Deruyck et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:248
http://jwcn.eurasipjournals.com/content/2012/1/248
RESEA RCH
Open Access
Characterization and optimization of the
power consumption in wireless access
networks by taking daily traffic variations
into account
Margot Deruyck* , Emmeric Tanghe, Wout Joseph and Luc Martens
Abstract
In this study, a power consumption model as a function of the traffic is developed for macrocell base stations based
on measurements on an actual base station. This model allows us to develop energy-efficient wireless access
networks by combining the Green radio access network design (GRAND) tool designed by the authors, which
develops an always-on network with a minimal power consumption for a predefined area, and an algorithm that
introduces power reducing techniques in the network such as sleep modes and cell zooming. Green-field
deployments and optimization of existing networks are investigated. For a green-field deployment, it was found that
introducing sleep modes and cell zooming in the network can reduce the power consumption by up to 14.4%
compared to the network without sleep modes and cell zooming. Optimizing existing networks by applying GRAND
(without sleep modes and cell zooming) results in a power consumption reduction of 34.5% compared to the original
network. A careful selection of base station locations already results in a significant energy saving. Introducing sleep
modes and cell zooming to the current networks results in a saving of 8%. Sleep modes and cell zooming are
promising energy-saving techniques for future wireless networks.
Introduction
Several studies indicate that within telecommunication
networks, the wireless access networks are high power
consumers [1-3]. Especially the base stations (BSs) are
responsible for a significant part of the power consumption caused by wireless access networks. Therefore, a lot of
effort has been put lately in designing new power reducing
techniques such as sleep modes and cell zooming [4-12].
Sleep modes allow that a (part of the) BS can be switched
off when there is no or little activity taking part in its coverage cell. Whenever necessary, the BS is waken up. When
applying cell zooming, the cell size is adjusted adaptively
according to the level of activity in a BS’s cell. These techniques on their own can significantly reduce the power
consumption in wireless access networks and combining them allows even higher power savings. Up to now
*Correspondence: margot.deruyck@intec.UGent.be
IBBT, Department of Information Technology, Ghent University, Gaston
Crommenlaan 8 box 201, B-9050 Ghent, Belgium
temporal variations of wireless access networks have only
been studied in [7-12] and not experimentally.
In this study, a power consumption model as a function
of the traffic load is developed for a macrocell BS based
on measurements on an actual HSPA macrocell BS. To
the best of author’s knowledge, this has never been done
before. Furthermore, this model is used in the Green radio
access network design (GRAND) tool [13] to design an
always-on network with a minimal power consumption
for a predefined area. This deployment tool is then combined with an algorithm that introduces power reducing
techniques in the network such as sleep modes and cell
zooming. Both green-field deployments (i.e., implementing a new network) and optimizing an existing operator
network by applying the GRAND tool are considered. To
the best of the authors’ knowledge, combining the power
consumption model with the deployment tool and the
algorithm is also a novelty.
© 2012 Deruyck et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly cited.
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The outline of this article is as follows. Section ‘Related
study’ gives an overview of the related study in this field.
Section ‘Load-dependent power consumption model for
a macrocell base station’ discusses the power consumption model of a macrocell BS. In Section ‘Measurement
procedure’, the measurement procedure is described. In
Section ‘Relating power consumption and the number
of voice and data calls’, a model is given for the relation between traffic load and power consumption based
on measurements. Section ‘Introduction of sleep modes
and cell zooming’ describes the algorithm which determines which BSs can sleep and which have to ‘zoom’.
Section ‘Results’ presents the results of applying GRAND
and the algorithm for introduction of sleep modes and cell
zooming on a green-field deployment and on an existing
operator network. In Section ‘Conclusion’, the conclusions
of this study are given.
as ours. However, it is also important to investigate what
the energy savings are when sleep modes are introduced
in a network with a minimal power consumption (here
resulting from the GRAND tool). In this way, the most
energy-efficient solution for the area is considered. The
algorithm developed in [10] is similar to ours, but it is
assumed that the cell size of the active (non-sleeping) BSs
is maximized. In our algorithm, the cell size of the active
BSs is expanded as much as needed which does not necessarily correspond with the maximum cell size. Note that
in this study it is not discussed how sleep modes and cell
zooming should be supported by the hardware, nor the
protocols that will be needed, e.g., for waking up the BS
when necessary. This is the scope of some other studies
[8-12].
