1
Spectrum Decision in Cognitive Radio Networks: A
Survey
Moshe Timothy Masonta, Student Member, IEEE, Mjumo Mzyece, Member, IEEE, and Ntsibane Ntlatlapa, Senior
Member, IEEE
Abstract—Spectrum decision is the ability of a cognitive radio
(CR) to select the best available spectrum band to satisfy
secondary users’ (SUs’) quality of service (QoS) requirements,
without causing harmful interference to licensed or primary
users (PUs). Each CR performs spectrum sensing to identify
the available spectrum bands and the spectrum decision pro-
cess selects from these available bands for opportunistic use.
Spectrum decision constitutes an important topic which has not
been adequately explored in CR research. Spectrum decision
involves spectrum characterization, spectrum selection and CR
reconfiguration functions. After the available spectrum has been
identified, the first step is to characterize it based not only on
the current radio environment conditions, but also on the PU
activities. The second step involves spectrum selection, whereby
the most appropriate spectrum band is selected to satisfy SUs’
QoS requirements. Finally, the CR should be able to reconfigure
its transmission parameters to allow communication on the
selected band. Key to spectrum characterization is PU activity
modelling, which is commonly based on historical data to provide
the means for predicting future traffic patterns in a given
spectrum band. This paper provides an up-to-date survey of
spectrum decision in CR networks (CRNs) and addresses issues
of spectrum characterization (including PU activity modelling),
spectrum selection and CR reconfiguration. For each of these
issues, we highlight key open research challenges. We also review
practical implementations of spectrum decision in several CR
platforms.
Index Terms—Cognitive Radio, Primary User, Reconfiguration,
Secondary User, Spectrum Characterization, Spectrum Decision,
Spectrum Selection.
I. INTRODUCTION
R
ECENT advancements in wireless technologies, such
as software defined radios (SDRs), promise to address
some of the major limitations experienced in legacy wireless
communication systems. One of these limitations is inefficient
utilization and management of the radio frequency (RF) spec-
trum in both licensed and unlicensed bands. Traditionally, RF
spectrum is managed by the regulatory agencies through the
assignment of fixed portions of spectrum to individual users
in the form of renewable licenses. Although this regulatory
Manuscript received 14 October 2011; 1st revision resubmitted March 2012;
2nd revision resubmitted 17 September 2012; manuscript accepted 24 October
2012.
M. T. Masonta is with the Council for Scientific and Industrial Research
(CSIR) and also a doctorate candidate at Tshwane University of Tech-
nology (TUT), Pretoria, Republic of South Africa (RSA), (e-mail: mma-
sonta@csir.co.za), M. Mzyece is with the French South African Institute of
Technology (F’SATI), Dept. of Electrical Engineering, TUT, Pretoria, RSA,
(e-mail: mzyecem@tut.ac.za), and N. Ntlatlapa is also with the CSIR, Pretoria,
RSA, (e-mail:nntlatlapa@csir.co.za).
The financial support of the CSIR is gratefully acknowledged.
approach ensures interference-free communications between
radio terminals, it suffers from inefficient spectrum utilization.
The available literature shows that spectrum utilization, on a
block of licensed RF band, varies from 15% to 85% at different
geographic locations at a given time [1]–[3]. As the demand
for advanced broadband wireless technologies and services
increases, traditional static spectrum regulation policies are
becoming obsolete. To keep up with growing demand, there
is a need for more efficient dynamic spectrum access (DSA)
[4] technologies and regulatory approaches.
The need for DSA or opportunistic spectrum access (OSA)
was first proposed for the United States by the Federal Com-
munications Commission (FCC) in 2003 [1]. This need was
mainly driven by the threat of lack of operating spectrum for
future wireless technologies. This move was recently followed
by another important decision by the FCC in 2008 [5] and the
Office of Communications (Ofcom) in the United Kingdom
in 2010 [6], to open up television white spaces (TVWS) for
unlicensed utilization. TVWS refers to large portions of RF
spectrum, in the very high frequency (VHF) and ultra high
frequency (UHF) bands, that will become vacant after the
switch-over from analogue to digital TV [7]. Alongside these
developments, there has been a strong trend towards research
and development of cognitive radio (CR) [8] technology to
optimally access the usable spectrum opportunistically and
dynamically. A CR is “an intelligent wireless communication
system capable of changing its transceiver parameters based
on interaction with the external environment in which it
operates” [1]. CR is therefore seen as an enabling technology
for efficient DSA.
While several approaches are proposed for achieving DSA
(such as the dynamic exclusive use model, the spectrum
commons model, and the hierarchical access model [4]), our
focus in this paper shall be on DSA using CR technology.
