Future Network & MobileSummit 2011 Conference Proceedings
Paul Cunningham and Miriam Cunningham (Eds)
IIMC International Information Management Corporation, 2011
ISBN:978-1-905824-25-0
Opportunities for Energy Savings in
Pico/Femto-cell Base-Stations
Björn DEBAILLIE1, Alexandre GIRY2, Manuel J. GONZALEZ3, Laurent DUSSOPT2,
Min LI1, Dieter FERLING4, Vito GIANNINI1
1
imec, Kapeldreef 75, 3001 Leuven, BELGIUM. [email protected]
2
CEA-LETI, rue des Martyrs 17, 38054 Grenoble 9, FRANCE.
3
TTI, calle Albert Einstein 14, 39011 Santander, SPAIN.
4
Alcatel-Lucent Bell Lab, Lorenzstr.10,70435 Stuttgart ,GERMANY.
Abstract: To support the growth and the dynamism in today’s wireless
communication, networks are evolving towards smaller cells and base-stations closer
to the mobile users. This evolution opens the possibility to deal with one of the main
problems in mobile networks: the energy consumption of the base-stations. In this
paper, different opportunities are proposed to save energy in small-cell base-stations.
These solutions, targeting energy adaptation, focus on the most power greedy
components. The energy savings are shown both for the individual components as
for a typical pico-cell base-station operating on LTE signals and considering a daily
data traffic profile. This offers an average energy saving of 30%, but the flexible
nature of the proposed solutions can be further exploited in heterogeneous networks.
Keywords: energy efficiency, pico-cell base station, femto-cell base station,
transceiver, baseband, power amplifier, EARTH.
1. Introduction
The wireless communication scene is both diverse and dynamic: the users show large
variations over time [1], space [2] and frequency. Serving them with large homogeneous
cells and static approaches is therefore inefficient [3]. Evolving towards a network of
smaller cells and base-stations closer to the user facilitates to reduce the radiation power.
Moreover, these smaller base-stations can be implemented as flexible and scalable solutions
that adapt their operation to the dynamic wireless scene with high energy efficiency [4].
This efficiency is crucial because the continuous growth of the wireless communication
scene leads to an intolerable ecological footprint and electricity costs. Based on this
concern, the Energy Aware Radio and neTwork tecHnologies (EARTH) [5] project sets the
ambitious objective to reduce the energy consumption of the 4th generation mobile wireless
communication network by 50%. This paper contributes to this objective by saving energy
in the small-cell base-stations. First, the pico/femto base-station architecture is described
and the components are identified that dominate the power consumption. Then, flexible
analaog and digital solutions are presented that allow dynamic and energy-efficient smallcell operation. Finally, the energy savings when applying all these solutions in a pico-cell
base-station are evaluated considering realistic LTE [6] traffic load and a daily cellular data
traffic profile [7].
2. Energy distribution
The power consumption of a base-station (BS) is mainly dominated by the radio equipment.
Such radio equipment is especially critical as it provides a physical interface between the
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cloud of mobile users and the network; it must guarantee a continuous flow of information
while providing sufficient quality-of-service. To reduce the power consumption of the radio
equipment efficiently, it is essential to quantify the power consumption over the different
radio equipment components and to focus on the main consumers.
Figure 1 (left) shows a simplified block diagram of a pico/femto base-station transceiver
supporting multiple transceivers, antennas and antenna interfaces (L). Each transceiver
comprises a Power Amplifier (PA), a Radio Frequency (RF) transceiver, a baseband (BB)
interface and a DC-DC power regulator. Both the RF and BB components offer reception
and transmission capabilities for Up-Link (UL) and Down-Link (DL) operation
respectively. Figure 1 shows on the right the energy distribution over the different basestation components for different cell sizes [7]. For macro-cell BSs, the power consumption
is mainly concentrated in the PA. Scaling down the cell size reduces the relative weight of
the power consumption from the PA towards the BB and RF components. For pico and
femto-cell base-stations, the power consumption of the RF component is more than 12%
and the BB and PA consume about 30% each. Therefore, for pico/femto-cell BSs, it is
opportune to investigate energy enhancement techniques for these three components. Note
that the power consumption of the antennas and their interface are minor consumers, but as
they co-determine the energy efficiency of the PA, they are also considered as potential
energy savers.
PA
Pico/Femto-cell Base station
Main supply
DC-DC
BB-DL
BB-UL
RF-DL
RF-UL
PA
LLL
Powerconsumptionbreakdown
100%
MainSupply
RF
BB
Cooling
8%
9%
80%
DCͲDC
7%
5%
7%
60%
29%
33%
39%
12%
5%
7%
40%
16%
7%
9%
64%
13%
6%
10%
47%
20%
36%
32%
Pico
Femto
0%
Macro
Micro
Figure 1: Simplified block diagram of a pico/femto-cell base-station (left) and the BS power consumption
breakdown for different cell-sizes (right).
