ph d thesis_seminar_on the design of energy efficient wireless access_sibeltombaz

On the Design of Energy Efficient Wireless Access Networks

Ph.D. Thesis Defense

Ph.D. Candidate: Sibel Tombaz

Advisor: Prof. Jens Zander

Co-advisor: Dr. Ki Won Sung Opponent: Prof. Timothy O'Farrell Committee: Dr. Ylva Jading Prof. Di Yuan Prof. Mikael Johansson

23 May 2014

4 Conclusion

2 Thesis Focus & Research Questions

1 Motivation

3 Key Findings

Outline

Motivation

3

1000X mobile traffic

+ +

10-100X # of connected

devices

10-100X end user data rate

+ 5X lower latency..

Affordable and Sustaniable

Motivation

4

Today Future

Mobile radio networks are responsible of 10-15% of ICT. 15 nuclear plants.

ICT is the 5th largest (3%) in electricity consumption. Co2 emissions is comparable to the global

aviation industry.

We are beginning of the new era.

Energy consumption of mobile networks increases ×2 every 5 years. Unit energy cost: x3 in 7

years!

Why to Save Energy?

5

• Energy constitutes up to 50 percent of operators’ OPEX.

Energy efficiency= OPEX efficiency

Lack of Electricity Supply

• Average temperature is increasing annually. • Government motivates CO2 reduction. Climate Change

• Continuation of the global success of ICT needs to be enabled. Sustainable growth

• Grid availability is challenging. • Delivering fuel to off-grid sites is costly.

High Level Challenges Equipment Level

6

BS equipment are optimized for full load. • Lack of load adaptation. • Operate at suboptimal points most of the

time.

• Technical drawbacks towards green wireless access networks.

Energy waste!

High Level Challenges Node Level

7

In average BSs are not serving any users more than 50% of the time during a year. • Still consuming high idle power consumption

• Technical drawbacks towards green wireless access networks.

Energy waste!

0( )BS TRX p txP N P P= ∆ +

High Level Challenges Network Level

8

Equipment Level

Node Level

Low utilization

Very low utilization

Network Level • Deployment has been done for

coverage and capacity; ready for the worst case.

• Demand for high data rates limits

the resource utilization.

• Significant spatial variation. • 80% of the BSs carry only 20%

of the traffic.

• Almost constant power consumption.

Energy waste!

+

+

4 Conclusion


1 Motivation

3 Key Findings

9

Thesis Focus & Research Questions

10

We focus on the network level problems with 3 main high-level questions:

1. How to assess the energy efficiency of a given network?

2. How should wireless access networks be deployed and operated in an energy efficient manner?

3. What are the consequences of energy efficient solutions on total cost?

Literature Review & Research Gap

11

Energy Efficiency Assessment Metrics: Various green metrics are proposed to quantify the energy efficiency. Misuse of the metrics result in contradictory and debatable conclusions.

Models: Accurate power consumption models for BSs are proposed by EARTH. The impact of mobile backhaul has been mostly ignored.

Energy Efficient Solutions Architectural Solutions : Various homogeneous and Hetnet network deployment solutions are proposed to minimize the energy consumption. However, contradictory conclusions exist: densification, indoor small cell deployment.

Fairness has been overlooked.

Literature Review & Research Gap

12

Energy Efficient Solutions Operational Solutions : Long-term sleep mechanisms aim to match the network

capacity with the actual traffic demand have been widely investigated. However, dynamic short-term solutions have been overlooked. The coupling relationship between deployment and operation has been

ignored.

Energy-Cost Tradeoff Analysis Economic benefits has been limited to annual OPEX saving. Energy Saving ≠ Total Cost Saving

Main sources of total cost and their relationship are missing. There is no methodology for assessing the economic viability of any EE solution.

Overview of the Contributions

13

1. Energy Efficiency Assessment - Metrics: Identify key aspects to be considered to avoid misleading results.

• How to get coherent results. - Models: We propose novel backhaul power consumption models and highlighted

its importance.

2. Energy Efficient Solutions - Identify the main design parameters and their impact on energy efficiency. - Quantify the potential energy savings through clean-slate Hetnet solutions. - Develop a methodology to assess the achievable saving through fast traffic

adaptive solutions (i.e., cell DTX). • Highlighted the benefit of holistic design (Deployment+Operation)

3. Energy-Cost Tradeoff Analysis

- Introduce the total cost perspective. • Propose a high-level framework capturing the main cost elements.

- Assess the economical impact of different solutions. • Viability analysis incorporating initial investment cost.


1 Motivation

3 Key Findings

4 Conclusion

14

Energy Efficiency Assessment Metrics

15

How to avoid misleading conclusions? • Identify right indication of how to achieve green wireless networks.

Bit per Joule Area Power Consumption

max min totEE E≠

To prevent contradictory conclusions both coverage and capacity requirements should be taken into account.

