Machine Learning for Cognitive Radio Networks
Vera Stavroulaki, Aimilia Bantouna,
Yiouli Kritikou, Kostas Tsagkaris,
Panagiotis Demestichas,
Pol Blasco, Faouzi Bader, Mischa Dohler,
Daniel Denkovski, Vladimir Atanasovski,
Liljana Gavrilovska, and
Klaus Moessner
Learning mechanisms are essential for the attainment of
experience and knowledge in cognitive radio (CR) sys-
tems, exposed to high dynamics with often unpredictable
states [1]. These mechanisms can be associated with user
and device profiles, context, and decisions. The focus learning
user preferences is the dynamic inference and estimation of cur-
rent and future user preferences. The acquisition and learning of
context information encompasses mechanisms for the system to
perceive its current status and conditions in its present environ-
ment, as well as estimating (and forecasting) the capabilities of
available network configurations. Finally, learning related to
decisions addresses the building of knowledge with respect to
the efficiency of solutions that can be applied to specific situa-
tions encountered. Based on knowledge obtained through learn-
ing, decision-making mechanisms can become faster, since
the CR system can learn and immediately apply solutions
that have been identified as being efficient in the past.
Moreover, knowledge obtained through learning
mechanisms may be shared among nodes of a sys-
tem. Thus, more reliable and more optimal deci-
sions can be made by exploiting knowledge
obtained through learning mechanisms.
In this context, this article presents various
basic learning functionalities and relevant
requirements for the identification, collection,
and processing of information that can lead to
exploitable knowledge. The article also provides
an overview of potential implementation
approaches for these functionalities that
exploit diverse machine-learning techniques.
Scenarios
This section introduces a set of indicative
scenarios focusing on information acquisi-
tion and processing, learning, and knowl-
edge management. The target of these
scenarios is to provide an overview of CR
applications, where learning and knowl-
edge management play an important role.
Digital Object Identifier 10.1109/MVT.2012.2190196
Date of publication: 13 April 2012
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In this respect, the particular scenarios have been selected
as they include different facets of information acquisition,
processing, and exploitation in the scope of advanced
coexistence technologies for radio optimization in licensed
and unlicensed spectrum.
Knowledge-Based Management of
Reconfigurable B3G Infrastructures
This scenario focuses on the operation of a cognitive net-
work management system (CNMS), which constantly moni-
tors the network context and is able to detect a problematic
situation [e.g., severe quality-of-service (QoS) degradation]
that has occurred in a network area (Figure 1).
The CNMS is able to handle these problematic situa-
tions by making the appropriate decisions for the reconfi-
guration of the managed infrastructure. Moreover, the
CNMS stores information on encountered contextual sit-
uations, including the solution that was applied for
handling them in a reference context repository. This
allows considering past network interactions to allow
faster and more efficient handling of problems. The on-
time reaction of the CNMS guarantees the smooth network
operation and the provision of services and applications
at the desired quality levels.
Self-Optimization of Cognitive User Devices
This scenario considers the behavior of user devices
that can comprise various awareness functionalities so
as to decide in an autonomous manner on the actions
they have to take to ensure that all of their running serv-
ices are constantly obtained at the best possible QoS
level. The need for action comes from the volatile con-
text of the network environment. There can be several
operators in the area, offering the same services but with
different quality and reliability. The user device, based
on the knowledge and experience developed over
time, when faced with similar contextual situations, can
autonomously make appropriate reconfiguration deci-
sions and continue to efficiently operate despite the
changing network conditions. A high-level view of the
Context Matching(k-NN)
ContextAcquisition
Registry
Request ID Solution ID
201202001 201202101
201202002 201202201
201202003 201202202
Registry
Request ID Solution ID
201202001 201202101
201202002 201202201
201202003 201202202
Context
1
2
Reference ContextRepository
Wireless Network Segment
ReconfigurationEnforcement
KnownSolution 3
Optimization Process(Decision Making)
2
Unknown
Context
New Context
CNMS
Profilesand
Policies6
ApplySolution
4
5Decision–Solution
Add New Contextand Solution
FIGURE 1 Knowledge-based management of reconfigurable B3G infrastructures [2].
