Energy and Cost Efficient Ultra-High
Capacity Wireless Access
Sibel Tombaz, Västberg, Jens Zander
Sibel Tombaz, Anders Västberg, Jens Zander
Wireless@KTH,
Royal Institute of Technology,
Stockholm, Sweden
eWIN
• National project – within the Strategic Research Area
”ICT – The Next Generation”
GreenNet Workshop IEEE VTC2011 Budapest
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1
Data consumption trends Tablet Computers
Voice-dominated
consumption
Super
Phones
Smart
Phones
Feature
Phones
Current values
Web-brow. 40MB
Spotify-like 60MB
YouTube 200MB
Predictions
Web-brow. 60 MB per hour
Spotify-like 60MB
YouTube 400MB
Data-dominated
consumption
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Why Energy Efficiency ?
Traffic, $
Traffic, cost
& energy consumption
The Revenue Gap
traffic vs. Telcos’ revenues
decoupling
Telcos’
revenues
Voice dominant
Data dominant
Time
• Energy costs grow even faster than traffic volumes
• Cost is a more effective drive than environmental concerns
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2
Solutions
• Short term – efficient implementation
– Improved efficiency of amplifiers/electronics/antennas/SP
• Medium term – exploit existing standards
– Provide capacity when its needed: Idle power & Transmit Power
Management
– Provide capacity where its needed: HETNETs – infrastructure &
energy spent close to the demand.
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HETNET deployment
(Klas Johansson, ”Cost Effective Deployment Strategies for Heterogeneous Wireless Networks”, Doctoral Thesis, KTH 2007)
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3
Solutions
• Short term – efficient implementation
– Improved efficiency of amplifiers/electronics/antennas/SP
• Medium term – exploit existing standards
– Provide capacity when its needed: Idle power & Transmit Power
Management
– Provide capacity where its needed: HETNETs – infrastructure &
energy spent close to the demand.
• Long term – Clean slate design
–
–
–
–
Revisit constraints: spectrum, infrastructure cost, energy cost
Clean slate design: How would an energy efficient design look like ?
What would a design minimizing the TOTAL cost look like ?
Are we “way off” today
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Key Design Constraints
Energy
C
A: Current designs
B: Femto/Picocell design
C:” Green” design
E
D: Total cost minimization –
abundant spectrum
D
Infra cost
B
C tot = C infra + C energy
GreenNet Workshop IEEE VTC2011 Budapest
A
E: Total cost min – less
spectrum available
Spectrum
+ C spectrum
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Outline – Clean slate design
• Objectives :
– Modeling energy consumption in Wireless Networks
– Simple model to allow design space exploration
– Characterizing trade-offs future architectures
• Two model architectures
– “Green architecture” – providing the required service at
minimal energy cost
– Architectures for total cost minimization
• Conclusions
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Energy consumption modelling
•
Assumptions:
–
–
–
•
Homogenous network constant traffic over cell size
Real network – composed of (tiers of) homogeneous “islands”
Given throughput requirement in area Rtot
Power consumption
Ρtot = N BS [aPtx + bradio + bbackhaul ] + d + yRtot
“Efficiency”
Proportional to #base stations
•Idle power,
•Control msg transmission
• backhaul ..
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Independent of #base stations:
•core network
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HETNET deployment
(Klas Johansson, ”Cost Effective Deployment Strategies for Heterogeneous Wireless Networks”, Doctoral Thesis, KTH 2007)
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Energy consumption modelling (2)
•
Spectrum-Infrastructure Cost-Power Trade-off (Shannon Bound)
Prx (d ) =
•
c' GPtx
dα
Average spectral efficiency

 

N W
Ρtot =  N BS a  o

cG

 

