Energy Efficient Mobile Communication
Abdulbaki Uzun, Axel K
Service-centric Networking, Deutsche Telekom Laboratories, TU Berlin, Berlin, Germany
Hans J. Einsiedler
Innovation Development, Deutsche Telekom Laboratories, Berlin, Germany
Mobile computing, Green ICT, Energy efficiency, Context-aware computing.
In order to fulfill today’s high demands on mobile network usage, mobile network providers in Germany have
around 100.000 base stations working 24/7. The permanent availability of those network components causes
a significant energy consumption. Through an adaptive and context-aware power management in mobile
networks, a considerable amount of energy can be saved by maintaining the high quality of experience at
the same time. There are a lot of contextual information present in mobile network components and end
devices, which can help to calculate decisions for a dynamic de- and reactivation of network components. This
position paper discusses the idea of a context entity that aggregates and processes various types of contextual
information, and the integration of it into the mobile network architecture.
Modern and future radio networks, such as WiFi,
UMTS or LTE, provide good connectivity and high
data transfer rates to the mobile user. In order to ful-
fill these requirements, mobile network providers in
Germany have around 100.000 base stations working
24/7 with a total power consumption of 760 GWh to
3040 GWh per year. However, these capacities are not
always fully utilized, e.g., in rural areas or at night.
Furthermore, mobile services that are mostly in use
today (e.g., telephony, SMS, etc.), do not have high
data rate requirements. Even though the demand on
bandwidth for mobile Internet increases continuously,
a lot of the services used today can be implemented
with older networks (e.g. GSM or EDGE) without
any noticeable loss in quality. These examples show
that there are massive potentials to save energy in ra-
dio access and core networks. This can be done by dy-
namically de- and reactivating network components
(e.g., base stations) or by adaptively reconfiguring the
network to the user’s needs based on a certain infor-
mation basis gained from the network.
The main objective of the project Communicate
Green is the development of an adaptive, context-
aware and technology-comprehensive power manage-
ment for modern radio networks, with which a consid-
erable amount of energy can be saved by maintaining
the high quality of experience at the same time. There
are a lot of contextual information present in mobile
network components and end devices, which can be
useful in order to calculate decisions for a dynamic
de- and reactivation of network components, such as
the current load of a cell, the average daily load of
a cell, the number of users in a cell, service usage
profiles or QoS requirements. This paper mainly dis-
cusses the idea of developing a context entity that ag-
gregates and processes various types of contextual in-
formation coming from mobile network components
as well as end devices, and the integration of this en-
tity into the mobile network architecture.
The project Communicate Green is about implement-
ing an adaptive and context-aware power manage-
ment in radio networks having a global view on dif-
ferent overlay networks. To achieve a high power effi-
ciency throughout different mobile network technolo-
gies, all parts of the system are going to be optimized.
Radio network infrastructures are most of the time
fully active even when they are not fully utilized (see
Uzun A., Küpper A. and J. Einsiedler H..
COMMUNICATE GREEN - Energy Efficient Mobile Communication.
DOI: 10.5220/0003399403020305
In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2011), pages
ISBN: 978-989-8425-48-5
2011 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1). Only in some regions, where the utilization
is foreseen, base stations are temporarily switched
off. The parameters are not very sophisticated and
dynamic, such as the time of the day or special knowl-
edge about certain activities in the region. A simple
optimization can be achieved by adjusting the param-
eters of a mobile network to the actual operating state
and by providing only the needed capacity. Possible
approaches are the dynamic de- and reactivation of
base stations and the transfer of coverage to neigh-
boring base stations.
Figure 1: Daily load profile of a telecommunication net-
work in January 2009, Ericsson GmbH.
The potential of optimizing mobile networks in
terms of energy efficiency can be increased by cre-
ating integrated power management concepts for het-
erogeneous radio networks. One possible approach is
the adaptive configuration and selection of radio net-
works based on decreasing the power consumption by
maintaining a high quality of experience for the user.
Figure 2 shows an exemplary night mode, in which
3G/4G cells and WiFi HotSpots are configured in a
way that only one base station is active in order to
guarantee basic services and a few WiFi HotSpots that
deliver broadband data rates.