LEARNING MECHANISMS ARE ESSENTIALFOR THE ATTAINMENT OF EXPERIENCE ANDKNOWLEDGE IN COGNITIVE RADIO SYSTEMS,EXPOSED TO HIGH DYNAMICS WITH OFTENUNPREDICTABLE STATES.
92 ||| IEEE VEHICULAR TECHNOLOGY MAGAZINE | JUNE 2012
process of self-optimization of cognitive devices is pro-
vided in Figure 2.
Cognitive Systems and Opportunistic Networks
This scenario addresses the use of opportunistic net-
works (ONs) for enabling devices to communicate over
infrastructure networks even if there is no direct connec-
tion to an infrastructure network [3] (Figure 3).
The ONs are operator governed (through the provision
of resources and policies) and can be coordinated exten-
sions of the infrastructure for a particular time interval.
For the scope of this scenario, various learning and knowl-
edge mechanisms can be useful. These include mecha-
nisms for the identification and learning of context
information, such as identification of spectrum opportuni-
ties [4] for the setup of ONs, and candidate ON nodes such
as devices in the vicinity, identification, and learning of
user preferences.
Learning and Knowledge Management
Mechanisms
Figure 4 illustrates an overview of learning mechanisms
that can be derived from the analysis of the scenarios out-
lined in the previous section.
Acquiring and Learning User Information
The goal of this mechanism is to acquire, maintain, and
process information on user preferences and behavior as
well as the capabilities of the user device. Indicative infor-
mation includes: 1) the set of potential configurations
(e.g., the radio access technologies that the mobile device
is capable of operating with and the associated spectrum
and transmission power levels), 2) the set of services that
can be used and the sets of QoS levels associated with the
Acquiring andLearning User
Information
Acquiring and Learning Context
Information
AcquiringPolicies
Decision on OptimalDevice Configuration
Update of UserInformation and
Knowledge
Update of ContextInformation and
Knowledge Acquisition of Policies
Refinement of Userand Context
Information Under Policies
Selection of OptimalDevice Configuration
Policies
Cognitive User Device
User Information and Knowledge
Context Information and Knowledge
FIGURE 2 Self-optimization of cognitive user devices.
Macro BaseStation 1
Macro BaseStation 2
ON 1(Opportunistic Coverage Extension)
ON 2 (OpportunisticCapacity Extension)
FIGURE 3 Coverage and capacity extension with ONs.
JUNE 2012 | IEEE VEHICULAR TECHNOLOGY MAGAZINE ||| 93
use of a service, 3) the utility volume associated with the
use of a service at a particular quality level [quality of
experience (QoE)], and 4) the maximum price that the
user is willing to pay to use certain services at specific
QoS levels, i.e., for a certain QoE. Furthermore, this func-
tionality deals with learning elements of the user behavior.
The term utility is borrowed from economics, where utility
is used to express the satisfaction obtained from the con-
sumption of goods or services. In this scope, the utility
volume provides a ranking (by order of preference) of
service and QoS combinations, similar to the mean opin-
ion score. User preferences may vary depending on the
contextual situation and may change over time. Therefore,
the utility volume depends on a range of context-related
observable parameters. More specifically, the utility
volume, apart from the service and QoS level, may be
related to the location of the user, the time zone, the user’s
role, and the feedback obtained from the user.
Acquiring and Learning Context Information
On the network side, this mechanism enables collection of
the status of network elements (base stations), the status
of their environment as well as the user devices [5]. Essen-
tially, each element may use monitoring and discovery
procedures [6], which can be sensing based [7], and/or
pilot channel based [8]. Monitoring procedures provide,
for each network element of the segment and for a specific
time period, the traffic requirements, the mobility condi-
tions, the current configuration in terms of operating
parameters (e.g., spectrum assignment and power level)
and the QoS levels offered. Furthermore, it includes proce-
dures for capturing the channel state information, which
is reflected by the estimation of the mean signal-to-noise
ratio value for each subcarrier. Context information is
used by the system for the assessment of key performance
indicators, used for addressing potential problems and
triggering appropriate optimization procedures whenever
necessary. Diverse types of context information storage
may be applied depending on the aforementioned types of
information. The notion of reference context repository
outlined earlier enables the storage of formerly encoun-
tered contextual situations in addition to the correspond-
ing solution that was selected by an optimization process
for addressing these situations. The concept of radio envi-
ronment maps enables the storage of context and policy
information [9].