Related study
To optimize the energy efficiency of the wireless access
network, it is important to determine the power consumption of the individual parts of this network. Here, the focus
is on the macrocell BS. A typical architecture of this BS
is shown in Figure 1. As discussed in [13], a macrocell
BS consists thus of different components each consuming a specific amount of power. These components can be
divided into two categories, the load independent and the
load dependent (Equation (1)). Furthermore, two groups
of equipment—one for all sectors and one per sector—can
be unidentified (Equations (2) and(3)).
The first group contains all the components that are
common for all sectors: the microwave link (that is used
for communicating with the backhaul network and is,
nowadays, often replaced by a fiber link) and the air conditioning. The power consumption of this equipment is
constant throughout time. Although, a remark should be
made. The temperature in the BS cabin should be kept
more or less the same to preserve good functionality of
the equipment. It is assumed that the heat generation
rate (from both the external temperature of the cabin
and the heat generated from the equipment) is constant
which results in a constant power consumption of the air
conditioning throughout time.
The second group is the equipment that is used per
sector such as the rectifier (also known as the AC-DC
Recently, more attention is drawn to the power consumption in wireless access networks. To model this power
consumption, it is important to quantify the power consumption of the different components in this network.
[4-7] propose a power consumption model for today’s
macrocell BSs. However, [7] does not use any measurements to establish the power consumption model. The
models proposed in [4-6] are based on GSM and/or
UMTS macrocell BSs, while the model in this study is
based on measurements on a HSPA BS. Our study shows
that it is important to perform measurements to identify
the relation between power consumption and traffic properly. Furthermore, a realistic HSPA traffic model can be
determined instead of using theoretical traffic models as
is often done in literature.
Also the possibilities of sleep modes to reduce the power
consumption in wireless access networks is already established in a number of studies [7-10]. These studies discuss
how sleep modes can be implemented and supported by
the network. However, in [7-9], the energy savings in a
realistic network deployment are not investigated. Our
study tries to determine how much power can be saved
by introducing sleep modes in a realistic network. In [10],
the effect of sleep modes for different operator networks
in a certain area is investigated which is a similar study
Figure 1 The architecture of a macrocell base station.
Load-dependent power consumption model for a
macrocell base station
Deruyck et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:248
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converter), the digital signal processing (used for system processing and coding), the power amplifier, and the
transceiver (for sending and receiving of the signals). The
power consumption of this group has to be multiplied by
the number of sectors nsectors supported by the BS. For a
macrocell BS, nsectors is typically 3.
Furthermore, the power consumption of these components (except for the rectifier) depends on the load on the
BS which is determined by the number of users and the
services they use in the BS’s cell. The higher the number of users and the higher the requirement, the higher
the load is. The power consumption of these components
should thus be scaled according to the load. This is done
by introducing a factor F, denoted here as the load factor.
The power consumption Pel/macro of a macrocell BS is
then determined as follows (in Watt):
Pel/macro = Pel/const + F · Pel/load
(1)
Pel/const = nsector · Pel/rect + Pel/link + Pel/airco
(2)
Pel/load = nsector · (nTx · (Pel/amp + Pel/trans + Pel/proc ))
(3)
with F the load factor and nsector the number of sectors supported by the BS, nTx the number of transmitting antennas per sector, Pel/rect , Pel/link , Pel/airco , Pel/amp ,
Pel/trans , Pel/proc the power consumption of, respectively,
the rectifier, the microwave link (if present), the air conditioning, the power amplifier, the transceiver and the digital
signal processing (in Watt). The typical power consumption of the different components can be found in Table 1
[13]. The power consumption of the power amplifier is
defined as follows (in Watt):
Pel/amp =
PTx
η
(4)
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with PTx the input power of the antenna (in Watt) and η
the efficiency of the power amplifier which is defined as
the ratio of the RF (Radio Frequency) output power to the
electrical input power [14].
