LTE Offloading

Make better business decisions

11/09/2009

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30/03/2011

Modeling mobile broadband

network costs:

LTE and offload case studies

Dimitris Mavrakis – Senior Analyst | Networks30th March 2011

Our legacy

The problem

Our model

Case studies

Presentation outline

Our legacy

11/09/2009www.informatm.com

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30/03/2011

• WCIS: 5 million data points, 10000+ cellular handsets, MNO ownership, network

summary data

• Intelligence Centre: Quantitative and qualitative analysis, including subscriber,

traffic and base stations forecasts

Informa’s key strengths

How can we make this data relevant to the business case of a

mobile operator or an infrastructure vendor?

• I.e translate our subscriber and traffic data to revenues, ROI, CAPEX,

and network TCO?

• First step towards this is to estimate cost to transfer a GB (Cost/GB) and

network costs

• Why should we do this?


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Challenges facing mobile operators

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Reve

nu

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tra

ffic

Traffic (cost?)

Revenues

Voice driven networks• All users treated equally

• Simple dimensioning

• Simple to add more capacity

• Abundant backhaul capacity

Voice Data

Mobile broadband networks• All users not treated equally

• Complex dimensioning

• Variety of upgrade options

• Backhaul challenges

Operators need answers

• When will the network face congestion and demand capacity upgrades?

• What is the most cost effective solution to meeting future traffic demand?

• What are the key network cost drivers?

• How do different geotype deployment strategies affect the cost per GB?

• Is LTE needed in the short to medium term?

• What are the savings from data offload, including WiFi and femtocells?

• What are the savings from introducing network optimisation?

• Analyse the impact of indoor traffic versus outdoor traffic?

Given the current and expected growth in mobile traffic, what are the

most cost effective ways that operators can deploy future networks

to successfully manage traffic demand?


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Our model


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Inputs

• Network deployment scenarios• Radio access Technologies: HSPA, HSPA+, LTE

• Traffic Management: Optimization, policy based management, offload

• Spectrum: Various options for each technology

• Relevant network costs• Network OPEX + Depreciation of Network CAPEX = Total Cost of Ownership (TCO)

• Cost classification and behaviour (Traffic Demand Scenarios)• Service category: mobile Internet, social networking, portable Internet etc

• Technology: HSPA, HSPA+, LTE

• Geotype: Dense Urban, Urban, Suburban, Rural

• Indoor/outdoor traffic segmentation

• Methodology validation

• Results validation

Methodology outline


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Population information

• Total population

• Distribution (per geotype)

• Operator subscriber base and targets

Country information

• Total area (per geotype)

• Coverage requirements

Traffic demand

• Capacity requirements

• Per technology

• Per geotype

• Per device type

• Per subscriber

• Indoor/outdoor

Network deployment

• Spectrum

• Technology

• Backhaul

• Core network

• Offload

• Optimization

Network

TCO

Cost/GB

Traffic

demand

methodology


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Country

demographics

Operator network coverage

Operator subscriber

base

Operator network

traffic

Option 1: Traffic

by device type

only

Option 2: Traffic

by device type &

traffic class

Traffic optimisation

Total traffic demand

Network

deployment

model

Network

deployment

model

Network

deployment

model

•Population size & growth

•Population density by

geotype (dense urban,

urban, suburban, rural)% coverage of population by geotype

•Penetration of population•Subscribers by device type (non-smartphones, smartphones& portable devices)

2 options depending on information availability

Average MB per device typeIndoor & outdoor traffic splitTraffic by geotype

Average MB per device type per traffic classIndoor & outdoor traffic splitTraffic by geotype

By device type, traffic class & geotype

By device type, traffic class, geotypeOptimised & unoptimised

Case study: UK operatorHSPA vs LTE


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UK market: Subscriber information


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Dense Urban16,625,473

27%

Urban22,167,297

36%

Suburban15,164,784

25%

Rural7,686,764

12%

• Dense Urban and suburban areas dominant

• Rural deployments still driven by coverage

UK: traffic profiles


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2010 2011 2012 2013 2014 2015

Ac

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illi

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Non-smartphone Smartphone Portable Total

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2010 2011 2012 2013 2014 2015

To

tral tr

aff

ic (

PB

per

year)

Non-smartphone data Smartphone data Portable data

Inflection point in 2014 not enough to drive

smartphone traffic higher than portable

Device classAverage traffic per month

Annual growth rate

Non-smartphone 25MB 30%

Smartphone 250MB 30%

Portable 2GB 30%

UK network modeling parameters


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Dense Urban parameters

Technology WCDMA 100% coverage

Technology HSPA 90% coverage

Technology HSPA+ (hotspot) 20% coverage

Backhaul technologyMix of T1/E1 for WCDMA/HSPA sites and Point-to-point RF or leased fiber for HSPA/HSPA+ and hotspots