This mechanism enables user devices to obtain infor-
mation about the network context in a radio environment
with various coexisting technologies. This network
context information should include data about available
access technologies in a given area and their corres-
ponding status (e.g., used frequencies, available resour-
ces, and coverage), information about the device status
(e.g., coverage at the current location, power available,
and technology capabilities), information about the status
of other devices in the area (e.g., activity and the ability to
cooperate). It should be noted that such information can
be directly obtained at the mobile device (e.g., received
power level) as well as at other nodes (e.g., load level in a
given access point). Therefore, appropriate mechanisms
are required, which enable the exchange of context
information/knowledge between nodes in a cognitive net-
work. Learning context information on devices aims to
estimate the most likely capabilities (in terms of QoS
parameters, such as bit rate) that
can be achieved by a given con-
figuration, in a given location, and
time zone. Such estimations can
be exploited to increase reliability
of decisions. Functionality for learn-
ing context information also ena-
bles estimation of situations that
are likely to be encountered in
future time points, given a certain
situation in the current time zone,
thus enabling proactive handling of
certain circumstances.
Acquiring, Deriving,
and Maintaining
Policy Information
This mechanism is responsible for
the acquisition, derivation, and
management of information related
to policies. Policies specify rules or
constraints that should be taken
into account for the selection of the
Acquiring andLearning User
Information
Acquiring andLearning Context
Information
Acquiring,Deriving and Maintaining
Policies
Decision Making(Knowledge-Based
Optimization)
ConfigurationActions
Knowledge(e.g., Encountered
Contextual Situations, Past Decisions/Actions,
Policies)
FIGURE 4 High-level view of learning functionalities in the scope of self-management in CR
systems.
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optimal configuration of a service area, network element,
or user device. In this sense, policies refine the set of data
comprised in context information.
Policies are usually stored in a database. A policy
database may be used as a central storage point for
the policies and policy users in the network. Such a
database enables keeping track of active users and
policies in the network as well as user-policies associa-
tions. Policy information should form a set of database
tables so that fast and efficient usage of the stored infor-
mation is enabled.
A key feature related to policies in a CR system is the
derivation of spectrum-sharing policies on the spectrum
vacancies (currently not utilized by primary users) for the
secondary users. The policy derivation requirements can
be divided based on the following:
n input parameters (sensing based and nonsensing based)
n type of derivation (reactive, history/predictive derivation)
n type of policies (based on location, functionalities,
connectivity and spectrum).
The derivation of spectrum policies may be realized in
a static or dynamic manner. The static approach relies on
long-term environment measurements, allows learning of
certain repetitive behaviors in the network and leads to
derivation of static spectrum sharing policies. The
dynamic approach relies on shorter real-time measure-
ments that enable policy derivation coping with the dyna-
mism of the wireless environment on a shorter time scale.
Learning Related to the Efficiency of Decisions
The goal of this mechanism is to build knowledge related to
decisions regarding the system configuration and to exploit
this knowledge to decrease the time required to reach a deci-
sion. More specifically, this functionality comprises mecha-
nisms that allow storing information about problems that
occur (e.g., QoS degradation) as well as their solutions and
to identify whether a problem currently addressed is similar
to an older one for which a solution is already available. This
allows for the overall decision-making process to speed up,
because it becomes less complex due to the limited number
of choices that should be checked. Thus, required system
adaptations can be performed faster.
This mechanism should enable
recording and storing of informa-
tion on situations encountered,
actions that were taken in reaction
to these situations, and, potentially,
information on the effectiveness/
efficiency of decisions taken. A
simple way of implementing this is
with the use of an appropriate
registry table. Implicitly, some of
the requirements for this function-
ality are related to necessities for
context acquisition.
