ran dimensioning: lessons learned by telstra

June

1CLIENT CONFIDENTIAL

Copyright © Maravedis-Rethink 2015

RAN dimensioning: Lessons learned by Telstra

April 30, 2015

Copyright © Maravedis -Rethink 2015

• Caroline Gabriel, Research Director, Maravedis-Rethink

• Mr. Max Downey, SME Capacity Management, Telstra

• Dr. Bruce Northcote, CEO, Telari

• Q&A

AgendaApril 2015

June

3CLIENT CONFIDENTIAL

Copyright © Maravedis-Rethink 2015

The capacity challenge moves on to customer experience

Caroline Gabriel, Research Director, Maravedis-Rethink

CLIENT CONFIDENTIALCopyright © Maravedis-Rethink 2013

www.maravedis-bwa.com

4

Dense networks to meet rising demand, but capacity alone does not create profit

The RAN capacity explosion

April 2015

Copyright © Maravedis Inc 2015.


• Capacity crunch and revenue crunch –a new approach to the RAN is essential• New ways to increasing real capacity –hardware capex heavy and a law ofdiminishing returns• New focus on optimization andresource management• Soft, dynamic capacity key to survivalof the MNO model

Two key differences from the past:

• More elements to optimize• Different objectives driven by CEM

Growth in mobile broadband capacity investment relative to incremental revenues for a typical developed market MNO(relative to capex baseline of ‘1’ in 2013)

0

0.2

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1.4

1.6

1.8

2013 2014 2015 2016 2017 2018 2019

Gro

wth

re

lati

ve t

o c

apac

ity

= 1

Capacity investments Data revenues



5

Need to dimension and optimize many types of cell equipment in dense networks

The new RAN – danger of chaos?

April 2015



% of carriers adopting each of the key architectures as their primary approach , MaRe January 2015

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2014 2015 2016 2017 2018 2019

% o

f ca

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rs a

do

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ng

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ap

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ach

as

pri

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y

Macro-first Carrier WiFi Small cell HetNet layer RAN virtualization

OR



6

Capacity not an end in itself, network drivers shifting to real world experience

The new KPIs – customer-centric RANs

April 2015



• Fewer operators measure RAN successjust with the three Cs – cost capacitycoverage• Hardware cost considerations shift toTCO and ROI, including software• QoE key competitive differentiator,features in two of top 5 network KPIs• Poor network QoE – second biggestreason for churn after cost• QoE may also support higher ARPU, newtypes of service eg enterprise, safety• Need to distinguish high value customersor apps

KPIs named as most important for RAN (top 5 identified in CTO survey, then respondents selected most important of those) Source MaRe survey March 2015

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2518

16

14

Lower TCO

QoE to reduce churn

Analytics

QoE to support newrevenues

Capex reduction



7

Large amounts of capacity locked up by static architectures

Key ways to achieve best experience

April 2015



Significant ways to make more from current networks, and plan new ones

• Unlock soft capacity and underused resources• Map capacity to demand by time, location, app• Dimensioning as part of broad optimization picture• Link RAN data to OSS/BSS• Dynamic resource allocation

Results

• Cost savings• Deferred hardware capex• Higher customer satisfaction• Lower churn• Support for new business cases



8

Shift in emphasis from sites to customer experience in real terms

New priorities in capacity management

April 2015



From this To this

TELSTRA T

EM

PLATE 4

X3 B

LU

E B

ETA |

TELPPTV4

RAN DIMENSIONING

When investing CAPEX in capacity related RAN infrastructure, we must try to

balance a number of competing goals:

• Maximise the customer experience

• For the largest number of customers

• With the most effective use of invested capital

• Through the prioritised expansion of network hardware driven by business needs.

Pr

es

ent

ati

on

Titl

e |

Pr

es

ent

er

SO, WHAT’S THE POINT?

SO, WHAT’S THE POINT?

Budget

($$$)

Scenario Modelling

Devil Engineers

Business Requirements

(… or, what are we buying?)

$

Reporting/Forecasting

Scenario List

We want to be able to know how

much a certain level of network

performance/customer

satisfaction will cost.

Accurate reporting closes the feedback loop

allowing us to both:

• Describe how specified ‘Business

Requirements’ will affect network

performance

• Validate forecasts by monitoring

actual network performance.

Translate business

requirements into capacity

actions.

• Maximise the customer

experience

• For the largest number of

customers

• With the most effective use of

invested capital

• Through the selective expansion

of network hardware driven by

business needs.

DIMENSIONING CONTROLS

Un-optimised Optimised

$

Capacity

Modelling

The setting of Network Controls needs to be translated into a set of required actions

(what should be built and where, etc.).

The impact of these network actions needs to be modelled, so that future network

performance can be forecast.