The process of realizing efficient spectrum utilization using
CR technology requires a dynamic spectrum management
framework (DSMF). In this paper, we shall adopt the DSMF
proposed in [2] due to its clarity and relevance to our discus-
sion. This DSMF consists of spectrum sensing, spectrum deci-
sion, spectrum sharing and spectrum mobility, as shown in Fig.
1. Spectrum sharing refers to coordinated access to the selected
channel by the secondary users (SUs) or CR users. (While the
terms “SU” and “CR user” are used interchangeably, in this
paper we shall only use the term SU). Spectrum mobility is
the ability of a CR to vacate the channel when a licensed
user is detected. Spectrum sensing involves identification of
spectrum holes and the ability to quickly detect the onset of
2
Fig. 1: Dynamic Spectrum Management Framework [2]
licensed or primary user (PU) transmissions in the spectrum
hole occupied by the SUs. Spectrum decision refers to the
ability of the SUs to select the best available spectrum band
to satisfy users’ quality of service (QoS) requirements. In this
paper we will focus on the spectrum decision component of
the DSMF.
Spectrum decision involves three main functions [2]: spec-
trum characterization, spectrum selection and CR reconfig-
uration. Once vacant spectrum bands are identified (using
spectrum sensing, geo-location databases or other techniques),
each spectrum band is characterized based on local observa-
tions and on statistical information of the primary networks
(which is normally called PU activities). The second step
involves the selection of the most appropriate spectrum band,
based on the spectrum band characterization. Thirdly, a CR
should be able to reconfigure its transceiver parameters to
support communication within the selected spectrum band.
The required functions for the spectrum decision framework
are summarised in Fig. 2. In order to perform these functions,
the following questions need to be answered:
1. How can the available spectrum be characterized?
2. How can the best spectrum band be selected to satisfy
the SU’s QoS requirements?
3. What is the optimal technique to reconfigure the CR for
the selected spectrum band? (And how?)
The above questions form the basis of spectrum char-
acterization, spectrum selection and CR reconfiguration, re-
spectively, as shown in Fig. 2. In this paper, we provide a
comprehensive, up-to-date survey of the key research work
on spectrum decision in cognitive radio networks (CRNs).
We also identify and discuss some of the key open research
challenges related to each aspect of the spectrum decision
framework. This paper surveys the literature over the period
2003 to mid-2012 on spectrum decision in CRNs. This survey
does not cover work done on spectrum sensing, spectrum
sharing, spectrum mobility or geo-location databases.
We choose to focus on spectrum decision because of its
importance in and centrality to the DSMF in CRNs and
because it has received relatively little attention compared to
other components of the CR DSMF (namely spectrum sensing,
spectrum mobility and spectrum sharing). In many ways,
spectrum decision represents the culmination of the DSMF
Fig. 2: Spectrum Decision Framework
in CRNs. We can limit our focus to this one aspect of DSMF
based on the well-known communications engineering prin-
ciples of modularity and abstraction, perhaps most famously
and powerfully exemplified in Shannon’s 1948 classic paper
[9].
The remainder of this paper is arranged as follows. Section
II provides the background of CR technology and the mo-
tivation for performing spectrum decision in CRNs. Section
III outlines CR standardization and regulation activities which
are related to spectrum decision in CRNs. Section IV discusses
spectrum characterization, the first of the three major spectrum
decision functions in CRNs. Section V focuses on spectrum
selection, the second major spectrum decision function in
CRNs. Section VI covers CR reconfiguration and reconfig-
urable parameters, the final major spectrum decision function
in CRNs. Section VII presents related work on practical
implementations of spectrum decision on CR platforms. Future
developments in CRNs are reviewed in Section VIII. Section
IX concludes the paper.
II. OVERVIEW OF CRNS
In this section we provide an overview of CR technology
and different CRN topologies. We briefly mention generic
problems affecting spectrum decision functions due to the
time-varying nature and fluctuations of the available spectrum
in CRNs.
A. Cognitive Radio Overview
Recently, CR has received considerable attention from the
research community as an enabling technology for efficient
management of RF spectrum. In order to achieve DSA, a CR
should be both spectrum and policy agile [10]. A spectrum
agile CR is capable of operating over a wide range of
frequency spectrum; while a policy agile CR will be aware of
the constraints under which it operates (such as the rules for
opportunistically using the vacant spectrum bands). Practically,
CR builds on the software defined radio (SDR) architecture
with added intelligence to learn from its operating environment
and adapt to statistical variations in the input stimuli for
efficient resource utilization [11]. With the current threat of
3
Fig. 3: Centralized CRN Topology
spectrum scarcity, CRs are widely proposed to build DSA-
based secondary networks for lower priority users.