Energy Efficiency Assessment Backhaul Power Consumption

16

Backhaul solution: technology; topology. Mobile radio network deployment: homogenous, heterogeneous. Deployment areas: urban, rural. Expected traffic growth.

01

( )backhaul

m

tot i p txi

P N P P P=

= ∆ + +∑

01

( )m

i itot i p tx

iP N P P

=

= ∆ +∑

Is backhaul becoming a bottleneck for green wireless access networks?


17

Urban Areas Macro+only densification Heterogenous deployment

Tradeoff between power saved by using smaller cells and idle power for backhaul.

Backhaul highly impacts the energy efficient solution.

Almost 50% is consumed for backhaul for

dense deployment in 2020. Switch power is dominant.

Fiber based solutions generally outperforms

the other solutions. 0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Area Throughput[Mbps/km2]

Are

a P

ower

Con

sum

ptio

n [W

att/k

m2 ]

HetNet deployment

2010 2012 2014 2016 2018 2020 20220

5

10

15

20

25

Year

Are

a P

ower

Con

sum

ptio

n [K

Wat

t/km

2 ]

With BackhaulWithout Backhaul

Hetnet

Backhaul impact shifts the point whereHetnet's beneficial

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 20200

2000

4000

6000

8000

10000

12000

14000

16000

18000

Year

Are

a P

ower

Con

sum

ptio

n [W

att/k

m2 ]

wireless only

Pbharch1

Pbharch2

Pbharch3

η ∈[0.1, 0.6] is increasing with the samerate between 2010 and 2020

backhaul impact ~50%


18

Rural Areas Macrocell deployment Outdoor small cell deployment

Backhaul impact is limited only for macro BS deployment.

Small cell deployment can still provide lower consumption when the demand is high.

Wireless backhaul solutions are the least energy efficient.

Backhaul will be responsible of remarkable share. It has to be included into the analysis.

Wireless MBH solutions

Coverage and capacity requirements are changing over the years.

Energy Efficient Solutions Architectural

19

0 5 10 15 20 25 300

500

1000

1500

2000

2500

NBS

Area

Pow

er C

onsu

mpt

ion

(Wat

t/km

2 )

Transmitpowerdominant

Baseline poweris dominant

Higher baseline

Tradeoff creates an optimum densification level.

Idle power consumption is a key parameter

to define the optimum point. Higher capacity requirement favors

densification. Careful prediction is the key.

What are the key design parameters? How they impact the energy efficiency?

0 arg1

( ) (W,C ,R,..)backhaul

m

tot i p tx t eti

P N P P P f=

= ∆ + + =∑

0 5 10 15 20 25 300

500

1000

1500

2000

2500

NBS

Area

Pow

er C

onsu

mpt

ion

(Wat

t/km

2 )

5Mbps/km2

20Mbps/km2

Higher capacitydemand


20

How much we can save through Hetnet solutions? Type of small BSs. Number of small BS per macro BS. Capacity requirement. Indoor/outdoor user split.

Methodology Ensure that each strategy has the same performance.

1000 1500 2000 2500 3000 3500 40000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Distance (m)

bits

/s/H

z/km

2

macromacro + 3 micromacro + 5 micro

Find ISD that can satisfy therequirement

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000300

400

500

600

700

800

900

1000

1100

1200

1300

Distance (m)

Wat

t/km

2

macromacro + 3 micromacro + 5 micro

Find the optimum ISD that minimizes APC

ˆmin(D, *)optD D=D̂ *D

Less need for macro densification


21

Scenario: Outdoor small cell deployment under macro-cellular coverage.

Uniform traffic scenario. Full buffer traffic.

2 4 6 8 10 12 14300

400

500

600

700

800

900

Area Throuhput [Mbps/km2]

Min

imal

Are

a Po

wer C

onsu

mpt

ion

[Wat

t/km

2 ]

macromacro + 3 micromacro + 5 micromacro + 3 picomacro + 5 pico

Traditional deployments is only efficient when the demand is low.

Most efficient design is dependent on the demand.

• 30% saving is feasible.


22

Scenario: Indoor small cell deployment under macro-cellular coverage.

Non-Uniform user distribution. Co-channel deployment.

Significant improvement in user SE. • Wall elimination.

Low traffic region Coverage limited • Femto deployment does not pay off.

High data rate requirement reverses the

conclusion. Blasting the signals over the walls is shown

to be neither energy efficient nor feasible to satisfy the growing capacity demand.

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User Spectral Efficiency [bps/Hz]

CDF

ρp=0

ρp=0.2

ρp=0.4

ρp=0.6

ρp=0.8

ρp=1

macro-femto

macro-only

10 100-60

-40

-20

0

20

40

60

80

Area Throughput (Mbps/km2)

Powe

r Sav

ings

(%)

Macro+Femto ρ=0.2Macro+Femto ρ=0.6Macro+Femto ρ=1

Beyond 2015

~2010

Energy Efficient Solutions Operational

23

How much we can save through Cell DTX for a given traffic pattern?