In general, the load factor F varies from 0 to 1. A value of
0 indicates that the load-dependent components are consuming no power, i.e., there are switched off, while a value
of 1 indicates that they are working on the highest power
consumption level. Instead of determining a load factor F
for each hour of the day, a formula is defined to calculate F
as a function of the load caused by voice calls and the load
caused by data calls. The formula will be of the following
form:
(5)
F =x·V +y·D+c
with V the load caused by voice calls (0 ≤ V ≤ 1) and
D the load caused by data calls (0 ≤ D ≤ 1). The division of load caused by voice calls and data calls is based
on the traffic data received from a mobile operator. The
parameters x, y, and c will be determined based on measurements of both the power consumption and the voice
and data traffic on an actual macrocell BS as discussed
in the following sections. These measurements are necessary to relate the traffic data of the operator to the actual
power consumption. These variations in power consumption can not be found in data sheets as they mostly provide
the maximum or average power consumption.
Measurement procedure
As mentioned above, to determine a model for the load
factor F, measurements are performed on an actual HSPA
(High Speed Packet Access) macrocell BS in the suburban area of Ghent, Belgium. The power consumption
of the BS is measured during six days (4 weekdays and
Table 1 Power consumption of the macrocell base station components
Equipment
Macrocell
base station
Number of sectors
Number of transmitting
nsector
3
nTx
1
Pel/proc
100 W
antennas per sector
Digital signal processing
Power amplifier
Transceiver
η
12.8 %
Pel/trans
100 W
Rectifier
Pel/rect
100 W
Air conditioning
Pel/airco
225 W
Microwave link
Pel/link
80 W
Pel/const
605 W
Pel/load
1067.7 W
Total BS (F = 0)
Pel/macro
605 W
Total BS (F = 1)
Pel/macro
1672.7 W
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2 weekend days). Only the power consumption of the
load-dependent components (i.e., the digital signal processing, the transceiver, and the power amplifier) were
included in these measurements. As DC (Direct Current)
is used in the BS, the voltage is constant (i.e., approximately 54 V) and thus only the current was measured. The
power consumption P(t) at a certain time instance t is
then determined as follows (in Watt):
P(t) = V · I(t)
(6)
with V the voltage (in Volt) and I(t) the current at time t
(in Ampere).
Using an AC/DC current clamp (Fluke i410), the current is measured every second which results in 518,400
samples for the measurement period of six days.
Relating power consumption and the number of
voice and data calls
Processing the measurement data
Based on the data from the measurements, a model for the
load factor F is determined. The following procedure is
used. First, the average power consumption for each hour
during the measurement period is determined. This gives
us a vector P containing 144 (=6 days × 24 h) power consumption values, i.e., for each hour of the measurement
period one value. This averaging is necessary as the power
consumption will later be related to the number of voice
and data calls. The data about the number of voice and
data calls is provided per hour by the operator.
Second, the averaged power consumption per hour P is
normalized [15]:
Pnorm =
P − min(P)
max(P) − min(P)
(7)
with P the vector of the average power consumption per
hour, and max(P) and min(P) the maximum, respectively
minimum, average power consumption per hour during
the measurement period.Pnorm is thus a vector of again
144 values, which represent the normalized power consumption per hour during the measurement period. Pnorm
is dimensionless and yields values between 0 and 1. The
data is normalized because the relative values are more
interesting than the absolute values.
Figure 2 shows Pnorm during one of the measurement
days for the considered HSPA macrocell BSs. Pnorm equals
F, as F is defined as a scale factor taking the influence
of the traffic on the power consumption into account.