3. Energy adaptation opportunities
Normally, base-stations are designed for maximal traffic load and maximal performance.
Unfortunately, the daily data traffic profile for cellular systems in a dense urban
environment (Figure 2) shows high variations. The highest utilisation ratio is achieved only
between 18.00 and 24.00.
The remaining time (18 hours), the base-station is under-utilized which opens the
opportunity for different techniques to enhance the BS energy efficiency while maintaining
the required traffic load.
Sleep modes: During low traffic periods (04.00 - 08.00) no or very few users are served
such that parts of the BS components can be switched off. A low energy sensing system
will monitor the network activity and wake-up the BS if required, while taking into account
transition times for activation and deactivation.
Run time savings: During reduced traffic periods, varying capacity should be provided
to the area and/or timeframe. This capacity is scaled by tuning the transceiver RF power or
Signal to Noise and Distortion (SiNAD), activation/deactivation the amount of antennas in
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a MIMO system, by scaling the bandwidth and by frequency and time-duty-cycling.
According the data frame structure, some specific BS components can also be
discontinuously activated (DTX) at symbol rate speed.
Heterogeneous deployment: At the network level, the BS energy efficiency
scalability can be further exploited. With a proper heterogeneous network, it could for
example be opportune during low traffic to deactivate several pico-cell BSs and to transfer
their traffic to a nearby macro-cell BS.
NormailizedTrafficLoad[%]
100%
80%
60%
40%
20%
0%
0
2
4
6
8
10
12
14
16
18
20
22
Time[hours]
Figure 2: Daily (24h) data traffic profile for cellular systems in a dense urban environment [7].
4. Energy adaptation solutions
In this section, attractive and realistic solutions are presented to adapt and enhance the
energy efficiency of the main power consumers in the pico/femto BSs: BB, RF and PA.
4.1 - Digital baseband engine
Due to the shrinking cell size and the rapidly growing signal processing complexity, the
energy consumption of digital baseband implementation is becoming more and more
dominant. Hence, optimizing the energy efficiency of digital baseband is crucial. The key
technique is to enable energy scalability for both the dynamic wireless communication
environment and the dynamic user requirement, such as the load of the system.
In general, baseband processing can be split into many components. Among other timedomain processing for filtering and up/down-sampling, frequency-domain processing for
modulation/demodulation or equalization, channel coding/decoding, pre-distortion,
platform control and backbone network serial link. These components have the potential for
Energy Adaptation (EA) according to the signal load (defined as the output power related to
the maximum specified transmission power) by adaptation of the bandwidth, modulation,
coding rate, number of antennas, duty-cycling in time or frequency etc. Figure 3 shows on
the left the power consumption of a 2x2 MIMO pico-cell BS BB engine over the signal
load. The UL power is almost double the DL given the more complex signal processing
needed at receiver side (MIMO signal detection, channel decoding...). The potential for EA
is mainly concentrated in the UL case because most of its signal processing is proportional
to the data throughput. In the DL case, BB processing is relatively independent of the
throughput (time-domain processing of the signal, platform control overhead...). The
overall power reduction ranges up to 20% at signal loads <20%. At higher signal loads, the
power reduction decreases.
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UL
ULEA
DL
DLEA
PotentialforEnergySavings
6
5
20%
10%
15%
Powerreduction
4
3
2
1
0
20%
40%
60%
SignalLoad[%]
80%
100%
PowerConsumption[W]
PowerConsumption[W]
7
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
UL+DL
UL+DLEA
PotentialforEnergySavings
58%
39%
32%
Powerreduction
0%
20%
40%
60%
80%
100%
SignalLoad[%]
Figure 3: Power consumption of a 2x2 MIMO pico-cell base-station BB engine (left) and RF transceiver
(right) over the traffic load.
4.2 - Analog RF transceiver
The introduction of energy adaptation in the base-station RF transceiver is inspired by
recent work in the area of handheld communication devices, where an analog software
defined radio (SDR) is implemented in low-cost and power-efficient CMOS technology [8].
This SDR builds on a simple zero-IF architecture which is highly reconfigurable over all its
analog building blocks. This flexibility permits to configure the radio over diverse
standards and operation conditions. The hardware provides flexibility over the operation
frequency and offers to control the filtering and amplification in the cascaded transmitter
and receiver stages. This control is particularly interesting in the context of energy
optimisation, as both the bandwidth and the gain/SiNAD performance are key components
for energy adaptation. To efficiently exploit the transceiver flexibility for run-time energy
optimization according to the traffic load, an off-line pruning is required to select all
relevant configurations.