A key requirement for this functionality is related to
the ability of the system to identify similar contextual sit-
uations so that it can be derived whether a previously
selected and enforced action should be directly applied
without the need of executing anew the decision-making
process. The direct application of known (good) solutions
facilitates the reduction of the time required to handle
contextual situations.
Docitive Networks
Despite cognition and learning having received a consider-
able interest, the process of knowledge transfer, i.e.,
teaching, over the wireless medium has received fairly lit-
tle attention to date. To this aim, in [10], a new and largely
unexploited paradigm referred to as docitive radios has
been introduced. The term docitive comes from ‘‘docere’’
= ‘‘to teach’’ in Latin, which relates to radios (or general
networking entities) and transfer their prior knowledge to
other radios. These radios are not only supposed to teach
end results (e.g., in form of ‘‘I sense the spectrum to be
occupied’’) but rather elements of the methods of getting
there. It capitalizes on the advantages but, most impor-
tantly, mitigates major parts of the aforementioned draw-
backs of purely CRs.
As illustrated in Figure 5, the canonical cognitive cycle
is advantageously extended by docition. It is realized by
means of an entity that facilitates knowledge dissemina-
tion and propagation with the nontrivial aim to help
learning. With this valuable knowledge at hand, a
newcomer CR is able to reduce the exploration phase and
increase its performance. In [11], docition is applied in the
context of femtocell networks. Femtocells use Q-learning,
an algorithm that originates from reinforcement learning,
Action
Information Acquisition
and Learning
Cognitive Radio
Docitive RadioDocition
DecisionDecision
FIGURE 5 Canonical cycle and its extension through docition.
LEARNING RELATED TO DECISIONSADDRESSES THE BUILDING OF KNOWLEDGEWITH RESPECT TO THE EFFICIENCY OFSOLUTIONS THAT CAN BE APPLIED TOSPECIFIC SITUATIONS ENCOUNTERED.
JUNE 2012 | IEEE VEHICULAR TECHNOLOGY MAGAZINE ||| 95
to allocate the optimal power and frequency to transmit.
In this context, the most expert femtocells share their
Q-tables with the newcomer femtocells that join the net-
work. Within this extended cognitive cycle, newcomer
femtocells capitalize on docition to use the knowledge
of expert radios and avoid learning from scratch. The
benefits of docition are gauged against regular CR
networks, showing better speed of convergence and
higher accuracy.
Docition is inspired by the so-far-successful problem-
based learning (PBL) concept used at schools. In PBL,
teachers are encouraged to be coaches and not informa-
tion providers, with the aim to have pupils work as a team
using critical thinking to synthesize and apply knowledge,
apprehend through dialog, questioning, reciprocal
teaching, and mentoring. Translated back to the wireless
setting, this implies a distributed approach where
nodes share potentially differing amounts of intelligence
acquired on the run. This, in turn, is expected to
sharpen and speed up the exploration process. Any
achieved gains, however, need to be gauged against the
overhead incurred as a result of the exchange of doci-
tive information.
The potential impact of docitive networks into next-
generation high-capacity wireless networks is ensured by
means of latest European Telecommunications Standards
Institute Broadband Radio Access Networks standardiza-
tion activities [12].
An Overview of Potential
Implementation Approaches
Learning and knowledge management mechanisms for CR
applications may be implemented by exploiting a variety
of machine-learning and pattern-recognition approaches-
such as Bayesian networks, neural networks, and fuzzy
cognitive maps [13]. This section provides an overview of
potential (indicative) approaches for the implementation
of learning and knowledge management for CR applica-
tions, focusing on a subset of machine learning techniques.
Learning User Preferences with the Use of
Bayesian Statistics
This section presents an approach for dynamically
learning user preferences regarding the perceived QoS
level per service/application [14] with the use of Bayesian
statistics concepts. The aim is to estimate the probability
of the level of user satisfaction for a specific service
and perceived QoS level, given a certain location and
time zone.