 R NW
α
Ptx =  2W − 1 0 Rcell
cG


S =
 Rt tot

 2 N BSW − 1 Α

 πN BS


GreenNet Workshop IEEE VTC2011 Budapest
R
W
α /2






Rtot 


+d / A
 + bradio + bbackhaul + y

N BS




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“Green” Architecture
• If power/energy is the dominant constraint;
lim Pc ( N BS ) →∝
α >2
N BS →∝
3000
W=1.25 Mhz
W=3.5 Mhz
W=5Mhz
W=10Mhz
Area Power Consumption (Watt/km 2)
2500
W=20Mhz
2000
1500
1000
500
0
0
5
10
15
20
25
NBS
30
35
40
45
50
There is always a non-null and finite that minimizes the
areapower consumption.
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Green Architecture
•
Idle power
PA - efficiency
2000
3000
bradio=0
1800
bradio=100
1600
bradio=300
bradio=1000
2000
Rtot=20Mbps/km2
1500
Rtot=5Mbps/km2
1000
500
Area Power Consumption (Watt/km 2)
Area Power Consumption (Watt/km 2)
2500
bradio=a
a=0
a=5
a=22
a=50
a=100
Rtot=20Mbps/km2
1400
1200
1000
800
Rtot=5Mbps/km2
600
400
200
0
0
5
10
15
20
25
NBS
30
35
40
45
50
0
0
5
10
15
20
25
NBS
30
35
40
45
50
Optimistic model: a(S)= const
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Deployment for Minimal Total Cost
C tot = C infra + C energy

 

N W
Cost = co * N BS + c1  N BS  a  o

cG

 

 Rt tot

 2 N BSW − 1 Α

 πN BS


Infrastructure



α /2
+ C spectrum



Rtot 


+ d  + c2W
 + bradio + bbackhaul + y

N
BS




Energy
Spectrum
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Deployment for Minimal Total Cost
2000
Energy Cost
Infra Structure Cost
Spectrum Cost
Total Cost
1800
1600
Cost (kEuro)
1400
1200
1000
800
600
400
200
0
•
•
0
5
10
15
20
25
NBS
30
35
40
45
50
Minimum total cost now occurs at a much lower number of base
stations than in the energy-only minimization.
Spectrum cost constant – provides only a level shift of the total cost;
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Increasing infrastructure cost
3000
Total Network Cost (kEuro)
2500
c2=0.05MEuro/BS/year
2000
1500
1000
500
infra cost increases
c2=0.02MEuro/BS/year
Rtot
0
•
•
0
5
10
15
20
25
NBS
30
35
40
45
50
Total cost increases – same effect as idle power/backhaul cost
Optimal number of base station is not that much affected.
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Spectrum cost impact
1400
Energy Cost
Infra Structure Cost
Spectrum Cost
1200
Total Cost
1000
kEuro
800
600
400
200
0
•
2
4
6
8
10
12
14
W [Mhz]
16
18
20
22
As the spectrum cost increases, optimum spectrum expenditure moves
closer to the “energy asymptote”
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Energy price dependence
1.8
Optimum Base Station Density (# BS/km2)
W=2 Mhz
W=5 Mhz
1.6
W=10 Mhz
1.4
1.2
1
0.8
0.6
0.4
0.2
-1
10
•
•
0
10
c 1-Cost of Unit Energy [Euro/kWh]
1
10
As the energy cost increase, distinct minimum moves closer to the “energy
asymptote” where denser deployment is better.
More available spectrum always decreases the optimum deployment density.
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Conclusions
• Proposed simple Power/Energy consumption model
allows for “clean slate” design space exploration
Specific findings:
• Increasing spectral efficiency further not really feasible –
severe penalty in energy cost
• Increase spectrum allowance significantly reduces the
energy consumption (a “cost” - not a “must” issue)
• There are unique optimal deployment densities that
minimize energy and/or total cost
• Optimum density – still fairly close to “Shannon Brick
Wall”
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Read more & Interact !
wireless.kth.se
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theunwiredpeople.com
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Additional Slides
11
A new ballgame
from “full coverage” to capacity
(Klas Johansson, ”Cost Effective Deployment Strategies for Heterogeneous Wireless Networks”, Doctoral Thesis, KTH 2007)
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Cost drivers
Greenfield
deployment
Cost structure,
new sites
1,00
Transmission
0,80
Site buildout, installation and lease
0,60
Base station equipment, RNC,
O&M and power
0,40
0,20
0,00
M acro
M icro
Pico
WLAN
(Klas Johansson, ”Cost
Effective
Deployment
Strategies
for Heterogeneous
Wireless
Networks”,
Doctoral
Thesis, Transmission
KTH 2007)
Radio
equipment
, O&M,
power
Sit e buildout
, inst
allat ion
+ lease
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