In traditional base-stations, the RF transceiver targets the best SiNAD performance
independent of the signal load. From an energy consumption perspective, it is more
advantageous to scale the transceiver to provide a ‘just good enough’ SiNAD performance.
This is illustrated in Figure 3 (right), which shows the measured power consumption on [8]
for a 2x2 MIMO pico-cell base-station RF transceiver over the signal load. This figure
depicts the power consumption for the UL receiver, the DL transmitter and the complete
transceiver (UL+DL). The dotted lines correspond to the traditional approach whereas the
solid lines consider energy adaptation of scaling the SiNAD performance. Energy
adaptation is mainly beneficial at lower traffic load, where the transceiver power
consumption is reduced beyond 30% (signal load <50%) and up to 55% (signal load <5%).
4.3 - Power amplifier
In pico and femto base stations, the power amplifier does not represent the main consumer
block. However, because of the spectral mask restrictions and the lack of digital predistortion techniques, the operating peak-to-average-power-ratio (PAPR) must be increased
causing that the energy efficiency of the PA goes down significantly. The concept Adaptive
Energy Efficient Power Amplifier is proposed to lead the power consumption reduction
when the RF output power is lower than the maximum. The main features are:
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x Operating Point Adjustment – PA operating point is optimised according to required
RF output power level (signal load). At the same time, spectral mask and PAPR
specification are fulfilled.
x Deactivation of PA Stages – Enabling a fast switching on/off in the RF power
transistor, the consumption is reduced to the minimum when no RF output power is
required.
The implementation of this concept is made in cooperation with digital baseband and power
supply unit blocks. A line-up power amplifier with at least two stages is required. The first
stage has a fixed bias point; however, the second stage operating point is optimized through
variable bias circuitry, which is controlled remotely by digital base band.
Based on a 24dBm maximum rms RF output power (AB-class, PAPR=12dB) [9], the
benefits of Adaptive Energy Efficient (EE) PA (Figure 4) has been evaluated in five
different operating points (OP1 to OP5) and in switch off condition. Given that the
operating point for maximum signal load (OP1) represents the performance without
adaptation, the comparison between OP1 and the others provides the enhancement of the
proposed concept. The power consumption when PA is deactivated (0% signal load), is
drastically reduced around 80%. The improvements when PA is adjusted in function of the
signal load, also provides high reductions, mainly, for medium and low signal loads. For
instance, below 20% signal load, in OP5, the expected reduction is in the range of 55%. In
OP4, for signal load range 20-40%, the benefit is around 37%, while for OP3 (22%) and
OP2 (11%), the improvement is still quite significant.
AdaptiveEEPAPerformance
3.0
PowerConsumption,W
2.5
2.0
1.5
1.0
0.5
OP1
OP2
OP3
OP4
OP5
SWITCHOFF
0.0
0
10
20
30
40
50
60
70
80
90
100
SignalLoad,%
Figure 4: Power consumption performance of Adaptive EE PA.
4.4 - Matching network
Adaptive power amplifiers scale their RF power according to the signal load, but
classically, their load impedance is fixed. From an energy efficiency point of view, the load
impedance should however scale inversely proportional to the RF power. Recent
technological evolution facilitates high quality variable passive components to design low
loss Tunable Matching Networks (TMN) that allow dynamic load modulation. Figure 5
shows the PA efficiency curves with a classical fixed matching network (blue curve) and a
tunable matching network (green curve). The green curve is obtained from selecting the
optimal load impedance and input drive levels over the RF power. At 12dB back-off, which
corresponds with a realistic PAPR for e.g. LTE signals in pico/femto BSs, the energy
efficiency increases from 17% up to 32%, which corresponds to a 90% improvement. This
efficiency improvement however does not include the TMN loss and its tuning range
limitations. Figure 6 shows the estimated power consumption of a 24dBm PA for different
signal loads, considering 0.5dB and 1dB loss in the fixed matching and tunable matching
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networks respectively. The main energy savings are observed at high signal load: 40%
power reduction is achieved at full signal load and 30% power reduction at half signal load.
+90%
with Fixed Matching Network (IL=0.5dB)
2,8
with Tunable Matching Network (IL=1.0dB)
2,6
Power Consumption [W]
Efficiency
Improvement
3,0
2,4
2,2
2,0
30%
Power Reduction
40%
1,8
1,6
1,4
1,2
12dB back-off
1,0
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Signal load [% ]
Figure 5: PA efficiency curve (excluding
matching loss) with fixed matching (blue) and
tunable matching (green); black dots
represent efficiency data for different load
impedances and input power levels.
Figure 6: Power consumption for different signal loads.