There are two main types of modelling/analysis that drive this stage of the capacity

process:

• Intrinsic Analysis (automatic, based on well defined rules)

• Extrinsic Analysis (qualitative, based on human interpretation of business

goals and local knowledge)

It’s important to have well defined and documented business rules and processes

that define how CAPEX should be prioritised. The rules are interpreted and

implemented by human engineers.

WELL DEFINED PROCESSES

Extrinsic Prioritisation

Intrinsic Prioritisation Capacity Engineers

Revised

IT’S GETTING HARDER

Complexity is increasing:

- AMPS – just voice, Erlang-B

-GSM – Voice and GPRS/EDGE

-WCDMA – CS12, CS64, PS-R99, PS-HSDPA, PS-EUL

-LTE – Data, VoLTE, ViLTE, Over the top (QCI ???)

-5G – Internet of things, M2M -Terrifying

14PRESENTATION TITLE | PRESENTER NAME | DATE |

THE AMOUNT OF TRAFFIC WE'RE CARRYING IS INCREASING


[Cisco]

http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html

THE NUMBER OF NETWORK ELEMENTS ARE INCREASING

• HetNets

• PicoCells

• 5G millimetre Wave (+100GHz)

• Excel based dimensioning tools are not scalable.


INCREASED SERVICE AND PRODUCT COMPLEXITY

• VoLTE

• ViLTE

• eMBMS

• Differentiated QoS

• Etc.


IMPORTANT CONSIDERATIONS

Finally, any good capacity model should consider the following:

• Just in time capacity

• User Experience KPIs

• Soft Capacity


Thank you

Questions?


Current measured load


Offered Load

Ca

rrie

d L

oa

d

System capacity

y=x

Zero loss

capacity

Dimensioned capacity:

Target KPI is x% loss

Sa

fe o

pe

ratin

g r

ang

e:

Ad

mis

sio

n R

eg

ion

(AR

)

Tracking HR over time

allows me to plan for a

“just in time” capacity

expansion

100% user

satisfaction

Lowest user satisfactionI have this much

spare capacity: my

headroom (HR)


Above this load

I will not meet

my target KPIs

1. Coverage: not a capacity management KPI

2. Resource utilisation (e.g. backhaul bandwidth, downlink power, interference

budget)

3. Service availability

4. Service retention (impacted by QoS policy, congestion control)

5. Throughput: timescales matter

a. Peak user throughput (single user speed test)

b. User throughput (speed test distribution)

c. Interactive throughput (throughput measured only when the user is interacting)

KPIS: SYSTEM-BASED VERSUS USER-EXPERIENCE-BASED

User Experience KPIs


I can dimension my network to meet any

given set of these user experience KPIs.

The KPIs then provide the reference that

defines my network capacity.

System resources are shared dynamically between multiple simultaneous

instances of a number of services.

May apply different KPIs to each service (stringent voice, lax data).

24

CAPACITY IN MULTI-SERVICE CELLULAR NETWORKS

Ascom Webinar, 2015 © Ascom

Signalling Load

Data

Lo

ad

Admission Region: all operating

points within the region should

have achieved the KPIs

Engineered capacity

Engineered capacity

Exact shape of the admission

region is unique to each cell & can

only be determined by modelling

Soft capacity: different radio conditions

provide for more or less capacity

MODELLING SOFT CAPACITY


ARs defined by user experience KPIs on service availability, retain-ability and

interactive uplink & downlink throughput

1. Spectrum (WCDMA codes; LTE PRB)

2. Power

3. Scheduler algorithm

4. QoS Settings & Call Admission Control (CAC)

a. Allocation, Retention & Prioritisation policy (ARP)

b. Resource reservation

5. Radio environment (weather, user location, user demand & interference)

6. User limits & licencing

7. Baseband (computational processing limits)

8. Backhaul bandwidth

FACTORS AFFECTING RAN CAPACITY


Delivering Optimized Capacity Management

Understanding current network load using network OSS data, including soft capacity and resource allocation

Visualizing available capacity or admission region with “Headroom” for different per service growth forecasts

Predicting and alerting when cells will reach capacity

Identifying capacity constraining resources

Providing configuration recommendations to maximize capacity from the existing network

Enabling Scenario analysis: the impact of different forecasts or network expansion options

Enabling Business Decisions

Just in time capital planning

Prioritization: network or regional level “top N” lists

Prioritization: revenue contribution per cell

Consistent systemized planning across business

27

INTRODUCING TEMS™ CAPACITY MANAGER

Ascom Webinar, 2015 © Ascom

Example screenshots from Ascom’s

TEMS™ Capacity Manager

[ ]

28Ascom Webinar, 2015 © Ascom

THANK YOU FOR ATTENDING!

For more information on TEMS™ Capacity Manager, visit us at www.ascom.com/networktesting

http://www.ascom.com/networktesting

ran dimensioning: lessons learned by telstra

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