The user profile can be modeled as a collection of
parameters that can be classified into two main groups:
observable and output parameters. Observable parame-
ters include the currently running services/applications,
corresponding QoS levels, and associated QoS parameters
such as location, time zone, and provided user feedback.
User feedback is obtained in the following manner. The
user initiates a specific service. At the initial stages, it is
considered that the user is indifferent between service
provision choices. Every time the user obtains a service, a
rating facility, embedded in the learning mechanism,
allows the user to rate how much he/she liked the particu-
lar service provision. Different rating options are pro-
vided, also enabling nonexperts to provide the system
with feedback on their preferences. Users are also given
the choice to decline by providing a rating. The value of
output parameters is dynamically updated over time
based on the value of observable parameters. The focus
here is on one output parameter, namely, the utility
volume. The utility volume provides a ranking, by order of
preference, of service and QoS combinations. User prefer-
ences may vary depending on the contextual situation and
may change over time. Conditional probabilities for the
utility volume are calculated. These yield the most likely
user preferences per QoS level, which in turn can be used
as input in a decision-making mechanism for deciding, for
instance, on the most appropriate configuration of a
user’s device.
Learning Context Information with the Use
of Bayesian Networks
In this case, Bayesian statistics are used to estimate the
future behavior of candidate networks in terms of QoS
capabilities based on collected measurements.
The set of candidate networks is determined by the
context of operation, the device profile, and the policies of
the network operator (NO). Initially, the context in which
the user and device are found and the capabilities of the
device provide the networks that are available in the area.
The set of candidate networks is a subset of the available
networks, as it comprises only those available networks
that are compliant with the policies of the NO. Compliance
with the policies means that the NO allows the selection of
the networks for the particular user and device.
Input, i.e., measurements from candidate networks,
may be collected for offline or online analysis. In the cur-
rent approach, online analysis is applied, and its objective
is to estimate the future state using the observations up to
the current time. This estimation is modeled as a probabil-
ity density function. The advantage of this approach is
that no past observations need to be explicitly stored.
THE ACQUISITION AND LEARNING OF CONTEXTINFORMATION ENCOMPASSES MECHANISMSFOR THE SYSTEM TO PERCEIVE ITS CURRENTSTATUS AND CONDITIONS IN ITS PRESENTENVIRONMENT, AS WELL AS ESTIMATING (ANDFORECASTING) THE CAPABILITIES OFAVAILABLE NETWORK CONFIGURATIONS.
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QoS levels for networks are considered as a combina-
tion of QoS parameters. Each parameter can refer to a spe-
cific aspect, e.g., bit rate and delay. The QoS capabilities of
the candidate networks are determined by the context of
operation (e.g., interference conditions) and by the poli-
cies of the operator.
In summary, the overall learning process evolves as fol-
lows. Measurements are collected from the candidate net-
works, which are in line with operator policies. Based on
these measurements, the conditional probabilities are
updated, which provide an estimation of how probable it
is that a specific QoS parameter will reach a certain value,
given a certain network. The next step is the update of the
probability density function values, which offers a more
aggregate estimation regarding the probability to achieve
a certain combination of QoS parameters that corre-
sponds to a QoS level, given a certain network. These
probabilities can be exploited by knowledge-based net-
work selection schemes [15].
Learning Context Information with the Use
of Self-Organizing Maps
This section focuses on an implementation approach for
learning context information with the use of self-organiz-
ing maps (SOMs). The elements that serve as inputs for
discovering the network capacity are parameters that are
obtained, given a configuration, e.g., received signal
strength identifier and number of input bytes. The variable
that expresses the network capacity is the bit rate.
According to this approach, SOM is used for mapping
multidimensional data in a two-dimensional (2-D) map. To
do so, SOM requires a training process where the data are
converted in data samples and, finally, in vectors that are
mapped with respect to their resemblance. Thus, the cre-
ated map depicts the clustering of data based on the
resemblance of the corresponding vectors.
At this point, it is essential to clarify that the term data
sample differs from the term data as follows. A data sam-
ple consists of more than one data. In fact, each data sam-
ple is a combination of values, each of which refers to a
different observed parameter.
