Review of BS and AP management schemes

Transcript

Review of BS and AP
management schemes
Łukasz Budzisz, Fatemeh Ganji, Adam Wolisz TU Berlin, Germany
Gianluca Rizzo, IMDEA, Spain (currently with Univ. of App. Sciences of
Western Switzerland)
Marco Ajmone Marsan, Michela Meo, Yi Zhang, Politecnico di Torino, Italy
Alberto Conte, Ivaylo Haratcherev, Alcatel-Lucent Bell Labs, France
George Koutitas, Leandros Tassiulas, University of Thessaly, Greece
Mario Pickavet Bart Lannoo, Sofie Lambert, Ghent Univ. - iMinds, Belgium
Energy efficient wireless access networks
n 
Design approach for wireless access networks:
q 
n 
In reality, however:
q 
q 
q 
n 
n 
Obtain maximum performance at full load
most of time, the wireless access networks are under low or
medium traffic load
load profile exhibits large variation (close to the users)
BS consumption is little load proportional and consume about
the same at any load
Savings between
20-40% can be
achieved
BS sleep modes are needed
Sleep modes require BS management algorithms
Michela Meo – Politecnico di Torino
BS/AP management schemes - taxonomy
Proposed framework points out:
n 
the most important design aspects
n 
shortcomings and advantages
n 
energy-saving potential
Existing BS management schemes
n 
Flat Network: with single type of BSs, macrocells/
microcells only, one operator
q 
q 
n 
Multi-Tier Network: with multiple types of BSs, also
multiple technologies
q 
q 
n 
Non-overlapping architecture
Overlapping architecture
Wi-Fi offloading
Femtocells
Mobile operator co-operation
Flat network with non-overlapping architecture
n 
n 
If the no. of BS always on is large, a slight increase of the
RF output power is enough (if any)
If the no. of BS always on is small, RF output power
increase are needed, as well as titling adjustments
Flat network with non-overlapping architecture
Two control schemes:
n  Centralized approach à higher complexity of the decision,
higher guarantee of reliable QoS
n  Distributed approach à BSs or groups of BSs takes
decisions independently
Flat network with overlapping architecture
n 
n 
Micro stations are used to provide the required capacity under the
coverage umbrella of macro stations
Two types of BSs:
q 
q 
n 
n 
Critical stations: usually the macrocell, which cannot be put into sleep
mode, due to coverage issues
Flexible stations: the BS that can be set in sleep mode
No need to increase the cell ranges or the parameters of the BSs
remaining on (low probability of coverage holes)
Control schemes:
n 
n 
Distributed approach: each flexible BS decides on the state of its operation
(on/off), independently
Pseudo-distributed approach: Flexible stations are assigned to critical
stations, usually under cell overlap criteria
Source:
S. Kokkinogenis and G. Koutitas, “Dynamic and static base station management schemes for cellular
networks,” in IEEE Global Communications Conference (GlobeComm ’12), Dec. 2012.
Multi-tier network
n 
Marco-micro network co-exists and cooperates with other
technologies, e.g. femto cell and WiFi
The main objective is to provide an offloading solution
n 
With WiFi:
n 
n 
n 
n 
Mobile operators usually do not have the administrative rights to the Wi-Fi APs
Multi-Radio Access Technology (multi-RAT) is needed
Limitations and constraints for integration with existing BS
system
q 
q 
Guarantee that coverage holes do not occur (especially indoor scenarios)
Software and hardware limitations of real equipment (availability of low
power states, transient times)
Sources:
•  S. Kokkinogenis and G. Koutitas, “Dynamic and static base station management schemes for cellular networks,” in