One of the major functions of a CR is to find spectrum
holes and be able to access and utilise them without causing
any harmful interference to the incumbent or PU. A spectrum
hole is defined as a band of frequency assigned to the PU, but
which at a particular time and specific geographic location is
not being used by that PU [11]. In the absence of signalling
between PUs and SUs, spectrum holes may be identified
by performing direct spectrum sensing, using geo-location
databases, beaconing techniques, or by combining spectrum
sensing with geo-location database information [7], [12]. (For
interested readers the latest developments on database based
CRNs, known as “SenseLess CRNs”, are reported in [13].)
A CR should also be intelligent enough to perform spectrum
decision in order to select the most suitable frequency band
to satisfy specific communication needs. Spectrum decision
is a key function of CRs which requires greater attention in
order to realize the practical implementation and deployment
of CRNs. Consequently, the focus of this paper will be
on spectrum decision frameworks in both centralized and
distributed CRNs. Readers are referred to [2] and [11] for
good introductions to spectrum agile CR technology.
B. Cognitive Radio Network Topologies
A CRN is a wireless communication network whose end-
user nodes are CRs. Similar to traditional wireless networks,
a CRN topology can be classified as either centralized
(infrastructure-based) or distributed (infrastructure-less or ad
hoc) network topology. These network types are depicted in
Fig. 3 and Fig. 4, respectively. In this paper, we consider
both centralized and distributed CRN topologies.
1) Centralized CRN Topology
In the infrastructure-based CRN architecture, a central node
such as a base station (BS) or access point (AP) is deployed
with several SUs associated with it, as shown in Fig. 3.
A typical example of a centralized CRN is a IEEE 802.22
wireless regional area network (WRAN) or a cellular network.
For simplicity, we shall take the IEEE 802.22 WRAN as
an example to discuss a centralized CRN. However, similar
reasoning can be applied to more complex centralized CRNs.
In a centralized network, a BS controls all the SUs (clients) or
consumer premises equipments (CPEs) within its transmission
range. The CRN operates within the transmission or coverage
area of the primary network. Thus it uses DSA techniques to
opportunistically access the primary network spectrum without
causing any harmful interference. To do this, all SUs perform
spectrum observation on specified spectrum channels and then
send their observations to the BS, which acts as a fusion centre.
Both the BS and its associated clients may be capable of
detecting the presence of the PUs using different detection
techniques (such as spectrum sensing, geo-location databases
or beaconing).
In some cases [14], two physical channels are used: one
for observing the primary channel and the other for reporting
data by the SUs to the BS. Once available channels are
gathered, a BS will build the final list of these available
channels and their associated maximum transmission powers,
and then decide on the best channels to be accessed. These
channels will then be broadcast back to all or selected SUs
for use. In the next subsection we discuss the distributed
CRN topology.
2) Distributed CRN Topology
In the distributed CR ad hoc network (CRAHN) topology,
the SUs communicate directly with each other without any
central or controlling node. As shown in Fig. 4, SUs share
their local observations and analysis among themselves, as
long as they are within each other’s transmission range. For
database-based networks, each SU may have access to query
the database for available spectrum bands. Using both its
results and the results of other SUs, a SU can make a decision
for an appropriate band using a local criterion. If the criterion
is not satisfied, the process may be repeated again until a
decision is reached.
It is clear that spectrum decision in CRAHNs does not
rely on a central node. However, if SUs decide to cooper-
ate, as in cooperative spectrum sensing, one node can be
chosen as the head node and be used for making spectrum
decisions. Unlike in infrastructure-based topologies, spectrum
decision in CRAHNs also involves route selection, which is
normally addressed as a joint spectrum and route selection
problem. A noticeable new challenge in CRAHNs, which
did not exist in traditional wireless ad hoc networks, is that
channel availability is determined by the present behaviour
of PUs, which may vary with location, time and frequency
[15]. Another new challenge is the re-routing and switching
to other available channels or links once the PU appears on
the occupied channel [16]. Thus the wide range of operating
or available spectrum makes it infeasible to transmit beacons
over all possible channels. Section V discusses these and
many other spectrum selection challenges experienced in both
distributed and centralized CRNs.
In this section, we have provided a brief overview of
4
Fig. 4: Distributed CRN Topology
CR technology in relation to spectrum decision and the two
most commonly deployed CRN topologies (i.e. centralized
and distributed topologies). In the next section, we present
standardization and regulation efforts around CR technology.