From 6 a.m. on, F increases significantly as during the day
more people make phone calls than during the night. A
higher value of F will result in a higher power consumption as can be seen from Equation (1). In the evening, the
load factor F decreases again. Around 7 p.m., a small peak
of demand can be noted. Similar results are found for the
other measurement days.
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As mentioned above, Pnorm will be related to the HSPA
traffic.Two categories of traffic can be defined: voice calls
and data calls. The data about the number of voice and
data calls is provided per hour by the mobile operator.
The vectors Vh and Dh contain thus 144 (=6 days × 24 h)
values which represent the number of voice, respectively
data, calls for each hour h of the measurement period. The
same procedure as for the power consumption is then followed for both the number of voice and data calls and thus
the number of voice and data calls are normalized:
Vh − min(Vh )
(8)
Vnorm =
max(Vh ) − min(Vh )
Dh − min(Dh )
Dnorm =
(9)
max(Dh ) − min(Dh )
with Vh and Dh the vectors containing the number of
voice, respectively data, calls during each hour of the measurement period, max(Vh ) and max(Dh ) the maximum
number of voice, respectively data, calls per hour during
the whole measurement period and min(Vh ) and min(Dh )
the minimum number of voice, respectively data, calls per
hour during the whole measurement period. Vnorm and
Dnorm are then vectors containing 144 values representing
the normalized number of voice, respectively data, calls
for each hour of the measurement period. The values of
these vectors vary from 0 to 1.
Figure 2 compares Vnorm and Dnorm with the load factor and normalized power consumption Pnorm for one of
the measurement days of the considered HSPA macrocell
BS. A similar trend as for F can be noticed. The number of voice and data calls are also higher during daytime.
So F and V and D will be correlated and modeled in the
following section.
Determining a model for the load factor F
Based on the results shown in Figure 2, a model for the
load factor F is now determined by using linear regression.
The dependent variable is Pnorm , while the two independent variables are Vnorm and Dnorm . The following
formula is then obtained for F:
F =x·V +y·D+z·V ·D+c
(10)
wit V equals Vnorm and D equals Dnorm . As y and z are
not significant (p-value = 0.7128 and 0.5044, respectively),
Equation (10) can be adapted to:
F =x·V +c
= 0.6 · V + 0.2
(11)
(12)
with x = 0.6 and c = 0.2. The model is shown in Figure 2
as a function of the time and shows very good agreement
with the measured data.
As can be seen from the form of Equation (12),
the BS’s power consumption depends linearly on V. In
[5,6], a similar power consumption model is proposed
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1
0.9
0.8
0.7
[−]
0.6
0.5
0.4
0.3
P
0.2
norm
Vnorm
D
norm
0.1
model for F eq. (12)
0
0
5
10
15
20
25
Time [h]
30
35
40
45
Figure 2 Comparison of Pnorm , Vnorm , and Dnorm as a function of time during 24 h for a HSPA macrocell base station (suburban
environment, Belgium).
based on measurements on a GSM and UMTS macrocell BSs. Both these studies also obtain a linear relation between the traffic and the power consumption
which validates our model. The daily power consumption
increases with 1.5% when the power consumption during sleep mode increases with 100 W. Note that when V
equals to 0, the power consumption does not decrease
to 0 W because the load-independent components (such
as, the rectifier, the air conditioning, and the microwave
link) still consume power and also the load-dependent
components keep consuming a small amount of power
(F = 0.2 for V = 0).
Note also that the parameters (x, y, z, c) depend on
the performance of the components. A component of
another brand or another model number can result in
different parameters (x, y, z, c). The values of Table 1
are average values obtained from data sheets from various vendors. The BS’s power consumption calculated
using these values agrees excellently (standard deviation
of 2%) with the measured ones, presented in this article. The obtained values for (x, y, z, c) are representative
for the current generation BSs. For new generation BSs
and future developments where the power consumption
of the components is optimized, new measurements will
be necessary.
Introduction of sleep modes and cell zooming
A promising technique to reduce power consumption
in wireless access networks is the introduction of sleep
modes where BSs are becoming inactive when no or
little activity takes place in their coverage cells [7-10].