4.5 - Tx/Rx antenna port
Standard RF front-end architectures for FDD communications are based on a 50 ȍ
reference impedance and make use of a duplexer to connect the Tx and Rx chains to the
antenna(s) (Figure 7). While this approach allows the separate optimization of each block of
the system, significant efficiency losses are caused in the matching network and duplexer
by the requirements on impedance matching and isolation. Typical state-of-the-art
duplexers, based on SAW or BAW technology, exhibit isolation levels between Tx and Rx
ports in excess of 50 dB. Insertion losses depend on the Tx and Rx frequency-bands
separation and are typically in the range 1.5-3 dB.
IL 1-2dB
PA
LNA
TMN
IL 1dB
IL 1.5-3dB
PA
Tx
Rx
Loss
0.5-1dB
IL 1dB
TMN
Tx
Rx
Isolation
> 30dB
LNA
Isolation > 50dB
Figure 7: Typical RF front-end architecture of a
pico/femto base station for FDD.
Loss < 0.5dB
IL 1dB
Figure 8: Low loss RF front-end architecture with
separate Tx/Rx antenna ports and relaxed filter
specifications.
Since the duplexer is a critical piece of the front-end, one may question the interest of
using a single antenna. Separate Tx/Rx antennas can provide some level of isolation that
may reduce the requirements of the Tx and Rx filters, and in turn result in lower insertion
losses for these filters. The antennas can be implemented as identical antenna elements
separated in space or can be the combination of different antenna elements into the same
volume in order to minimize the size. Custom antenna design allows the development of
dual-access (Tx/Rx) antennas (Figure 8) with a significant isolation between ports and low
impedance levels (<20 ohms). With such antenna, the PA matching network and Rx filter
isolation specifications can be significantly relaxed with a net benefit on insertion losses
and a global efficiency of the Tx front-end: the total insertion loss is reduced from 3.5dB
down to 2.5dB and the efficiency improvement ranges between 25% and 100%.
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5. System evaluation
This paragraph provides a system evaluation based on [7] for a typical 10MHz bandwidth
2x2 MIMO pico-cell with 24dBm maximum rms output power. The main objective is to
map a certain percentage of traffic load expressed in terms of relative rms RF output power
to the actual power consumption of the small-cell BS. FIGURE 9 shows a comparison
between a conservative state-of-the-art implementation (Earth OFF) with the power
scalable implementations we propose in this paper (Earth ON). Savings up to 50% can be
achieved in low load conditions. Furthermore, advanced sleep-modes can be easily
implemented that make the small BS consumes a stand-by power that is a small fraction of
the maximum. This is critical when small-cells are deployed in heterogeneous networks; at
low capacity, selected small-cell BS will be put to sleep to save energy.
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Earth OFF+Sleep Mode
Earth ON
Earth ON+Sleep Mode
CO
10
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PicoCell - 24dBm Max
15
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PA
80
60
40
20
RF Output Power [%]
100
0
80
60
40
20
RF Output Power [%]
100
FIGURE 9 – Pico-cell BS power consumption versus the signal load
12
100%
90%
10
80%
70%
60%
nooutput
50%
PSS,SSS,BCH
40%
CSRS
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Load[%]
0
FIGURE 10 – Example of FTP resource utilization
model
0
5
10
Day [h]
15
20
FIGURE 11 Possible savings over typical daily
traffic-load
In order to quantify the average savings over the dense-urban scenario described in section
3, an FTP resource utilization model is used [7], shown in FIGURE 10, which distributes
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probabilities of user data versus LTE signalling over the relative traffic-load that a pico-cell
can support. FIGURE 11 shows how both power model and resource utilization result in an
average power consumption distribution over the day that allows in relative terms savings
at the base-station up to 30%. From a system network perspective, this energy adaptation is
exploited by building heterogeneous networks with a flexible deployment and scheduling
depending on the user profiles, the traffic load and the environmental conditions (channel,
interference, etc.).
6. Conclusion
This paper presents new concepts to save energy in small-cell wireless communication
base-stations. The power consumption of these base-stations is dominated by three
components: the digital baseband engine (~30%), the analog RF transceiver (~12%) and the
power amplifier (~30%). For these components, energy adaptation solutions are identified
and quantified in function of the signal load. These solutions offer a high potential in
energy savings beyond the technological improvements while enhancing the flexibility to
adapt to the dynamic wireless communication scene. A system evaluation has been
performed on a pico-cell base-station (2x2 MIMO, 10MHz bandwidth) operating on LTE
signals and considering a daily data traffic profile in a dense-urban environment. This
evaluation indicates an average power consumption reduction of 30% when exploiting the
energy adaptation solutions described in this paper.
Acknowledgements
The research leading to these results has received funding from the European Community's
Seventh Framework Programme FP7/2007-2013 under grant agreement n° 247733 - project
EARTH.
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Copyright © 2011 The authors
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