IEEE Global Communications Conference (GlobeComm ’12), Dec. 2012.
•  I. Haratcherev and A. Conte, “Practical energy-saving in 3g femtocells,” in IEEE Green Broadband Access (GBA)
workshop, in conjunction with ICC 2013, Jun. 2013.
•  I. Haratcherev, M. Fiorito, and C. Balageas, “Low-power sleep mode and out-of-band wake-up for indoor access
points,” in GLOBECOM Workshops, 2009 IEEE, 2009, pp. 1–6.
Network sharing
BS power
The low load proportionality of the devices makes the
whole access network little load proportional
Network power
n 
load
load
P = Pconst + f ( ρ )
Service provisioning cost is multiplied by the no. of networks
Network sharing
n 
Several competing mobile operators cover the same
area with their equipment
n 
During low traffic periods, when the resources of one (or
a few) operator are sufficient to carry all the traffic, make
the operator share their infrastructure
q  Switch off the network of one operator
q  Let users roam to other operators
n 
In the short term, inter-operator switching schemes can
reduce the waste
In the long term, a unique efficient infrastructure with
multiple virtual operators can be envisioned
n 
Case study: Some European Countries
Country
France
Germany
Greece
Italy
Netherlands
Poland
Portugal
Spain
Romania
Russia
Ukraine
U.K.
MNOs
3
4
3
3
3
4
3
3
3
3
3
3
Market share [%]
46 36 19
32 31 21 16
51 28 21
38 36 26
46 26 28
29 29 28 14
45 40 15
44 34 22
41 32 26
37 33 30
48 37 15
39 33 28
-
Subscr. [M]
58.2
113.6
15.4
84.0
19.0
47.5
16.4
51.4
24.2
189.7
52.3
68.5
Table 1: Characteristics of the considered countries: Number
of MNOs offering both 2G and 3G services, market share for
0.8
consumer, const
consumer, var
business, const
business, var
0.7
Relative saving
0.6
0.5
Large savings,
more than
30%!
0.4
0.3
0.2
0.1
UK
Ukraine
Russia
Romania
Spain
Portugal
Poland
Netherlands
Italy
Greece
Germany
France
0
Source:
M. A. Marsan, M. Meo, “Network Sharing and its Energy Benefits: a Study of European Mobile Network
Operators”, IEEE Globecom 2013 - Symposium on Selected Areas in Communications, December 2013.
1
consumer, WD
consumer, WE
business, WD
business, WE
The key point
is increasing
utilization
0.6
0.4
0.2
UK
Ukraine
Russia
Romania
Spain
Portugal
Poland
Netherlands
Italy
Greece
Germany
France
0
No sharing
Utilization
0.8
n 
Game-theory based model
q 
q 
q 
q 
n 
Two MNOs cover the same area and offer the same QoS, but may have
different network planning strategies
Heterogeneous architecture is assumed (different BS types)
Deployment targets peak of the user demand
Binary transitions: all traffic is migrated from one to the other MNO
The following game (transferrable utilities coalitional game of BS
sharing) has been constructed:
q 
q 
q 
MNO 1 has different strategies corresponding to the chosen roaming price
MNO 2 has the choice between roaming or no roaming its traffic
Game objectives: reduce own costs by cooperation, receive fair share of benefits
of total cost reduction
On-going work (iMinds, PoliTO, UTH, TUB): conference article “Greening the AirWaves: energyefficient BS sharing” under preparation.
From energy efficient networking to
sustainable networking
n 
Power BSs with Renewable Energy Sources
q 
q 
q 
Deploy cellular networks in countries where the power grid does
not exist or is unreliable
Achieve extremely low carbon footprints
Survive natural disasters which damage the power grid
Possible scenarios:
1.  New opportunities for the deployments of networks in emerging
regions
Possible scenarios
From energy efficient networking to
sustainable networking
n 
Power BSs with Renewable Energy Sources
q 
q 
q 
Deploy cellular networks in countries where the power grid does