The BS is not completely switched off during the sleep
mode as it keeps monitoring and if necessary (e.g., when
a call has to be made) it can become active again.
Another technique is called cell zooming [11] which
adaptively adjust the cell size according to (amongst others) the traffic load. In this section, the designed algorithm, which combines these two techniques for power
consumption reduction in a wireless access network,is
discussed.
The algorithm is discussed based on Figure 3. Figure 3a
shows the initial situation, i.e., the always-on network as
obtained from the GRAND tool. For each BS in the network, it is checked if it is possible to put it into sleep.
However, when a BS is put into sleep the coverage of this
BS should be guaranteed by other BS(s), which are awake.
To define if this is possible for a certain BS, here called
BStest , the neighboring BSs are determined as shown in
Figure 3a. The range of the neighboring BSs will then
be increased by adding up to 5 dBm to the antenna’s
input power of the neighboring BSs which is shown by
the arrows in Figure 3b. If the area covered by BStest can
be covered by extending the range of neighboring BS(s),
BStest will be put into sleep when the sleep threshold is
met as shown in Figure 3c. The dotted line in this figure
indicates the ’old’ coverage of the neighboring BSs. The
traffic load of BStest will be divided over the neighboring BSs. When the sleep threshold is not met, the final
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As discussed above, the algorithm consists of a number
of steps. The different steps of the algorithm will now be
discussed in details based on the flow graph in Figure 4.
First, a network is designed for a predefined area by
using the GRAND tool [13]. This tool can be used to
design greenfield networks, but also enables optimizing
existing networks. The input power of the (macrocell) BS
antenna is limited in this first step to e.g., the maximum
possible input power minus 5 dBm. In this way, it is possible to let cells zoom by adjusting the input power of the
antenna.
Second, the algorithm for introduction of sleep modes
and cell zooming is applied (Figure 4). The different
steps are now explained. For every active base station
BSi (i = 1, . . . , M with M the total number of BSs) in
the network resulting from the GRAND tool, it is determined whether it is possible to put this BS to sleep.
This is done by selecting the 4 closest active BSs (BSj )i
(j = 1, . . . , 4 with j = 1 the BS the closest to BSi and
j = 4 the BS the furthest to BSi ) (Figure 4 Step 1).
Remark that the number of closest BSs can be chosen
freely. Here, 4 was chosen as this gave a good balance
between performance and the computational time of the
algorithm.
In the next steps, the input power of the antenna of
one or more neighboring BSs is increased. This is done
as follows:
Figure 3 Overview of the proposed algorithm for introducing
sleep modes and cell zooming: always-on configuration (a),
checking if a base station can sleep (b), final configuration with
base station asleep (c).
configuration is as shown in Figure 3a. When the sleep
threshold is met, the final configuration is as shown in
Figure 3c.
Figure 4 Flow diagram of the algorithm for introducing sleep
modes and cell zooming in a network.
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1. Increase the input power of the antenna of (BS1 )i
with 1 dBm, i.e., apply cell zooming (Figure 4 Step 2).
This will expand the range of the cell. Note that it is
important to increase the input power sequential
because each dBm added to the input power results
in a higher power consumption as shown in
Equations (3) and (4).
2. Check if a solution is found. This means: check if the
cell of (BS1 )i covers the cell of BSi (Figure 4 Step 3).
If so, the calculation for BSi can be stopped (Figure 4
Step 8).
3. If not, follow the same procedure (step 1 and 2 of this
list) for all the other BSs from set (BSj )i (Figure 4
Step 4).
4. If no solution can be found by expanding the range of
only one BS from the set (BSj )i , check if there is
solution when expanding the range of two BSs from
(BSj )i (Figure 4 Step 5 & 6). If a solution is found,
stop the calculation for BSi (Figure 4 Step 3).
5. If all combinations with two BSs from set (BSj )i are
checked and no solution is found (Figure 4 Step 5 &
6), check if there is a solution when expanding the
range of three BSs from (BSj )i (Figure 4 Step 5 & 6).
