Characterization and optimization of the power consumption in wireless access networks by taking daily traffic variations into account
© Deruyck et al.; licensee Springer. 2012
Received: 6 January 2012
Accepted: 18 July 2012
Published: 8 August 2012
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.
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 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 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.
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.
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, 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, the effect of sleep modes for different operator networks in a certain area is investigated which is a similar study 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 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].
Load-dependent power consumption model for a macrocell base station
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 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.
Power consumption of the macrocell base station components
Number of sectors
Number of transmitting
antennas per sector
Digital signal processing
Total BS (F = 0)
Total BS (F = 1)
with P Tx 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.
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.
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.
with P the vector of the average power consumption per hour, and and 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. P normis dimensionless and yields values between 0 and 1. The data is normalized because the relative values are more interesting than the absolute values.
with V h and D h the vectors containing the number of voice, respectively data, calls during each hour of the measurement period, and the maximum number of voice, respectively data, calls per hour during the whole measurement period and and 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.
Figure2 compares Vnorm and Dnorm with the load factor and normalized power consumption Pnormfor 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
with x = 0.6 and c = 0.2. The model is shown in Figure2 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 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 Table1 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 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.
First, a network is designed for a predefined area by using the GRAND tool. 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 (Figure4). 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 (BS j ) i (j=1,…,4 with j=1 the BS the closest to BS i and j=4 the BS the furthest to BS i ) (Figure4 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.
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).
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).
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).
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).
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).
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).
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 BS i or when no solution is found at all for BS i , it is checked if there are other active BSs in the network which have not been investigated yet (Figure4 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 (Figure4 Step 9). If so, the solution with the lowest power consumption is determined and the corresponding base station BS i is put into sleep (Figure4 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 (Figure4 Step 0). If no solution is found for any of the active BSs BS i in the network (Figure4 Step 9), the algorithm is stopped and the final result is obtained.
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 network resulting from the GRAND tool (without activation of sleep modes and cell zooming) is shown in Figure6b. It consists of 80 macrocell BSs with a total power consumption of 1566 kWh per day.
Figure6c 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 Figure4 determines for which BSs 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.
Optimizing a network
Results for the optimization of an operator network in the Brussels caption region
No. base stations
Daily power consumption (kWh)
Figure10d 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 (Table2). This results in a daily power consumption of 4641.4 kWh (Table2) 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.
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 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.
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).
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