not exist or is unreliable
Achieve extremely low carbon footprints
Survive natural disasters which damage the power grid
Possible scenarios:
1.  New opportunities for the deployments of networks in emerging
regions
2.  New business models (high electricity price, green incentives and
sensitivity)
Dimension PV powering for a BS
n 
Consider a typical BS
q 
q 
n 
n 
n 
Consumption profile
Traffic profile
energy need
Choose a location
Simulate energy production
Simulate battery charge
and discharge to decide
system dimensioning
18
BS consumption
Macro cell with LTE technology, with and without Remote
Radio Unit (PA close to antenna)
Power [W]
Power [W]
1350
840
780
500
Psleep, 450
Psleep, 336
Deep sleep
Deep sleep
0
• 
load
1
0
load
1
When needed (no TLC infrastructure) wireless backhauling consumes
additional 200-250W, for a total of 30KWh/day
10
Traffic profiles: Business area
0
n 
n 
n 
1
0.8
0.6
0.4
Parameter L
0.2
0
Fast Fig.
transitions
4. Double switch-off: network saving versus the parameter L for the synthetic traffic profile.
Peaks during the day
Large difference weekday/weekend
1
Weekday
Weekend
Average Daily Consumption
With RRU
Without RRU
Fig. 5.
Traffic, f(t)
0.8
WD
[KWh]
WE
[KWh]
15.2
12.55
23.9
19.5
0.6
0.4
0.2
0
00:00
05:00
Business cell: weekday and weekend traffic profiles.
10:00
15:00
Time, t [h]
20:00
00:00
20
Traffic profiles: Consumer
n 
n 
n 
Slow transitions
Peaks in the evening
Little difference weekday/weekend
1
Weekday
Weekend
Average Daily Consumption
Traffic, f(t)
0.8
WD
[KWh]
WE
[KWh]
With RRU
15.5
15.7
Without RRU
23.8
24.7
Fig. 6.
0.6
0.4
0.2
0
00:00
05:00
Consumer cell: weekday
andMeo
weekend
traffic profiles.di
Michela
– Politecnico
10:00
15:00
Time, t [h]
Torino
20:00
00:00
21
!
Figura 28: Radiazione solare annuale in Europa (Fonte: PVGIS, Institute for Energy - Unione Europea,
2012)
Three locations
Solar
radiation
Torino
(4KWh/m2, high
seasonal variance)
Solar
radiation
!
Figura 29: Radiazione solare annuale in Africa (Fonte: PVGIS, Institute for Energy - Unione Europea, 2012)
Assuan
!
!
(6.8KWh/m2, low
seasonal variance)
!
2)
Palermo
(5.1KWh/m
H`!
ura 28: Radiazione solare annuale in Europa (Fonte: PVGIS, Institute for Energy - Unione Europea,
2012)
Dimensioning the system
n 
Dimensioning based on the average of the days of the
worst month: look for the system dimension that
guarantees min. surplus
!
Power [W]
!
!
Figura 31: Produzione mensile di energia elettrica (Torino)
Hour
Figura 34: Potenza nel giorno di riferimento per Torino
Location
w/o RRU
KWp
w RRU
KWp
Torino
20
14
Palermo
16
10
Assuan
!
8
6
!
Figura 32 Produzione mensile di energia elettrica (Palermo)
Storing energy: Battery dimensioning
Energy waste
Torino
Battery charge [%]
!
Low charge
(spoils
batteries)
week
Figura 43 Stato di carica delle batterie nell’arco di un anno –Torino – Impianto da 20 kWp
!
Dimensioning the system:
Panel area & batteries
n 
Dimension battery so that charge never goes below
30% during the whole year
Without RRU
With RRU
Location
KWp
Area
[m2]
No.
batt.
KWp
Area
[m2]
No.
batt
Torino
20
98
75
14
68
45
Palermo
16
78
50
10
49
32
Aswan
8
39
30
6
29
16
Results strongly depend on the location
Source:
Marco Ajmone Marsan, Giuseppina Bucalo, Alfonso Di Caro, Michela Meo, Yi Zhang, Towards Zero Grid Electricity Networking: Powering
BSs with Renewable Energy Sources”, IEEE ICC'13 - Workshop on Green Broadband access: energy efficient wireless and wired network
solutions, June 2013.
Dimensioning the system:
Panel area & batteries
n 
Dimension battery so that charge never goes below
30% during the whole year
Without RRU
With RRU
Location
KWp
Area
[m2]
No.
