Security Level：Limited distribution

ETSI Workshop 20150603-05

Methods to estimate future Radio Access Network (RAN) deployment and energy consumption. Case study European network p y preducing energy consumption while supporting traffic evolution.

www.huawei.com

Tomas Edler

Rev B

o as d eSenior Expert Energy EfficiencyHuawei Technologies Sweden

tomas.edler@huawei.com

HUAWEI TECHNOLOGIES CO., LTD.

Content

Introduction RAN (Radio Access Network)

evolution energy assessment RAN Evolution studies examples European theoretical country RAN 2010-2020

European country RAN 2014-2020

Huawei global trends study 2010-2030

Proposal for ETSI EE EEPS

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Introduction Can operators mitigate traffic growth with RAN energy efficiency? Depends on traffic evolution, network evolution strategy, assumptions and used method Results from 3 different studies: Results from 3 different studies:

– Different methods (theoretical country, physical country, global trends)– Different areas (countries, global)( g )– Different network evolution strategies– Different period. 2010-2020, 2014-2020, 2010-2030

ETSI EE has standardized methods to assess energy efficiency of RAN equipment and legacy RAN. We propose a new work item to find feasible methods to estimate energy performance for evolution of a current RANperformance for evolution of a current RAN.

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RAN Evolution Assessment impact factors p

Spectrum Strategyp gy Technology Roadmaps (Standards, Radio concepts, features) Traffic Evolution (services, devices) Area (Topology, Population distribution..)Area (Topology, Population distribution..) RAN/Base Station (Base Station types: Macro/HetNET, Cloud-RAN, Site: Site efficiency...) RAN capacity utilization RAN capacity utilization Known and unknown (disruptive) factors

– Historic: SMS, Smartphones,

Future?: IoT 8K3D Self driven cars Augmented reality Holographic Video new– Future?: IoT, 8K3D, Self driven cars, Augmented reality, Holographic Video, new

5G services....

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Traffic growth: Exponential Amsterdam Exponential Persistent ~ Moore’s Law Driven tech evolution (smartphones, tablets, laptops,

2G>3G>4G )

Int’l internetExch.Source: bsigroup.com

2G>3G>4G..)

Mobile Data Growth Sweden:90%/Y 2010-2013

2010-2020 estimates CAGR 61-67% 2010 2020 estimates CAGR 61 67%– CISCO: +61%/Y, 117X 2013-2018

– Huawei study: +67%/Y 2010-2020

Beyond 2020 estimates:– METIS: 1000X (2013-2028? = CAGR 61%)

RANRAN

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Efficiency Evolution

kbps/W

1000Trafficgrowth 10x per 5Y

Efficiency

Massive MIMO?10000

5G

HetNet

100LTE

LTE-ALTE-NG

1000 growth 10x per 5Y (60%/Y)evolution.

Transferred bits/W

End of Moore,s Law?

10

WCDMA/DCH

WCDMA/HSPA

bits/W

Total cell average

0.1

1

GSM

EDGE

WCDMA/DCH

GPRS Substantial improvement of spectral & BS eff

”Less to win” on BS eff.Close to Shannon

(high load)EC of processing must be reduced.New Paradigm!Hybrid Analog/Digital?

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Efficiency factors: Dynamic Power ManagementEnergy consumption must scale with load

Power Consumption- not scaling with traffic

In field – large static part Scaling characteristics

Potential for power saving

g Scaling characteristics Sleep modes Shut down of redundant

p g- scaling with traffic

resources Dynamic design

Source: Bath Green Radio Conference 2009, Orange presentation

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2010-2020 Europe RAN Evolution study

100000,0

Typical mature central EU countries Analysis area 100.000 sqkm

Theoretical study – equal cell load per area

PS traffic growth,

1000,0

10000,0Subs/ Sqkm

Subs/ SQk

67%/Y, 170X in period Traffic growth from a PS

(Packet Switch) resource view M lti RAT l ti 2 4G

10,0

100,0

DU U SU R

SQkm Multi-RAT evolution 2-4G Spectrum refarming Higher utilization of RAN

DU0,2% U

11%

R27%

% of sites DU2%

R20%

% of subs

4

5

ISD, km

SU62%

U42%

SU36%

20%

0

1

2

3

ISD, km

90% ofsites

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DU U SU R

Traffic 2010-2020 Traffic Evolution, Busy Hour traffic

Year 2010 2012 2014 2016 2018 2020 GrowthTotal kbps 0,56 0,84 1,48 3,14 7,63 20,11 35,7PS kbps 0,113 0,316 0,881 2,46 6,88 19,21 170,0CS kbps 0,45 0,525 0,6 0,675 0,75 0,9 2E BH 0,015 0,015 0,015 0,015 0,015 0,015 1

