ones whose charges are to be regulated)

Makes the model and results entirely dependant upon forecasts

Results ought to be the same anyway, since the objective of the approach is to identify that set of prices which an incumbent would charge which are consistent with those that new entrants would charge


141

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



142

Network costing is the multiplication of economic cost per item and network deployment per item

Coveragenetwork

cost

Incrementalnetwork

cost

Full networkcost


Economic cost for each item



The model Network costing

+

=

143

A number of business activities are included either directly or indirectly in the network costs

Included explicitly as direct costs:

equipment, site rentals, switch software, building preparation

network management

Included as indirect costs, per unit of infrastructure:

maintenance

accommodation, power, vehicles and IT


144

A number of similar calculations produce coverage, incrementaland total costs

The simple multiplication of economic cost per item and equipment deployments produces the headline total costs:

coverage network cost, defined as just the MCP

incremental network cost (which includes the equipment designated as coverage capacity)

total network cost

Economic cost per item

1

2

3

…

MCP

Number of items deployed

1

2

3

…

x

MCP

Total cost for each item

1

2

3

…

=

Total cost

TotalIncremental

TotalIncremental


145

The average incremental cost per unit output of each network element is simply the incremental cost of each network element divided by its output

Incrementalcost of each network element

1

2

3…

Routeing factors

Demand by service

1 2 3 …

x Average incrementalcost per unit output of each network element

1

2

3…

=

=Output of each

network element

146

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



147

The matrix of routeing factors is key to the allocation of incremental costs to services

* Routeing factors defined below

The model Service costing

Mark-upsto recovercommon

costs

Unitised incremental

cost per service


Average incremental cost per unit output of each network element

Routeing factors*

148

Routeing factors are relative numerical weightings for the consumption of resources by services

Capacity of each network element required by each

service (per unit of demand)

=Routeing factors


149

The axes of the routeing factor matrix are services and network elements

Each network cost must be allocated to one or more services, according to the consumption of resources

The allocation of certain network costs to particular increments may be varied, provided there is a good reason for allocating such a cost to a different service increment than that used to drive the cost. For example:

the costs of location updates could be allocated to customers or traffic, depending on whether location updates were seen as a feature applicable to subscribers or calls

Cus

tom

ers

Inco

min

g m

ins

OG

off

-net

min

s

OG

on

-net

min

s

SM

S m

ess

age

s

etc

Services

Assets

3-sector macro

BSC

HLR

MSC/VLR

etc

1 1 2 0.01

1

1000 50 20 70

1 1 2 0.01

* Illustrative routeing factors; OG = outgoing


150

Unitised incremental cost per item, for service 2

1

2

3…

The output of the service costing calculation is unitised incremental service costs

Average incrementalcost per unit output of each network element

1

2

3…

Routeing factorsx =


1

2

3…


1

2

3…


1

2

3…


1

2

3…


1

2

3…


1

2

3…

…

Total unitised incremental cost for service 1

Total unitised incremental cost for service 8

…


151

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



152

For each year of calculation:

The key outputs of the model are unitised and total costs

Total cost of Coverage MCP

etccustomers incoming calls outgoing calls

represents the unitised cost for one customer, number of boxes indicates the number of customers

represents the unitised cost for one incoming minute, number of boxes indicates the annual number of minutes

represents the unitised cost for one outgoing offnet minute, number of boxes indicates the annual number of minutes

The model Results

153

The costs of the coverage network must be recovered (marked-up) from the services

The economically optimal method of mark-up utilises Ramsey

pricing economics:

a larger mark-up is applied to services with a lower

elasticity to price change

this is complex and requires knowledge or assumptions

about service elasticity

A number of simpler approaches may be taken, for example:

equal proportionate, as selected by Oftel: mark-up is

applied to all the incremental costs (a proxy for

simplified Ramsey pricing)

‘premium on mobility’: coverage costs are seen as

attributable in equal proportionate terms to customers

and outgoing call minutes

attributable to access: coverage costs are seen as

entirely attributable to customers

NB In all cases the relevant mark-up is calculated and applied

as a percentage increase on the raw incremental cost of some

or all of the services

LRIC

Mark-up

Cus

t-om

ers

Coverage MCP

Traffic

Outgoing Incoming SMS…

LRIC

Mark-up

Cus

t-om

ers

LRIC

Mark-up

Cov

erag

eM

CP

Cus

t-om

ers Traffic

Outgoing Incoming SMS…

Coverage MCP

Traffic

Outgoing

Traffic

Incoming SMS…

Equal proportionate

Premium on mobility

Attributable to access

The model Results

154

We discuss four key sensitivities

Base modelIncreasing long term

growth in demand

Modifying networkdesign parameters

Reducing the price of (modern equivalent)

equipment

Modifying servicerouteing factors

A

B

C

D

The model Results

155

Sensitivity – service routeing factors

Base model

Modifying servicerouteing factors

A

the economic cost of the equipment required to support demand is allocated to each service in proportion to the consumption of each resource – new routeing factors will redistribute costs across the relevant services, and impact the outcome of common cost mark-up

The model Results

156

Sensitivity – long term demand

Base modelIncreasing long term

growth in demand B

algorithms in the model deploying equipment in advance of future demand would bring forward deployments - reducing the average utilisation of equipment

The model Results

157

Sensitivity – network design parameters

Base model

Modifying networkdesign parameters

C

network design algorithms would respond to new parameter values, ensuring appropriate deployments and, for example, impacting the economies of scale present in parts of the network – impacting the evolution of network utilisation as these economies of scale are exhausted

The model Results

158

Sensitivity – equipment prices

Base modelReducing the price of (modern equivalent)

equipment

D

economic depreciation algorithms take into account the expected prices of equipment in the future – will increase the recovery of costs in earlier years, as the price of equipment in future years is expected to be lower

The model Results

159

Executive summary



Cost drivers

Service costing

Demand forecasts

Network design

Network costing

Model results

Conclusions

The model:



160

The 2001 Oftel model was developed over a long period of time

The Oftel model contains a number of very specific features which have been tailored to meet the needs of the consultation process in the UK, including a specific variant of economic depreciation

Iterative processes with Oftel and the industry working group meant that a large number of complex calculations have been added or refined in the model, often as a reactionary measure to the demands of the industry working group

Conclusions

161

A number of lessons can be derived from our LRIC modelling experience

Use of a single increment for all traffic is necessary if the model is to be manageable

Understanding how new services will be reflected in the model (and potential corresponding regulation) should be defined early in the process

Ensuring that the model contains enough fidelity to capture key areas in sufficient detail, yet is concise enough to be understandable and workable, is critical

Conclusions

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[30/1/02 -02] The LRIC model of UK mobile network costs, developed for Oftel by Analysys, September...

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