batt.
KWp
Area
[m2]
No.
batt
Torino
20
98
75
14
68
45
Palermo
16
78
50
10
49
32
Aswan
8
39
30
6
29
16
Very large! EE
technology is needed
50(!0.&!A&/3!/)&)(*.@!5:3!/(!)4*E&!&!;3/)(43!0.&!A&//&!(.)3./()Q!2(!)4&88(5*@!'*//&!3.)4&43!(.!0.*!
50(!0.&!A&/3!/)&)(*.@!5:3!/(!)4*E&!&!;3/)(43!0.&!A&//&!(.)3./()Q!2(!)4&88(5*@!'*//&!3.)4&43!(.!0.*!
/)&)*! 238(.()*! /+33'! 1*23C! T03/)*!
+*!/+33'!
/'3;.(13.)*!
2(! A0*.&!
'&4)3!
/)&)*!(1'+(5&!
238(.()*!
1*23C! T03/)*!
(1'+(5&!
+*!23++F:&42R&43!
/'3;.(13.)*! 5:3!
2(! A0*.&! '&4)3! 23++F:&42R&43! 5:3!
Combining RES with sleep modes
5*1'*.3!+&!A&/3!/)&)(*.@!(.!'&4)(5*+&43!+F&1'+(8(5&)*43!2(!'*)3.6&@!(.!1*2*!2&!)3.343!(!5*./01(!
5*1'*.3!+&!A&/3!/)&)(*.@!(.!'&4)(5*+&43!+F&1'+(8(5&)*43!2(!'*)3.6&@!(.!1*2*!2&!)3.343!(!5*./01(!
'(S!A&//(C!
'(S!A&//(C!
n  2*E0)*!
When
traffic
is below
50%,
make
50%
of the
8&4/(! 5&4(5*!
/(&! 5*1'+3)&13.)3!
/1&+)()*!
0.F&+)4&!
A&/3! /)&)(*.!
&! +3(! 2&!
&2(&53.)3C!
2*E0)*! 8&4/(!
5&4(5*!2&!
/(&!
5*1'+3)&13.)3!
/1&+)()*!
0.F&+)4&!,.!
A&/3! /)&)(*.! &! +3(! &2(&53.)3C! ,.!
T0&.2*! 0.&! A&/3! /)&)(*.! ?! (.!T0&.2*!
/+33'! 1*23!
'43/0''*.3!
(+! )4&88(5*!
&E43AA3! 5:3! (+! )4&88(5*! 2(! 50(! &E43AA3!
0.&! /(!
A&/3!
/)&)(*.! ?! 5:3!
(.! /+33'!
1*23!2(!
/(!50(!
'43/0''*.3!
BS go to sleep
mode
1*13.)*C!
B03/)*! 1*2*! /(! 5*.)(.0&! &! ;&4&.)(43!
+&! 5*1'+3)&!
2(/'*.(A(+()Q!
23+!+&!
/(/)31&!
(.! B0&+/(&/(!
B03/)*! 1*2*!
/(! 5*.)(.0&!
&! ;&4&.)(43!
5*1'+3)&!
2(/'*.(A(+()Q! 23+! /(/)31&! (.! B0&+/(&/(!
1*13.)*C!
T03/)*!/31'+(53!&+;*4()1*!2(!/+33'!1*23!?!4&''43/3.)&)*!(.!Y(;04&!_#C!
T03/)*!/31'+(53!&+;*4()1*!2(!/+33'!1*23!?!4&''43/3.)&)*!(.!Y(;04&!_#C!
!
Peak hours
Figura 62
Figura 62
!
Off-peak hours
U&!'*)3.6&!4(5:(3/)&!2&++&!A&/3!/)&)(*.!U9c!(.!/+33'!1*23@!5*13!;(Q!E(/)*!.3+!%&'()*+*!"@!?!
U&!'*)3.6&!4(5:(3/)&!2&++&!A&/3!/)&)(*.!U9c!(.!/+33'!1*23@!5*13!;(Q!E(/)*!.3+!%&'()*+*!"@!?!
5*/)&.)3!3!'&4(!&-!!
!
5*/)&.)3!3!'&4(!&-!!
! !!" ! !!"# ! !!"##$ !
K"C"^L
! !
!!" ! !!"#
! !!"##$
K"C"^L !
Meo.3+!
–2(!Politecnico
di !Torino
2*E3! !!"# ! ?! (+! .0134*! 2(! &.)3..3!
4(53)4&/1())3.)(!
/()*!
13.)43!
! !!"##$
?! +&! '*)3.6&!
2*E3!Michela
!!"# ! ?! (+! .0134*!
&.)3..3!
4(53)4&/1())3.)(!
.3+! /()*! 13.)43! ! !!"##$ ! ?! +&! '*)3.6&!
27
Use of sleep modes
n 
While dimensioning of BS always on is basically
unchanged, the dimensioning for BS going to
sleep mode is much smaller
No sleep
With sleep
Deep sleep
Location
Area
[m2]
Batt.
Area
[m2]
Batt.
Area
[m2]
Batt.
Torino
98
75
78
52
39
27
Palermo
78
50
58
27
29
17
Assuan
39
30
29
18
14
13
Significant improvement!
What about cost?
Total cost: CAPEX+OPEX in 20 years
Area
type
BS ISD
[m]
Coverage Grid only RES only
[km2]
[K€/km2] [K€/km2]
Dense Urban 500 0.65 39.4 29.5 Urban 1000 2.60 9.8 7.4 Suburban 1732 7.79 3.3 2.5 Rural 4330 48.71 0.5 0.4 RES is
cost effective!
Hybrid powering: RES & grid
Total cost: CAPEX+OPEX
No RES
Only RES
Hybrid
RES is
cost effective!
Conclusions
n 
Higher degree of load proportionality is
needed
q 
n 
Load proportionality can be partially achieved
through
q 
q 
n 
To adapt to traffic variations
BS sleep modes (intra-operator approach)
Network sharing (inter-operator approach)
New promising scenarios with RES powering
q 
q 
q 
Sustainability
Cost reduction
But needs also EE solutions
Thank you!
Additional
slides
Mobile traffic explosion vs energy costs
n 
Information and Communication
Technology (ICT) sector is
responsible for 2% of global CO2
emissions and consumes
2-10% of global energy
n 
Mobile traffic explosion will
further aggravate this picture:
q 
n 
increase of BS/AP density to fulfill the
demand
Up to 70% of cellular operators
OPEX are energy costs