When applying the SOM approach, as soon as a pat-
tern has been identified, a new data sample can be
mapped on it using the same process. The identification
of patterns in data is based on the Euclidean distance
(selected due to its straightforward applicability)
between vectors of data samples. Data samples that are
similar to each other will be grouped into the same
cluster. This means that the bit rate observed at the
same time with the variables that formulate one data
sample of the cluster is expected to be the same with
the respective bit rate of the other data samples of the
cluster. Thus, for inferring the network capacity, the
cluster in which a newly obtained entry belongs is iden-
tified. Further information about the SOM technique
and its application to learning context information can
be found in [16].
Enabling Learning Related to the Efficiency of
Decisions with the Use of k-Nearest Neighbor(s)
Learning related to the efficiency of decisions requires the
identification of the similarity of contextual situations so
as to enable appropriate updating of information. For the
identification of the similarity of contextual situations an
approach based on the k-nearest neighbor(s) (k-NN) algo-
rithm [17] can be applied. Elements of the current context
information are compared with past network conditions
(reference contexts) based on several threshold compari-
sons regarding the total number of users, the Euclidean
and profile distance among users (the Euclidean distance
is a typical metric used in k-NN algorithms). More specifi-
cally, given the current and a set of reference contexts, the
algorithm checks each reference context to match with
the current context according to an overall distance. The
overall distance is based on 1) the total number of ses-
sions distance, 2) the total number of sessions per service
distance, and 3) the sessions distribution distance. Taking
into account these subdistances, the overall distance for
each reference context from the current context is calcu-
lated as the sum of the distances calculated in each phase.
The outcome is the reference context with the minimum
overall distance from the current context, if such exists in
the reference context repository. If no such context
exists, then a new record is created in the repository for
the current context. If it exists, then the decision that
was recorded for this solution is derived. If it is rated as a
good decision, it can be applied without the need of
running an optimization process. If the decision applied
in the past has a bad rating, the optimization process
can be triggered and the previously recorded decision can
be replaced in the repository. In all cases, after the imple-
mentation of the decision, the rate on the efficiency of
the decision corresponding to the identified context is
updated accordingly.
Conclusions
The article presented various basic learning functional-
ities for the identification and processing of information
that can lead to exploitable knowledge in CR networks.
The article also provided an overview of potential imple-
mentation approaches for these functionalities that
exploit diverse machine-learning techniques.
POLICIES SPECIFY RULES OR CONSTRAINTSTHAT SHOULD BE TAKEN INTO ACCOUNT FORTHE SELECTION OF THE OPTIMALCONFIGURATION OF A SERVICE AREA,NETWORK ELEMENT, OR USER DEVICE.
JUNE 2012 | IEEE VEHICULAR TECHNOLOGY MAGAZINE ||| 97
Acknowledgment
This work supports training activities in the context of
advanced coexistence technologies for Radio Optimiza-
tion in Licensed and Unlicensed Spectrum -Network of
Excellence (ACROPOLIS) project (http://www.ict-acropolis.
eu). This article reflects only the authors’ views, and the
community is not liable for any use that may be made of
the information contained therein.
Author Information
Vera Stavroulaki ([email protected]) received her diploma
degree in informatics from Athens University of Econom-
ics and Business in 2000 and Ph.D. degree in electrical
and computer engineering from the National Technical
University of Athens in 2004. She is currently an assist-
ant professor at the Department of Digital Systems,
University of Piraeus, Greece. She has ten years of
experience in European and national research and devel-
opment projects. Her current interests include cognitive
management functionality for autonomous, reconfigur-
able devices and networks, service governance and
virtualization, cloud computing, Internet of things, and
future Internet.
Aimilia Bantouna ([email protected]) received her
diploma degree from the School of Applied Mathematics
and Physical Sciences of the National Technical Univer-
sity of Athens in 2007. She obtained her M.Sc. degree in
digital communications and networks from the Depart-
ment of Digital Systems, University of Piraeus, Greece, in
2010. Since June 2011, she has been pursuing her Ph.D.
degree in machine-learning techniques of future net-
works and is a research engineer at the University of
Piraeus Research Center. She is also involved in various
European Union (EU)-funded projects.