Urban parameters



Technology HSPA+ 30% coverage

Backhaul technologyMix of T1/E1 for WCDMA/HSPA sites and Point-to-point RF or leased fiber for HSPA/HSPA+

Suburban parameters



Backhaul technologyMix of T1/E1 for WCDMA/HSPA sites and Point-to-point RF or leased fiber for HSPA/HSPA+

Rural parameters



Backhaul technology T1/E1 for WCDMA coverage and Point-to-point RF for HSPA

Baseline WCDMA and

HSPA network

Geotype Constraint

Dense Urban 2013

Urban No constraint

Suburban 2013

Rural 2012

Capacity constraints

UK network costs


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2011 2012 2013 2014 2015

Ne

tork

TC

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US

$ m

illi

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)

Dense Urban Urban Suburban Rural

Worst case scenario:

Capacity upgrades will be handled

through new base station additions

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2011 2012 2013 2014 2015

Co

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GB

(U

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UK case study: Key takeaways

• Network costs are dominated by OPEX (~80% of annual TCO)

• Dense HSPA network can generally handle traffic

– Apart from capacity hotspots that need to be managed

• LTE does not present an economically viable solution to meet with traffic

demands.

• A new LTE deployment will cost a minimum of US$58 million compared

to upgrades to existing networks, assuming that the LTE deployment

begins during 2013


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US operator case studyWiFi offload


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US network modeling parameters


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• Area modeled: Dense Urban

• Operator market penetration: 30% (today) increasing to 32% (2015)

• Traffic profiles:

– Non-smartphone = 15MB

– Smartphone = 250MB

– Portable = 3GB

– Annual traffic growth = 30%

• Spectrum used for mobile network

– 850MHz and 1.9GHz for WCDMA and HSPA/HSPA+

• Backhaul

– T1/E1, owned/leased fiber and microwave

US WiFi offload assumptions

• No cost to operator

• No visibility on user behavior and traffic

Private offload (home access

point)

• Operator leases WiFi capacity from third party

• Average cost of $1/GB offloaded

Public offload –leased capacity

• Operator installed WiFi network

• Number of hotspots: 23,000 today increasing to 50000 (2015)

• CAPEX per hotspot: $1000 today decreasing to $800 (3015)

• OPEX per hotspot: $200 today decreasing to $100 (2015)

Public offload –owned capacity


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Data offloaded: 10% of total portable traffic and 20% total

smartphone traffic

US network offloaded traffic


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Tra

ffic

(P

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nn

ua

lly)

Total traffic Traffic after offload

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To

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Dense Urban Urban Suburban Rural

US network base stations


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2010 2011 2012 2013 2014 2015

To

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of

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Only new base station upgrades New base stations with WIFi offload

US network scenario comparison


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2011 2012 2013 2014 2015

An

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New BTS only New BTS and upgrades Private offload Public offload - leased Public offload - owned

WiFi offload key takeaways

• Offload strategies need to be carefully addressed in order to avoid additional costs.

• Private WiFi offload

– Best case in terms of cost, but no visibility on user behavior or traffic patterns.

• Public WiFi

– Best suited to solve capacity constraints in congested areas and operators can either partner with a

hotspot provider or deploy their own networks.

• Leasing WiFi

– Additional costs may break the offload business case and increase overall network costs as high as

radio access upgrade costs.

• Operator owned public WiFi

– Best suited to offload traffic in congested areas and allow operators to control the user experience

while providing necessary headroom for radio access networks.

– Costs involved in deploying nationwide hotspot networks but the available WiFi capacity to the

mobile operator can be significant.

• Specialist solutions for WiFi offload will appear in the market during 2011, including

gateways that interface WiFi with cellular networks. Standardization is also ongoing to

integrate two networks and allow mobility between WiFi and cellular networks.


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Overall conclusions

• Capacity constraints only appear in hotspots

–Selective upgrades necessary

• LTE not economically viable

–From current capacity demands perspective

–Other reasons for deploying now: first to market and future

• Even WiFi offload needs careful management

–Leasing WiFi bandwidth can be expensive

• Variety of tools available to operator

–Policy, optimization, offload are some examples


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11/09/2009

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30/03/2011

Thank you.

Dimitris MavrakisSenior Analyst, NetworksInforma Telecoms & Media

Email: [email protected]

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LTE Offloading