Figure 2: Exemplary deactivation of network nodes.
Changes in radio access networks as they are men-
tioned above directly affect the core network. In order
to assure that all functions in the core network can be
delivered without any loss in the quality of service,
the core network has to be optimized as well. Virtual-
ization of functions is the key approach here.
The basis for these network optimizations is built
by various types of information present in mobile net-
work components, end devices and other sources
here named as context. A list of relevant contextual
information will be determined by examining differ-
ent mobile network context sources. Furthermore, a
service classification scheme will be developed show-
ing what kind of service can be delivered by what
kind of radio technology. The main focus of our work
in the project is the development of a context entity
that aggregates and processes contextual information
in order to calculate decisions for network optimiza-
tions. By applying various context acquisition ap-
proaches, both the network as well as the user view
will be included into the power management process.
The problem of energy consumption is also recog-
nized by other project groups. The EARTH project
(EARTH, 2010), for example, funded by the EU, in-
vestigates the energy efficiency of mobile communi-
cation systems in order to develop a new generation
of energy efficient equipment, components, deploy-
ment strategies and energy aware network manage-
ment solutions. It mainly focuses on LTE, its evolu-
tion LTE-A and beyond. Communicate Green, how-
ever, considers all available radio technologies (e.g.,
GSM, UMTS or WiFi) and applies energy efficient
decision algorithms on available hardware and net-
works based on network, service and user context.
Therefore, a context entity containing context
quality, provisioning, modeling and reasoning mecha-
nisms plays a big role in this project. A lot of research
has been done in this field so far. (Flor
een et al., 2005)
describe a context management framework developed
within the MobiLife project (MobiLife, 2006), which
handles context information, modeling and reason-
ing for mobile applications and services. For this
purpose, essential functionalities are split into differ-
ent components, which can be configured for differ-
ent tasks and reasoning methods. (Mannweiler et al.,
2009) uses the concept of a context management ar-
chitecture in order to apply context information for
intelligent radio network access (IRNA) purposes. As
presented by (Schneider et al., 2010), this architecture
is also able to integrate any kind of context provider
(e.g., sensor or mobile devices) in a ”plug-and-play”
manner irrespective of type, manufacturer or accuracy
in order to collect context information useful for the
mobile communication industry and network opera-
COMMUNICATE GREEN - Energy Efficient Mobile Communication
A good overview about context modeling ap-
proaches and reasoning techniques is given by (Bet-
tinia et al., 2010). The authors come to the conclu-
sion that hybrid context modeling approaches are a
promising direction discussing also a possible hybrid
The architecture comprises two parts: the functional
and the system architecture.
Figure 3: Functional Architecture.
The functional architecture (as illustrated in Fig-
ure 3) encapsulates the functions of the system to
logical entities and illustrates the relations between
them. An integral part of the functional architecture
is the data – here named as context information – with
which decisions for a dynamic de- and reactivation
of network components are calculated. Based on the
context model and service classification scheme men-
tioned above, various types of contextual information
can be considered.
The context entity collects and aggregates con-
textual information by applying two different ap-
proaches. In the net-centric context acquisition ap-
proach, data is only extracted from network compo-
nents. This approach has the advantage that already
available data (e.g., coarse location data delivered by
the HLR, VLR or RNC) can easily be integrated into
the context aggregation process. However, the user’s
view is completely ignored in this approach, so that
deactivations calculated by the system might lead to a
bad quality of experience for the user. In order to re-
duce this problem, context is also acquired using the
hybrid approach that considers context data from end
devices as well.
The decision entity uses the collected contextual
data in order to make a decision concerning de- or re-
activation of components, whereas the control entity
controls and manages de- and reactivations.
Figure 4: System Architecture.
In the system architecture (see Figure 4), the func-
tional entities are mapped to system components.
Data is directly extracted from network components
and from end devices via dedicated client applications
on modern smartphones. However, due to the mass of
different and diverse mobile end devices subscribed to
the mobile network, it is quite impossible to consider
all mobile users and operating systems. The UMTS
SIM Application Toolkit provides potentials to avoid
this problem since it allows the implementation of
value-added services on the SIM card that can acquire
different information from the network regardless of
the mobile phone in use. Because of this, context data
acquisiton possibilities via the USAT will be analyzed
and implemented as well.