( )24

0 01 1

1 ( ) (1 )24

BSNt t

tot p tx BS i it i

P P N P Pη δ η= =

= ∆ + + − ∑∑

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

50

100

150

200

250

T [Hours]

Area

Pow

er C

onsu

mpt

ion [W

/km

2 ]

Saving due to Cell DTX for agiven deployment Optimized deployment + apply cell DTX

Significant saving.

Incorporating cell DTX at the planning stage brings 40 percent more saving. By deploying slightly faster than

actual requirements.

Cell DTX

We proposed a Quantitative method

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

50

100

150

200

250

T [Hours]

Area

Pow

er C

onsu

mpt

ion [W

/km

2 ]

Saving due to energy-awaredeployment

5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

Number of BSs

Tota

l Cos

t [kE

uro]

0 20 40 600

10

20

30

40

50

Energy Cost

Total Cost Optimum

Analysis on Energy-Cost Tradeoff Main Tradeoffs

24

How does the key cost elements impact the future design of green networks?

infra (N , W,P )tot spectrum energy BS totC C C C f= + + =Key cost elements:

More spectrum.

Energy minimization ≠ Cost minimization Spectrum has the significant impact on energy and cost. Secondary spectrum access, high frequencies can be the solution.

Analysis on Energy-Cost Tradeoff Economic Viability Analysis

25

Under which circumstances will an operator will get a total cost saving from EE solutions?

1i i itot BSref ref reftot BS

C c NC c N

=

Methodology Economic viability method using NPV

11 (1 )

Nn

nn

ccd −

=

=+∑

Existing Deployment Solution: Hardware upgrade with

cell DTX capability Investment cost per BS:

Analysis: What is the break-even cost of

new hardware?

Greenfield Deployment Solution: Energy efficient deployment

Have higher initial investment cost. Analysis:

How expensive energy cost must be EE design brings TCO saving?

c∆

2 Case Studies

Both CAPEX and OPEX

( )11

(1 )

refN n n n

BS cnn

e E EN

d −=

−> ∆

+∑


26

Existing Deployment Solution: Hardware upgrade with cell DTX capability

0

0.5

1

00.2

0.40.6

0.810

0.2

0.4

0.6

0.8

1

δ en [Euro/kWh]

Bre

ak-e

ven

cost

( ∆c/

ccape

x )

Mature markets

Developing countries

Upgrading the hardware is more cost-effective as energy cost increases. Significant saving by cell DTX (up to 60%) can compensate the additional investment

cost.

0.08break evenc capexc−∆ ≈ ×

0.42break evenc capexc−∆ ≈ × ( )

11

(1 )

refN n n n

BS cnn

e E EN

d −=

−> ∆

+∑


27

Greenfield Deployment Solution: Energy efficient greenfield network deployment

( )1 21 11

1

( , ) ( , ) ' ( )( )

(1 ) (1 )

ref i NN n tot tot opexe c

BS BS capext nnn

e E t E t c tN N c

d dρ ρ

− −==

− > − + + +

∑ ∑Saving Additional Cost

0 5 10 150

5100

5

10

15

20

25

30

Time [Years] 1/en [kWh/Euro]

KEur

o/km

2

Energy Saving Clean Slate energy efficient deployment

might compensate the additional capital investments. However, annual increase in OPEX

reverses the situation.

Over-dimensioning might not be motivated for total cost saving. Unless operators get additional

benefits from energy saving. 0 5 10 15

05

100

5

10

15

20

25

30


KEur

o/km

2

Energy SavingIncremental increase in CAPEX

0 5 10 150

5100

5

10

15

20

25

30


KEur

o/km

2

Energy SavingIncremental increase in CAPEXIncremental increase in OPEX

0 2 4 6 8 10

1020

300

1

2

3

4

5

copex [KEuro] ccapex [KEuro]

Brea

k ev

en c

ost o

f ele

ctric

ity [E

uro/

kWh]


1 Motivation

3 Key Findings

4 Conclusion

Conclusions

29

1. How to assess the energy efficiency of a given network?

Energy efficient does not always mean lower energy consumption. Both coverage and capacity should be considered to avoid disputable indication.

Backhaul will potentially become a bottleneck for future green networks. Technology and topology choices are highly important.

2. How should wireless access networks be deployed and operated in an energy efficient manner?

Energy-oriented clean slate deployment brings significant savings. Key design parameters: Idle/transmit power ratio, capacity requirement.

Cell DTX is a promising hardware improvement enabling up to 60% savings. Consider the fact: Deployment and Operation are highly dependent.

3. What are the consequences of energy efficient solutions on total cost?

Energy minimization does not mean total cost saving. Applying sustainable solutions might also bring total cost reduction.

Viability of the solutions must be assessed case by case.

30

Thank you for your attention.

Any questions?.... OR NOT?