III. STANDARDIZATION AND REGULATORY EFFORTS
The introduction of frequency agile CR technology created
a spark in numerous academic, industry, regulatory and stan-
dardization bodies worldwide. Like any other new technol-
ogy introduced to the market, CR technology’s success will
depend on sound standardization and regulation efforts from
standardization bodies, regulators and industry. It is important
to note that initial research and development efforts on CR
technology have been focused in the United States. This is
mainly due to the FCC’s adoption of CR as an enabling
technology for efficient spectrum management. Since then,
other standardization bodies and regulatory agencies around
the world have become interested in the standardization and
regulation of CRs for DSA. In the following sub-sections we
will discuss standardization and regulatory efforts on CRs,
focusing mainly on efficient frequency management.
A. Standardization
1) IEEE 802.22 Standard
The global switch-over from analogue to digital TV will
leave a considerable amount of VHF/UHF spectrum vacant.
The TVWS spectrum has excellent radio propagation char-
acteristics, and is now being proposed as the most useful
spectrum for improving wireless broadband connectivity in
rural communities [12], [17]. In order to take advantage of
the TVWS spectrum, the IEEE 802.22 WRAN standard [18]
was established, and the first official standard was released
in July 2011. IEEE 802.22 is the first wireless air interface
standard focused on the development of CR based WRAN
physical (PHY) and medium access control (MAC) layers for
operation in TVWS. It specifies a fixed point-to-multipoint
wireless air interface where a BS manages its own cell and
all associated CPEs. The IEEE 802.22 PHY layer is based on
orthogonal frequency division multiple access (OFDMA) and
can support a system which uses TVWS channels to provide
wireless communication links over distances of up to 100 km.
A typical use case for the IEEE 802.22 standard would be in
sparsely populated rural areas [7].
The IEEE 802.22 standard supports incumbent or PU
detection through spectrum sensing techniques with an
option for geo-location databases. However, there are still
technical difficulties in performing reliable spectrum sensing
practically. Thus, some regulatory bodies such as the FCC and
Ofcom prefer geo-location databases as the primary means
for incumbent detection. A beaconing option is also provided
for incumbent user detection in IEEE 802.22. In IEEE
802.22, both the CPE and BS have the capability to detect
the incumbent, but spectrum decision is only managed by the
central BS. The BS employs the CR capabilities for spectrum
decision based on the TV channels’ operating characteristics
[19]. The BS actually performs the spectrum characterization
and selection functions, while the CPE is responsible for the
reconfiguration of its transceiver parameters.
2) IEEE DySPAN
After realizing the importance of coordinated work around
CR standardization, the IEEE P1900 Standards Committee
was jointly established by the IEEE Communications Society
(ComSoc) and the IEEE Electromagnetic Compatibility So-
ciety in the first quarter of 2005 [20]. On 22 March 2007,
the IEEE Standards Association Standards Board approved
the reorganization of the IEEE P1900 activities as Standards
Coordinating Committee 41 (SCC41), called Dynamic Spec-
trum Access Networks (DySPAN). The main aim of SCC41 is
to develop supporting standards to address issues related to
new technologies and the development of techniques for next
generation radio systems and advanced spectrum management
[21]. The SCC41 concentrates on developing architectural
concepts and specifications for network management between
incompatible wireless networks rather than specific mecha-
nisms that can be added to the air interface.
In December 2010, the IEEE SCC41 was renamed the
IEEE DySPAN-Standard Committee (DySPAN-SC). The
IEEE DySPAN-SC consists of seven working groups (WGs),
named 1900.1 through to 1900.7. Out of these WGs, the
IEEE 1900.4’s work has some elements of spectrum decision.
This WG focuses on architectural building blocks enabling
network-devices decision making for optimized radio resource
usage in heterogeneous wireless access networks [20].
3) European Telecommunications Standards Institute
In Europe, the European Telecommunications Standards
Institute (ETSI) is also involved in the standardization of
CR systems (called reconfigurable radio systems) under their
Reconfigurable Radio Systems Technical Committee (RRS-
TC) [22]. Cognitive radio principles within ETSI RRS-TC are
concentrated on two topics: a cognitive pilot channel proposal
and a functional architecture for management and control of
reconfigurable radio systems. There are four WGs forming
5
the ETSI RRS-TC, WG 1 to WG 4. Cognitive management
and control falls under WG 3. This WG focuses on defining
the system functionalities for reconfigurable and dynamic
spectrum management and joint radio resource management.