The context, decision and control entity are
merged to one component called the Power Manage-
ment Component (PMC). Various options to integrate
this component into the topological structure of the
network are going to be examined. In a central archi-
tecture, for example, each mobile network provider
has a PMC in his core network controlling and man-
aging the whole infrastructure of the network. Using a
hierarchical approach, on the other hand, would lead
to many PMC nodes in the access networks controlled
by a central PMC in the core network. In a distributed
implementation, the PMC nodes are only in the access
networks organizing themselves when de- and reacti-
vating network components.
PECCS 2011 - International Conference on Pervasive and Embedded Computing and Communication Systems
The power consumption of base stations differs from
manufacturer to manufacturer. Typical values are be-
tween 750W and 3000W
per base station.
Table 1: Number of base stations in Germany.
Provider GSM UMTS Total
Deutsche Telekom 25000 11000 36000
Vodafone 20000 13000 33000
O2 17000 10000 27000
E-Plus 18804 6616 25420
80804 40616 121420
Taking the base station numbers (O2, 2010)
(KPN, 2009) (Vodafone, 2009) (Flatrate To Go, 2009)
in Table 1 as a reference, the total power consump-
tion of base stations in Germany is between 760 GWh
and 3040 GWh per year meaning that 410.000 to
1.645.000 tons of carbon dioxide is emitted in Ger-
many per year
(BDEW, 2008). For 2020, (The Cli-
mate Group, 2008) estimates a carbon emission of
349.000.000 tons for the telecommunication infras-
tructure, whereas the mobile environment will con-
tribute with 51%. (Remark: In 2002, the carbon
emission of the telecommunication infrastructure was
151.000.000 tons and the mobile environment cov-
ered 43% of it.) With the optimizations being done
in the mobile network, we estimate a reduction of the
operating time of network components up to 40%
60%, i.e., a reduction of the carbon dioxide emission
up to 328.000 to 1.974.000 tons per year.
The objective of the project Communicate Green
comprises the development of an adaptive and
context-aware power management. The decision and
adaptation algorithms that are going to be imple-
mented throughout the project, are going to save en-
ergy in mobile networks by dynamically de- and re-
activating network components and by reconfiguring
the network to user needs. Optimizations will be pro-
posed for single and heterogeneous radio technolo-
gies as well as for the core network. Our part of the
project concentrates on the development of a context
Numbers extracted from data sheets of the companies
Ericsson, Motorola, Nokia Siemens Networks and Huawei
Calculation is based on the average value for carbon
dioxide emission in Germany 2007, 541 g/kWh
entity that collects and processes contextual informa-
tion from various context sources in order to build the
data basis for decision calculations. The expected re-
sults as listed above show that the projects approach
is innovative and promises an enormous reduction of
the power consumption in future mobile networks.
We thank all partners in the consortium of the
Communicate Green project funded by the Federal
Ministry of Economics and Technology (BMWi):
Deutsche Telekom Laboratories, Ericsson GmbH,
Fraunhofer Gesellschaft zur F
orderung der ange-
wandten Forschung e.V., Universit
at Paderborn,
Technische Universit
at Berlin.
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EARTH (2010). Energy Aware Radio and Network
Technologies., Accessed:
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een et al. (2005). Towards a context management
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Communications Summit.
KPN (2009). Annual Report.,
Accessed: 2010/12/14.
Mannweiler, C., Klein, A., Schneider, J., and Schotten, H.
(2009). Exploiting user and network context for intel-
ligent radio network access. In Proceedings of ICUMT
International Conference on Ultra Modern Telecom-
munications, St. Petersburg, Russia.
MobiLife (2006). Life goes mobile., Accessed: 2010/12/15.
O2 (2010). Press Release, O2 with highest customer
growth in 2009., Ac-
cessed: 2010/12/14.
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context-aware systems. ICaST - ICST’s Global Com-
munity Magazine.
The Climate Group (2008). Smart 2020: Enabling the low
carbon economy in the information age. Technical re-
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COMMUNICATE GREEN - Energy Efficient Mobile Communication