Wireless Sensor Network, 2009, 3, 123-131
doi:10.4236/wsn.2009.13018 ctober 2009 (http://www.SciRP.org/journal/wsn/).
Copyright © 2009 SciRes. WSN
Published Online O
A Cognitive Radio Receiver Supporting Wide-Band Sensing*
Volker BLASCHKE, Tobias RENK, Friedrich K. JONDRAL
Institut für Nachrichtentechnik, Universität Karlsruhe (TH), Karlsruhe, Germany
E-mail: {blaschke, renk, fj}@int.uni-karlsruhe.de
Received April 29, 2009; revised May 8, 2009; accepted May 10, 2009
The specification of IEEE 802.22 defines the world-wide first cognitive radio (CR) standard. Within a range
of 40 MHz to 910 MHz CR systems are allowed to allocate spectrum besides the currently established radio
services like radio and TV broadcasting. In order to fulfill the regulative guidelines of interference limita-
tions, a capable spectral sensing and user detection has to be provided. Due to the wide frequency range
specified in IEEE 802.22 and the high dynamic range of signals allocated in this band there are high de-
mands on the CR receiver’s front-end. Especially the performance requirements on analog-to-digital con-
verters increase significantly compared to current wireless systems. Based on measurements taken in this
frequency range requirements to CR’s ADCs are figured out. Furthermore, the measurement results are ana-
lyzed regarding expectable allocation scenarios and their impacts to spectral sensing. Derived from this re-
sults and a comparison of general spectral sensing mechanisms an approach for a CR receiver supporting
wide-band sensing is presented. Considering the apriori information resulting from scenario analysis and
including adapted information processing in the terminal the ADC’s performance requirements can be re-
Keywords: Cognitive Radio, IEEE 802.22, Spectrum Sensing, A/D Conversion
1. Introduction
The term Spectrum Sensing becomes more and more
important, especially in the context of cognitive radio
(CR). Due to the increased request for wireless transmis-
sion resources and the ongoing installation of new radio
access technologies for broadband access, enhanced re-
search in the field of mobile CR receivers is necessary.
Based on the results of spectral measurements [1,2] a
low utilization over wide frequency ranges was identi-
fied. This additionally motivates the development of in-
telligent radio resource allocation mechanisms to over-
come this waste of resources. For increasing the overall
utilization, dynamic allocation of free spectral resources
that considers both the users and the spectral environ-
ment is required. This approach is supported by the CR
concept [3,4]. Providing a dynamic resource allocation,
sufficient information about the spectral environment has
to be collected. Thereby, different acquisition methods
can be used. On one side, all information are collected by
a central control unit and distributed to simple mobile
terminals. In this case traffic load information could be
exchanged between joint networks via backbone [5].
Other approaches base on distributed sensing using all
mobile terminals of a radio access network. This re-
quires sensing capabilities in each mobile entity and
an efficient algorithm for consolidation of the results.
Especially in such scenarios swarm-intelligence algo-
rithms could offer additional benefits. Nevertheless,
an appropriate spectral sensing and information ex-
traction forms a precondition for dynamic and effi-
cient allocation mechanisms. In order to avoid unac-
ceptable interferences a reliable detection of other
users has to be supported by the CR. Therefore, both,
the temporal as well as the spectral characteristics of the
observed frequency band have to be known to the termi-
nal. All these requirements lead to high demands on the
radio’s frontend.
In this paper the performance demands on analog-to-
digital converters (ADCs) in mobile CR receivers sup-
porting spectral sensing are discussed. Based on the fre-
quency ranges specified in IEEE 802.22 [6] general as-
*This paper is an expanded version of the correspondent article ac-
cepted in the proceedings of “2008 IEEE International Conference on
Communications Workshop.”
pects of wide-band sensing as well as specific demands
on the structure of mobile CRs are presented. Analyzing
the frequency range of IEEE 802.22 several types of
channel utilization can be figured out. This a-priori
knowledge combined with suitable information process-
ing in the terminal will lead to an optimized front-end
structure supporting wide-band spectral sensing.
The paper is structured as follows: In the next section
a brief introduction to ADCs is presented. In Section 3, a
detailed description of sensing algorithms in CR termi-
nals is given. Considering the frequency bands specified
in IEEE 802.22 the expected signal characteristics are
identified. Based on this, two general wide-band sensing
methods including a comparison of their demands to
ADC’s performance are described in Section 4. In Sec-
tion 5 a CR receiver structure is presented including
adapted spectral sensing combined with a convenient
information processing. Finally, a conclusion is given.