More information on ETSI RRS-TC can be found in [22].
B. Regulation
The International Telecommunication Union (ITU) is also
involved in standardization efforts of CR technology through
their ITU-R Working Party (WP) 1B and WP 5A [23]. These
two WPs prepared reports describing the concepts and the
regulatory measures required to introduce CR. The ITU-R WP
1B developed a working document towards draft text on World
Radio-communications Conference 2012 (WRC-12) agenda
item 1.19. Agenda item 1.19 reads: ”to consider regulatory
measures and their relevance, in order to enable the introduc-
tion of software-defined radio and CR systems, based on the
results of ITU-R studies, in accordance with Resolution 956
of WRC 07” [24]. The ITU-R WP 5A is currently developing
the working document toward a preliminary new draft report,
Cognitive Radio Systems in the Land Mobile Service [25]. This
report will address the definition, description, and application
of CR systems in the land mobile service [23]. The regulatory
technicalities on dynamic spectrum management and spectrum
decision from the ITU’s point of view should become clearer
after the WRC-12, where they will be discussed under agenda
item 1.19.
Now that we have presented the necessary background to
assist in understanding CR technology, CRN topologies, and
CRN-related standardization and regulation activities, in the
following sections we will discuss the three major functions
in the spectrum decision framework. These will be followed
by additional sections on spectrum decision in CR platforms
and future developments in CR technology.
IV. SPECTRUM CHARACTERIZATION IN CRNS
In CRNs, multiple spectrum bands with different channel
characteristics may be found to be available over a wide
frequency range [26]. In order to properly determine the most
suitable spectrum band, it is crucial to first identify the charac-
teristics of each available spectrum band. Spectrum character-
ization allows the SUs to characterize the spectrum bands by
considering the received signal strength, interference and the
number of users currently residing in the spectrum, based on
RF observation. The SUs should also observe heterogeneous
spectrum availability which varies over time and space due
to PU activities. Heterogeneous spectrum availability refers
to the availability of spectrum holes which fluctuate over time
and location and have different characteristics. Thus, spectrum
characterization should include both the current RF environ-
ment conditions and the observed PU activity modelling. In
this section, spectrum characterization in terms of the radio
environment and PU activity models is discussed along with
some related work. The section ends with key open research
challenges in spectrum characterization.
Fig. 5: Radio frequency environment characterization elements
A. Radio Frequency Environment Characterization
A CR is expected to continuously characterize radio
environment usage in frequency, time and space. This is
mainly due to the fact that available spectrum bands in
CRNs always have different characteristics. Radio frequency
(RF) environment characterization is a process that involves
estimation of the following key elements or parameters: (1)
channel identification, (2) channel capacity, (3) spectrum
switching delay, (4) channel interference, (5) channel holding
time (CHT), (6) channel error rate, (7) subscriber location,
and (8) path loss [2], [27]. These elements are illustrated in
Fig. 5 and analysed and discussed in the following subsections.
1) Channel Identification
Primary channel identification is the first important step
to be performed by each CRN. As research in CR evolves,
different CRNs application areas are being introduced to
the market. Some of these applications include: television
(TV) white space networks, smart grid networks, machine-to-
machine (M2M) networks, public safety networks, broadband
cellular networks and wireless medical networks [28]. These
applications or networks exhibit different traffic data patterns,
either deterministic or stochastic.
In deterministic traffic, the PU is assigned a fixed time slot
on a frequency band for communication. Once the PU stops
communicating, the frequency band becomes available and can
be used by the SUs. Examples of deterministic traffic data
patterns occur in TV broadcasting (longer periods) and radar
transmitters (shorter periods). Normally deterministic signals
have fixed or predictable ON and OFF periods which can be
determined by a mathematical expression, rule or table. Any
future value for a deterministic signal can be calculated or
predicted based on its past values, which makes it easy to
predict future PU idle periods for CRNs.
On the other hand, stochastic traffic patterns can only
be described and analysed using probabilities and statistics
because their spectrum usage tends to exhibit greater variations
in time and space [11]. Due to their randomness, stochastic
signals are analysed using average values from a collection of
primary signals. Examples of stochastic traffic data patterns
occur in cellular networks. In order to improve the accuracy of
stochastic traffic modelling, Haykin [11] suggested the design
![Fig. 1: Dynamic Spectrum Management Framework [2]](/figures/fig-1-dynamic-spectrum-management-framework-2-gixfprxn.webp)
![Fig. 7: Prediction System Model [29]](/figures/fig-7-prediction-system-model-29-1pdr9gdo.webp)
