PhD Thesis Defense Seminar on "On the Design of Energy Efficient Wireless Access Networks"

1. 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

2. 4 Conclusion
2 Thesis Focus & Research Questions
1 Motivation
3 Key Findings
Outline

3. Motivation
3
1000X mobile traffic
+ +
10-100X # of
connected
devices
10-100X
end user data rate
+
5X lower latency..
Affordable and Sustaniable

4. 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!

5. 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.

6. 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!

7. 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= ∆ +

8. 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!
+
+

9. 4 Conclusion
2 Thesis Focus & Research Questions
1 Motivation
3 Key Findings
9

10. 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?

11. 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.

12. 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.

13. 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.

14. 2 Thesis Focus & Research Questions
1 Motivation
3 Key Findings
4 Conclusion
14

15. 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.

16. Energy Efficiency Assessment
Backhaul Power Consumption
16
 Backhaul solution: technology; topology.
 Mobile radio network deployment: homogenous, heterogeneous.
 Deployment areas: urban, rural.
 Expected traffic growth.
0
1
( ) backhaul
m
tot i p tx
i
P N P P P
=
= ∆ + +∑
0
1
( )
m
i i
tot i p tx
i
P N P P
=
= ∆ +∑
 Is backhaul becoming a bottleneck for green wireless access networks?

17. Energy Efficiency Assessment
Backhaul Power Consumption
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.0 100 200 300 400 500 600
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
x 10
4
Area Throughput[Mbps/km2
]
AreaPowerConsumption[Watt/km2]
HetNet deployment
2010 2012 2014 2016 2018 2020 2022
0
5
10
15
20
25
Year
AreaPowerConsumption[KWatt/km2]
With Backhaul
Without Backhaul
Hetnet
Backhaul impact shifts the point where
Hetnet's beneficial
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Year
AreaPowerConsumption[Watt/km2]
wireless only
Pbh
arch1
Pbh
arch2
Pbh
arch3
η ∈[0.1, 0.6] is increasing with the same
rate between 2010 and 2020
backhaul impact ~50%

18. Energy Efficiency Assessment
Backhaul Power Consumption
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.

19. Energy Efficient Solutions
Architectural
19
0 5 10 15 20 25 30
0
500
1000
1500
2000
2500
NBS
AreaPowerConsumption(Watt/km2)
Transmit
power
dominant
Baseline power
is 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 arg
1
( ) (W,C ,R,..)backhaul
m
tot i p tx t et
i
P N P P P f
=
= ∆ + + =∑
0 5 10 15 20 25 30
0
500
1000
1500
2000
2500
NBS
AreaPowerConsumption(Watt/km2)
5Mbps/km2
20Mbps/km2
Higher capacity
demand

20. Energy Efficient Solutions
Architectural
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 4000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Distance (m)
bits/s/Hz/km2
macro
macro + 3 micro
macro + 5 micro
Find ISD that can satisfy the
requirement
1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000
300
400
500
600
700
800
900
1000
1100
1200
1300
Distance (m)
Watt/km2
macro
macro + 3 micro
macro + 5 micro
Find the optimum ISD that
minimizes APC
ˆmin(D, *)optD D=
ˆD *D
Less need for macro densification

21. Energy Efficient Solutions
Architectural
21
Scenario: Outdoor small cell deployment under
macro-cellular coverage.
 Uniform traffic scenario.
 Full buffer traffic.
2 4 6 8 10 12 14
300
400
500
600
700
800
900
Area Throuhput [Mbps/km2
]
MinimalAreaPowerConsumption[Watt/km2]
macro
macro + 3 micro
macro + 5 micro
macro + 3 pico
macro + 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. Energy Efficient Solutions
Architectural
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 6
0
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
)
PowerSavings(%)
Macro+Femto ρ=0.2
Macro+Femto ρ=0.6
Macro+Femto ρ=1
Beyond 2015
~2010

23. Energy Efficient Solutions
Operational
23
 How much we can save through Cell DTX for a
given traffic pattern?
( )
24
0 0
1 1
1
( ) (1 )
24
BSN
t t
tot p tx BS i i
t 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 24
0
50
100
150
200
250
T [Hours]
AreaPowerConsumption[W/km2]
Saving due to Cell DTX for a
given 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 24
0
50
100
150
200
250
T [Hours]
AreaPowerConsumption[W/km2]
Saving due to energy-aw are
deployment

24. 5 10 15 20 25 30 35 40 45 50
0
500
1000
1500
2000
2500
Number of BSs
TotalCost[kEuro]
0 20 40 60
0
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.

25. Analysis on Energy-Cost Tradeoff
Economic Viability Analysis
25
 Under which circumstances will an operator will get a total cost
saving from EE solutions?
1
i i i
tot BS
ref ref ref
tot BS
C c N
C c N
= 
 Methodology
 Economic viability method using NPV
1
1 (1 )
N
n
n
n
c
c
d −
=
=
+
∑
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

26. ( )
11
(1 )
ref
N n n n
BS cnn
e E E
N
d −=
−
> ∆
+
∑
Analysis on Energy-Cost Tradeoff
Economic Viability Analysis
26
Existing Deployment
 Solution: Hardware upgrade with cell DTX capability
0
0.5
1
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
δen
[Euro/kWh]
Break-evencost(∆c/ccapex)
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 even
c capexc−
∆ ≈ ×
0.42break even
c capexc−
∆ ≈ × ( )
11
(1 )
ref
N n n n
BS cnn
e E E
N
d −=
−
> ∆
+
∑

27. Analysis on Energy-Cost Tradeoff
Economic Viability Analysis
27
Greenfield Deployment
 Solution: Energy efficient greenfield network deployment
( )1 2
1 11
1
( , ) ( , ) ' ( )
( )
(1 ) (1 )
ref i N
N n tot tot opexe c
BS BS capext nn
n
e E t E t c t
N N c
d d
ρ ρ
− −=
=
−  
> − + 
+ + 
∑ ∑
Saving
Additional Cost
0
5 10
15
0
5
10
0
5
10
15
20
25
30
Time [Years]1/en
[kWh/Euro]
KEuro/km2
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
0
5
10
0
5
10
15
20
25
30
Time [Years]1/en
[kWh/Euro]
KEuro/km2
Energy Saving
Incremental increase in CAPEX
0
5 10
15
0
5
10
0
5
10
15
20
25
30
Time [Years]1/en
[kWh/Euro]
KEuro/km2
Energy Saving
Incremental increase in CAPEX
Incremental increase in OPEX
0 2 4 6 8 10
10
20
30
0
1
2
3
4
5
copex
[KEuro]ccapex
[KEuro]
Breakevencostofelectricity[Euro/kWh]

28. 2 Thesis Focus & Research Questions
1 Motivation
3 Key Findings
4 Conclusion

29. 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. 30
Thank you for your attention.
Any questions?.... OR NOT?