Yiouli Kritikou ([email protected]) received her
diploma degree in 2003 and her Ph.D. degree in 2009
from the Department of Digital Systems at the University
of Piraeus. Since September 2003, she has been a
research engineer at the University of Piraeus, Labora-
tory of Telecommunication Networks and Integrated
Services. She also conducted a postdoc research in
advanced services in the context of the future Internet.
She has participated in numerous national and interna-
tional projects. Her research interests include design,
specification, and development of services for wireless
networks, concentrating on user profile, ubiquitous
service delivery, and technoeconomic aspects of serv-
ices delivery.
Kostas Tsagkaris ([email protected]) received his
diploma and Ph.D. degree (Ericsson’s Awards of Excel-
lence in Telecommunications) from the School of Electri-
cal and Computer Engineering at the National Technical
University of Athens, Greece. Currently, he works as a
senior research engineer and adjunct lecturer at the
Department of Digital Systems in University of Piraeus,
Greece. He has been involved in many research projects
and published more that 100 papers in the areas of
design, management, and optimization of wireless auto-
nomic/cognitive networks.
Panagiotis Demestichas ([email protected]) is a
professor at the Department of Digital Systems, Univer-
sity of Piraeus, Greece, where he is also the head of the
department and leader of the Laboratory of Telecommu-
nication Networks and Integrated Services. He received
his diploma and Ph.D. degrees in electrical engineering
from the National Technical University of Athens. He has
been involved in numerous international research and
development programs. His research interests include
design and performance evaluation of wireless and wire-
line, broadband networks, software engineering, service
and network management, algorithms and complexity
theory, technoeconomic management of networks, and
services. He has several publications in international
journals and conferences.
Pol Blasco ([email protected]) graduated in tele-
communications engineering in 2009 at the Technical
University of Catalonia [Universitat Politecnica de Cata-
lunya (UPC), Barcelona]. He graduated in electrical and
information theory engineering in 2008 at the Technical
University of Darmstadt (Germany). In 2011, he obtained
his M.Sc. degree for the Signal Theory Department of
UPC (Barcelona). Between 2007 and 2009, he pursued
research in several international research centers and
agencies. In 2009, he joined the Technical Telecommuni-
cation Centre of Catalonia (CTTC) to pursue his Ph.D.
degree in the area of intelligent energy.
Faouzi Bader ([email protected]) received his Ph.D.
degree (with honors) in telecommunications at the Uni-
versidad Polit�ecnica de Madrid-Spain in 2002. Then, he
joined CTTC in Barcelona-Spain as a research associate,
in which since 2006, he was a senior researcher. He is a
Senior Member of the IEEE. His research interests
include broadband networks, radio resource manage-
ment (RRM) in cognitive radio, and relaying scenarios.
Mischa Dohler ([email protected]) is leading
the Intelligent Energy group at CTTC in Barcelona, with
a focus on smart grids and green radios. He is working
on machine-to-machine, wireless sensor, femto, coopera-
tive, cognitive, and docitive networks. He has published
around 150 technical journal and conference papers at a
citation h-index of 26 and citation g-index of 55, holds a
BASED ON KNOWLEDGE OBTAINED THROUGHLEARNING, DECISION-MAKING MECHANISMSCAN BECOME FASTER, SINCE THE CR SYSTEMCAN LEARN AND IMMEDIATELY APPLYSOLUTIONS THAT HAVE BEEN IDENTIFIED ASBEING EFFICIENT IN THE PAST.
98 ||| IEEE VEHICULAR TECHNOLOGY MAGAZINE | JUNE 2012
dozen patents, authored, coedited, and contributed to
19 books, has given 25 international short courses, and
participated in various standardization activities. He is
the editor-in-chief of ETT and has been holding various
editorial positions for numerous IEEE and non-IEEE jour-
nals. He is a Senior Member of the IEEE and Distin-
guished Lecturer of IEEE ComSoc.