If a solution is found, stop the calculation for BSi
(Figure 4 Step 3).
6. If all combinations with three BSs from set (BSj )i are
checked and no solution is found (Figure 4 Step 5 &
6), check if there is a solution when expanding the
range of the four BSs from (BSj )i . If a solution is
found, stop the calculation for BSi (Figure 4 Step 3).
7. If no solution can be found by expanding the range of
the four BSs from the set (BSj )i , it is not possible to
put the BS into sleep.
When a solution is found for BSi or when no solution
is found at all for BSi , it is checked if there are other
active BSs in the network which have not been investigated yet (Figure 4 Step 7 & 8). If so, the above described
procedure is repeated for these BSs. If not, it is checked
if there was a solution for any of the active BSs in the
network (Figure 4 Step 9). If so, the solution with the
lowest power consumption is determined and the corresponding base station BSi is put into sleep (Figure 4 Step
10). To determine the power consumption of a solution,
the traffic load of the BS put into sleep mode is equally
divided over the BSs that need to be expanded. This is a
very simple approach, but it is expected that more complex approaches will not change the results as the power
consumption depends linearly on the number of voice
calls V (see Equation (12)). The whole procedure is then
again repeated (Figure 4 Step 0). If no solution is found
for any of the active BSs BSi in the network (Figure 4
Step 9), the algorithm is stopped and the final result
is obtained.
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Results
In this section, the power consumption of the macrocell
BS is determined as a function of the load V caused by
voice calls. Furthermore, the evolution of the BS’s power
consumption during the day is investigated.
The evolution of the macrocell BS’s power consumption
during the day is shown in Figure 5. This figure is obtained
by taken the average of the power consumption over the
measurement period for each hour of the day. The highest
power consumption (i.e., 1259.2 W, V = 0.7) is obtained at
5 p.m., while the lowest power consumption (i.e., 820.4 W,
V = 0.003) is reached at 4 a.m. The power consumption
is clearly higher during daytime because less people make
phone calls at night (Figure 5).
Green-field deployment
The power savings in a network by introducing sleep
modes and cell zooming are now determined for a greenfield deployment. For this investigation, a new network is
developed for the city center of Ghent. This target area
is shown in Figure 6a and has a surface of approximately
13.3 km2 . A coverage requirement of 90% of the target
area is here assumed. Furthermore, LTE (Long Term Evolution) is chosen as wireless technology (frequency of
2.6 GHz) [16] and a physical bit rate of approximately
5 Mbps (in a 5 MHz channel) is provided. LTE is here considered as it is assumed that this technology will mostly
be introduced in new network deployments. It is assumed
that the (voice) traffic behaviour will not change significantly when a new technology is introduced in the
network. Comparing the shape of the daily evolution of
the HSPA traffic with the shape of the daily evolution
of GSM (Global System for Mobile communications) as
shown in [15], shows no significant differences. Therefore,
the model from the previous sections is also used here.
Furthermore, it is assumed that the traffic is uniformly distributed over the area. Each BS will thus have the same
traffic load at the same time when sleep modes are not
activated.
The network resulting from the GRAND tool (without
activation of sleep modes and cell zooming) is shown in
Figure 6b. It consists of 80 macrocell BSs with a total
power consumption of 1566 kWh per day.
Figure 6c shows the resulting network when sleep
modes and cell zooming are activated. 28 BSs can be put
into sleep when the sleeping conditions are met (i.e., when
V is below the sleep threshold). Note that it is here not
necessary to define the actual value of the sleep threshold, as it is assumed that each BS has the same traffic
pattern, each BS would reach the sleep threshold at the
same time, and thus it would be possible to put each BS
into sleep. However, the coverage that is lost by putting
a BS into sleep should be provided by another BS(s). The
algorithm shown in Figure 4 determines for which BSs
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1
0.9
0.8
0.7
F [−]
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
Time [h]
Figure 5 Evolution of F during the day.
this is possible and for which not. The sleep threshold will
be a very important parameter in determining the actual
power savings in the network as is now discussed.