/

Voice coder data more demanding.12.2 kbps voice = 30kbps (GSM)-75kbps(HSPA) kbps

”Equal PS resource” for 2G/3G mix considered

CS kbps/E of  PS 30 35 40 45 50 60 2

PS Total36XGrowth

PS data growth 170X Total traffic growth only 36X as CS voice

10,00

Total kbps

CS

SubscriberTraffic evolution

170XGrowth+67%/Y

Growth Total traffic growth only 36X as CS voice is dominating until 2013 – considering the QoS of voice coder bits

1,00CS kbps

CS kbpsLoad

% Daily Traffic Profile

0,102010 2012 2014 2016 2018 2020

Voice Voice X-over

302010

Hr/Day

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80% 5%2013

Spectrum & Equipment refarming2010 RAT/BW 2020 RAT/BW

Band Macro BS Macro/DBS Ba

ndNote

DU U SU R DU U SU R

2012 HW

refresh

2013-2020 New RAT/BAND/HW

2010 RAT/BW 2020 RAT/BW

7 - - - - L10 - - - 7 Cap & Coverage

8 - - - - L10 - L10 L10 8 Cap & Coverage

New

New

Coverage

9 G4C G2C G2C G2C G3CU5

G2CU5

G1CU5

G1CU5

9 GU Multi-RAT Radio Units

18 G4C G2C G2C G2C L20 L20 - - 18 CapNew

21 U5 U5 U5 U5 U20 U5 - - 21 Cap

26 L20 - - - 26 Cap

Micro/Pico BS Micro/Pico BS

New

New

21 - - - - - U10 - - - 21 Cap

100% Macro 50% Macro Reuse of sites

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Base Stations 50% DBS+u-Base stations

Low hanging fruits Moore’s law vs trends

(2X eff in 18 months) Cell capacity utilization in SU, R

100%

G

Busy Hour Load 2010

50% 90% of sites

DU U SU R

U

DU U SU R

Busy Hour Load 2020 100%

Source:IEEEcomputersociety.org/10.1109MAHC.2010.28

G

U

L

50% Low utilization

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DU U SU R

RAN Results Total energy saving 50% 450000

500000

MWh/Y Energy Consumption, 100.000 Sqkm Network

Total energy saving 50% Improvement mechanisms

- Eff gains 2G – 4G: ”RAT” gain, Single carrier =>

multi carrier, Flexibility of multi RAT BS

Refarming of spectrum and Hardware 300000

350000

400000EC, 65% CAGR

EC, 65% ESF*

- Refarming of spectrum and Hardware

- Utilization eff gain

- Energy saving features

- ”Dynamic power management” ie shut down of

d d

200000

250000

2010 2012 2014 2017 2020

TB/MWhredundant resources.

60,00

70,00

80,00

165kb/JEff gain 70X (avg)39-77XDU SU

TB/MWh

Total energy saving 50% Traffic growth PS only 170X Traffic growth ”total”: 36X 30 00

40,00

50,00USU RDU

DU-SU

g Total Efficiency gain: 70X

0 00

10,00

20,00

30,00

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0,002010 2012 2014 2017 2020

2014-2020 Europe RAN Evolution study

Central European country 2014: Actual field RAN energy consumption 2014: Actual field RAN energy consumption

and traffic 2020 traffic evolution:

2G decline2G decline 3G growth 4G massive growth.

Solution A: No swap of RAN equipment Energy consumption will

Solution B Refresh of RAN equipment Band refarming

H tN t l ti i iti d f it Energy consumption willincrease by 2020

HetNet solutions prioritized for capacity growth

Energy consumption may reduce by 2020

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Evolution of electricity usage of Wireless access networks (Ew ) 2010-2030

70

80

90

100

Best case electricity usage (TWh) of Wireless Access Networks 2010–2030 (Ew)Global ICT electricity usage based on anticipated user traffic limits , extrapolation of global traffic and energy

1420

30

40

50

602G/3G voice

2G data

3G data

4G data

5G data

global traffic and energy efficiencyevolution trends. 3 scenarios: Best, expected and worst case

n

2G,2010n2010n2010n2010,G2 100%)EE%100(EI12MDTG2SE

n

V,2010n2010n2010,Voice 100%)EE%100(EI12VTE

42

14

13

0

10

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030Year

Expected case electricity usage (TWh) of Wireless Access Networks 2010–( )

and worst case.

n

nnnGEEMDTSNGE

100%)%100(EI12 3G,2010201020102010,3 91

80

100

120

140

2030 (Ew)

2G/3G voice

Energy Consumption 3-5G (=N)Tot: 205 106 192

Source: Andrae, A.S.G.; Edler, T. On Global

Electricity Usage of Communication

Technology: Trends to 2030. Challenges

2015, 6, 117-157

23

70

0

20

40

60

80 2G/3G voice

2G data

3G data

4G data

5G data

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, ,

. http://www.mdpi.com/2078-1547/6/1/11702010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

Proposal for ETSI EE EEPS

New WI e Methods how to deploy and assess an energy efficient

future RAN, including expected impact of evolution of traffic, services and networks

First task: Review current methods, studies and results to find a best practice for prediction.p p

Result presented as a Technical Report Second task: Define a guideline standard.

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Thank YouThank You

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