Daniel Denkovski ([email protected])
received his Dipl.-Ing. and M.Sc. degrees in Telecommu-
nications at Ss. Cyril and Methodius University (UKIM)
in Skopje, in 2008 and 2010, respectively. He is currently
a Ph.D. student in electrical engineering and information
technologies at UKIM. Since 2009, he has been a re-
searcher and a member of the Wireless Networking
Group at the Faculty of Electrical Engineering and Infor-
mation Technologies. His research work in the areas of
cognitive wireless networking and policing, spectrum
sensing, cooperative communications, and RRM has
resulted in more than 20 publications in relevant confer-
ences and journals.
Vladimir Atanasovski ([email protected])
currently holds the position of assistant professor at the
Institute of Telecommunications at the Faculty of Electri-
cal Engineering and Information Technologies, UKIM in
Skopje. He received his B.Sc., M.Sc., and Ph.D. degrees
from UKIM in Skopje, in 2004, 2006, and 2010, res-
pectively. He is the author/coauthor of more than 70
research journal and conference publications and
technical papers. His major research interests lie in
the areas of cognitive radio networks, resource man-
agement for heterogeneous wireless networks, traffic
analysis and modeling, cross-layer optimizations, and
ad-hoc networking.
Liljana Gavrilovska ([email protected]) cur-
rently holds the positions of full professor, head of the
Institute of Telecommunications and the Center for Wire-
less and Mobile Communications at the Faculty of
Electrical Engineering and Information Technologies, in
Skopje. She is the author/coauthor of more than 150
research journal and conference publications and
technical papers, a coauthor of the book Ad Hoc Net-
working Towards Seamless Communications (Springer,
2006) and coeditor of the book Application and Multidisci-
plinary Aspects of Wireless Sensor Networks (Springer,
2010). Her major research interest is concentrated on
cognitive radio networks, future mobile systems, wireless
and personal area networks, cross-layer optimizations,
ad-hoc networking, traffic analysis, and heterogeneous
wireless networks.
Klaus Moessner ([email protected]) is a
professor in the Centre for Communication Systems
Research at the University of Surrey, United Kingdom.
He earned his Dipl-Ing (FH) degree at the University of
Applied Sciences in Offenburg, Germany, an M.Sc. degree
from Brunel University, United Kingdom, and his Ph.D.
degree from the University of Surrey. His research inter-
ests include reconfigurability of the different system
levels, including reconfiguration management, service
platforms, service-oriented architectures, and IMS, as
well as scheduling in wireless networks and adaptability
of multimodal user interfaces.
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[11] A. Galindo-Serrano, L. Giupponi, and M. Dohler, ‘‘Cognition and doci-tion in OFDMA-based femtocell networks,’’ in Proc. IEEE Global Commu-nications Conf. (IEEE GLOBECOM), Miami, FL, Dec. 6–10, 2010, pp. 1–6.
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[14] V. Stavroulaki, Y. Kritikou, and P. Demestichas, ‘‘Acquiring andlearning user information in the context of cognitive device manage-ment,’’ in Proc. IEEE ICC, 2009, Dresden, Germany, pp. 1–5.
[15] P. Demestichas, A. Katidiotis, K. Tsagkaris, E. Adamopoulou, andK. Demestichas, ‘‘Enhancing channel estimation in cognitive radiosystems by means of Bayesian networks,’’ Wirel. Pers. Commun.,vol. 49, no. 1, pp. 87–105, Apr. 2009.
[16] A. Bantouna, K. Tsagkaris, and P. Demestichas, ‘‘Self-organizingmaps for improving the channel estimation and predictive modellingphase of cognitive radio systems,’’ in Proc. 20th Int. Conf. ArtificialNeural Networks, (ICANN), Thessaloniki, Greece, Sept. 15–18, 2010.
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THE FOCUS LEARNING USER PREFERENCES ISTHE DYNAMIC INFERENCE AND ESTIMATION OFCURRENT AND FUTURE USER PREFERENCES.
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