Figure 7 shows the power savings that can be achieved
by introducing sleep modes and cell zooming in the network. Here, we vary the sleep threshold from 0.0 to 0.3 in
Figure 6 The target area (a), the greenfield network (b) for the target area resulting from the GRAND tool, and the network (c) when
sleep modes and cell zooming are applied.
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Figure 7 Power savings by introducing sleep modes and cell zooming in the network for varying sleep thresholds from 0 to 0.3.
steps of 0.05. The daily power consumption of the network
as a function of the sleep threshold is shown in Figure 7.
When the sleep threshold is 0.1, the network consumes
1432.8 kWh per day, which is 8.6% lower than without
sleep modes and cell zooming. If the sleep threshold is 0.2,
the network only consumes 1369.9 kWh per day which
corresponds with a power saving of 12.6%. If the sleep
threshold is 0.3, the network only consumes 1297.3 kWh
per day which corresponds with a power saving of 13.5%.
A higher power saving is achieved when a higher threshold is applied because the BSs can sleep more hours per
day than for a lower threshold (Section ‘Results’). However, for a sleep threshold of 0.3, the load on the neighboring BSs is becoming very high (load of 0.9). Therefore,
it is recommended to use a sleep threshold lower than
0.3. Furthermore, remark that it is assumed that a BS in
sleep mode consumes no power (both the load dependent
and the load independent power consumption equal 0 W).
This might however be too optimistic, but it allows us to
determine the upper bounds of the power savings that can
be achieved. Figure 8 shows the influence of this assumption on our results. The power consumption for the sleep
mode is varied between 0 W and 600 W (which corresponds with the BS’s power consumption when there is no
traffic (Table 1)) and a sleep threshold of 0.1 is assumed.
The percentages show the relative daily power consumption with respect to the daily power consumption of the
network without sleep modes. A linear relation between
the power consumption of the sleep mode and the daily
power consumption of the network is found. As mentioned above, 28 BSs can put into sleep during 8 hours
of the day (sleep threshold = 0.1). If a power consumption of 100 W is assumed for the sleep mode, this results
in an extra power consumption of 22.4 kWh. The higher
the power consumption during sleep mode, the lower the
obtained power savings. When a BS consumes 600 W during sleep modes, the network’s power consumption is the
same as for the network without sleep modes.
Figure 9 shows the evolution of the network’s power
consumption during a day with and without sleep modes.
The power consumption during daytime (from 8 a.m.
till 7 p.m.) is similar for all the investigated cases (i.e.,
no sleep modes and sleep modes with the considered
sleep thresholds) due to the fact that for this time period
V is higher than the sleep thresholds considered. At
night (1 a.m. − 7 a.m.) and 11 p.m., 28 of the 80 BSs
can be put into sleep (V is lower than all the sleep
thresholds considered). For the other hours of the day, it
depends on the sleep threshold if the 28 BSs can be put
into sleep.
Optimizing a network
For this investigation, Brussels Capital Region, Belgium, is
considered with a surface of approximately 149 km2 . Data
about the BS sites is provided by BIPT (Belgian Institute
for Postal services and Telecommunications). Figure 10a
shows all the 858 available BS sites in Brussels. Within this
region, four different operator networks can be identified.
Deruyck et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:248
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Page 10 of 12
Figure 8 Influence of the power consumption during sleep mode on daily power consumption of the network (sleep threshold = 0.1).
Figure 10a shows thus all the BSs of the four operators. Here, only one operator network (289 BSs, HSPA
[17]) is considered. All the possible locations are shown
in Figure 10b and are used as an input for the GRAND
tool. The GRAND tool will adapt the operator network
by adding BSs, changing the locations of BSs, or changing settings such as the input power of the antenna. By
changing the locations of BSs, it is meant that a BS is
removed from a location and added at another possible location. The set of BSs that can be added to the
Figure 9 The evolution of power consumption of the network through time when sleep modes are activated and deactivated.