Radio access networks – prime
target for energy saving
Breakdown of energy consumption (one of
the European mobile network operators)
Source: S. Vadgama and M. Hunukumbure, Trends
in Green Wireless Access Networks, 2011
Components of a Base Station (BS)
n 
n 
Fixed power: cooling, main supply, DC-DC
Load-proportional power: PA, BBU, RF
Source: EARTH (Energy Aware Radio and network technologies) project, “Energy efficiency analysis of the
reference systems, areas of improvements and target breakdown,” 2012.
Base station power consumption
n 
LTE is energy efficient:
In urban areas, with a typical user density of 300 users/km2
q 
q 
q 
n 
With the same transmission power, an LTE macro BS covers
q 
q 
n 
LTE à 18W/user
WiMAX à 27W/user
HSPA à 68Wer/user
Urban area à about 0.22 km2
Suburban/rural environments à 2.6 km2
An LTE macro BS consumes around 1 kW
q 
q 
Urban area à 4500W/km2
Suburban/rural environments à 400W/km2
Source: W. Vereecken, W. Van Heddeghem, M. Deruyck, B. Puype, B. Lannoo, W. Joseph, D. Colle, L. Martens,
and P. Demeester, “Power consumption in telecommunication networks: overview and reduction strategies,” IEEE
Communications Magazine, vol. 49, no. 6, pp. 62–69, 2011.
Load proportionality and power saving
When k-1 BSs out of k can be in sleep mode (ρ<1/k), the power per BS becomes:
a + kbρ ( k −1) Psleep
PS ( ρ ) =
+
k
k
100
Normalized power consumption vs. load
for different device types:
n 
10% proportion: the devices today
n 
50% proportion: the devices under
development
n 
90% proportion: not realistic with
today technologies
10% propor.
Normalized consumption
50% propor.
10
90% propor.
1
n 
0.1
0.001
with sleep
no sleep
0.01
0.1
n 
1
Sleep modes need to be
introduced
Need for BS management
algorithms
BS load, ρ
n 
Main obstacles to network sharing:
n 
n 
n 
MNOs are reluctant to allow their subscribers to frequently roam to a
competitor MNOs
QoS provisioning in the visited network (dominant vs small MNOs)
Need for extended roaming and billing procedures
ZEN: an example
Orange green strategy for AMEA zone
Project : optimized power consumption and solar powered mobile
Morocco
network
Jordan
Egypt
Mali
Niger
Senegal
Guinea Bissau
Guinea
Ivory Coast
Cameroon
Central African Republic
Kenya
Madagascar
Equatorial Guinea
+ Dominican Republic,
Vanuatu, Armenia, France
the project was initiated in Africa and is now deployed
in 18 countries of France Telecom-Orange
fully integrated photovoltaïc solution
Objectives

sustainable rural Telecom network

strong reduction of power consumption by

selecting high efficiency telecom equipment,

no active cooling

smart power architecture and management
– of
Politecnico
optimized solar energyMichela
solution andMeo
less use
Diesel engines di
sizing and techno-economic tools
•  2065
network
sites,development
for 3.3 M
people
 access
greener solutions
enabling Telecom
in bad
quality
electric
grid
or
off
grid
areas,
•  13GWh solar energy produced in 2011
•  25 Mliters
Innovationfuel 67 Ktons CO2 saved in 2011


Torino
optimized and evolutive solution
Dimensioning the PV system
n 
A system is defined in terms of KWpeak: the
achieved production in KW when radiation is 1KW/m2
Av. daily production [KWh]
!
Torino
month
•  Larger systems allow
for larger production
•  Production changes
according to seasons,
while BS traffic and
consumption not
!
Battery dimensioning
Av. daily production [KWh]
!
Torino
To absorb variability
more batteries are
needed
!
!
month
Assuan
!
Michela Meo –Figura
Politecnico
Torino
33 Produzione di
mensile
di energia elettrica (Assuan)

Review of BS and AP management schemes

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