Deruyck et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:248
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Page 11 of 12
Figure 10 Brussels capital region: available sites (a), original operator network (b), network optimized by the GRAND tool (c), network
with sleep modes and cell zooming activated.
network corresponds with the set of all the available BSs
in the Brussels Capital Region (Figure 10a). Furthermore,
it is assumed that the traffic is uniformly distributed over
the considered area for both the existing and the optimized network. The model from the previous sections
will be used in this study which is a realistic approach
as only the HSPA macrocell BSs are considered for the
existing network.
Figure 10c shows the network resulting from the
GRAND tool with deactivation of the sleep modes and
cell zooming. This network uses only 188 BSs and has
thus a daily power consumption that is 33.4% less than the
power consumption of the original network (4733.4 kWh
versus 7108.0 kWh) as shown in Table 2. Remark that
the optimized network with its lower power consumption still has a significant higher coverage than the original network (95.0% versus 88.7%, Table 2). It is thus
concluded that it is useful to place the BSs properly to
save power.
Figure 10d shows the optimized network when sleep
modes (sleep threshold of 0.1) and cell zooming are
activated. 18 BSs can sleep when the traffic is below
the sleep threshold of 0.1 (Table 2). This results in
a daily power consumption of 4641.4 kWh (Table 2)
which is 1.9% lower than the optimized network without activating sleep modes and 33.4% lower than
the original operator network. Again it is concluded
that it is recommended to add sleep modes and
cell zooming into the networks. Today’s operator networks are currently thus not optimized towards power
consumption.
For comparison, it is also determined how much power
can be saved when introducing sleep modes in the original
operator network without first optimizing the network.
Assuming a sleep threshold of 0.1, this results in a daily
power consumption of 6526.5 kWh (versus 7108.0 kWh
for the original network). A reduction of about 8% is
thus obtained.
Conclusion
In this study, a power consumption model as a function
of the traffic load and temporal variations is developed
for a macrocell base station based on measurements on
an actual base station. This model allows us todevelop
energy-efficient wireless access networks by combining
the GRAND tool, which develops an always-on network
with a minimal power consumption for a predefined
area, and an algorithm that introduces power reducing
Table 2 Results for the optimization of an operator network in the Brussels caption region
Original
Optimized
Optimized
no sleep
sleep
No. base stations
289
188
170
Coverage (%)
88.7
95.0
95.0
7108.0
4733.4
4641.4
Daily power consumption (kWh)
Deruyck et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:248
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techniques in the network such as sleep modes and cell
zooming.
Two cases are investigated. In the first case, a completely new LTE network is developed for the city center
of Ghent. By introducing sleep modes and cell zooming
in this network, the power consumption can be reduced
up to 14.4% (depending on the used sleep threshold)
compared to the network without sleep modes and cell
zooming. In the second case, an existing operator network
for Brussels capital region is optimized. Applying GRAND
on this network results in a reduction of 34.5% for the daily
power consumption. The introduction of sleep modes and
cell zooming causes an additional saving of 2.5% compared to the optimized network. A careful selection of the
base station locations can already result in a significant
saving (34.5% as shown by the results from the GRAND
tool). In current networks, this can be done by site sharing.
For future networks, it is recommended that sleep modes
and cell zooming are supported.
Future research will consist of taking also adaptive
capacity demands into account and exposure for the
human being.
Competing interests
The authors declare that they have no competing interests.
9.
10.
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12.
13.
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17.
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doi:10.1186/1687-1499-2012-248
Cite this article as: Deruyck et al.: Characterization and optimization of
the power consumption in wireless access networks by taking daily traffic variations into account. EURASIP Journal on Wireless Communications and
Networking 2012 2012:248.
Acknowledgements
The study described in this article was carried out with support of the
IBBT-project Green ICT. W. Joseph was a Post-Doctoral Fellow of the FWO-V
(Research Foundation Flanders).
Received: 6 January 2012 Accepted: 18 July 2012
Published: 8 August 2012
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