2. Analog-to-Digital Conversion in CR
The digitization of the received signal is a basic compo-
nent in each digital receiver. Only a suitable sampling
and quantization of the analog input signal enables the
receiver to provide the communication tasks supported
by digital signal processing. Most CR concepts described
in literature assume an appropriate ADC as precondition.
But an efficient analog-to-digital conversion contains a
lot of challenges in order to support the performance
constraints assumed in these CR concepts.
Generally, the key parameters for summarizing the
ADC’s performance are stated resolution, signal to noise
ratio, spurious free dynamic range, and power dissipation
[7]. Furthermore, aperture jitter as well as two-tone in-
termodulation distortion is important for characterizing
ADCs. A detailed description and performance analyzes
can be found in [8] and [9]. In [8] the performance of
on-the-market ADCs is analyzed in order to describe the
evolution and trends in ADC’s technology. This evalua-
tion was continued in [9] including also present-day
In this article ADC parameters which restrict an im-
plementation of current ADCs in mobile CR terminals
are figured out. These are sampling frequency fs, affect-
ing the effective resolution bandwidth, effective number
of bits Neff, describing the dynamic range supported by
the ADC, and power dissemination Pdiss resulting in bat-
tery running time.
In order to fulfill the Nyquist criterion the converter’s
sampling frequency fs has to be more than two times the
effective analog bandwidth [10]. Furthermore, the effec-
tive number of bits Neff is lower than the stated number
of resolution bits specified by the vendor. Due to hard-
ware imperfections and quantization noise Neff is [8]
where D describes the effective dynamic range of the
converter in dB. Especially for detection and sensing
applications a high dynamic range is of increased impor-
tance. If the dynamic range of the expected input signals
is higher than ADC’s dynamic range, weak signals may
not be detected due to resolution limitations.
Having a look to the results depicted in [8] and [9]
three main hardware architecture concepts become po-
tential candidates for implementation on mobile CR ter-
minals. Flash converters offer sampling rates of about 1
Gsps due to a parallel comparator structure. But this re-
quires high hardware effort which causes increased
power dissemination. Therefore, this architecture is un-
attractive to mobile applications. Due to the high hard-
ware effort an effective resolution of only Neff = 6 ... 8
Bits can be supported. Using Pipelined ADCs Neff can be
increased up to 15 Bits but the sampling frequency fs is
less than 500 Msps. Due to the specific design, imple-
mentation of analog track-and-hold blocks is required.
The third group of potential candidates is -converters.
The available effective resolution is up to 20 Bit but the
available sampling rate is less than 100 Msps. Though,
due to their low power consumption these ADCs are
very interesting for an implementation in mobile CR
terminals. A detailed description of the different ADC
structures can be found in [10,11].
3. Spectrum Sensing and Related Hardware
Sensing the current channel state is one important task of
each radio system using dynamic channel allocation. The
well-known IEEE 802.11 wireless LAN systems, for
instance, use carrier sense multiple access with collision
avoidance (CSMA/CA). This multiple channel access
scheme requires sensing and detection of channel alloca-
tion. In such systems, sensing bandwidth and transmis-
sion channel bandwidth are the same.
During the last years several approaches for dynamic
spectrum allocation (DSA) [12] and channel allocation
adapted to the current spectrum situation and user re-
quirements were published [5]. These concepts combine
available transmission resources of several systems in
order to optimize the spectrum utilization of all consid-
ered systems. This would require the availability of the
current system state to all other systems. Assuming a
coupling of combined systems and a general control en-
tity, the information exchange can be realized using traf-
fic control channels in the wired backbone network. So,
each system can handle and optimize the channel alloca-
tion of its subscribers considering this additional traffic
Copyright © 2009 SciRes. WSN
load information. Inter-system handover need to be initi-
ated and controlled by the general control entity. Basi-
cally, this architecture requires cooperation between all
radio access networks participating in DSA, which again
results in a relatively static system configuration.
Another approach for increasing the spectral utiliza-
tion is overlay systems [13]. Based on the fact that wide
frequency ranges offer a lot of unallocated transmission
capacity, this approach describes the allocation of local
networks exploiting temporarily and/or locally unoccu-
pied transmission channels. Due to the basic precondi-
tion that licensed systems should not be changed for
overlay usage, the rental user must observe the commu-
nication channel in order to provide a reliable detection
of the licensed user’s channel allocation.
Furthermore, the rental user’s signal must be adapted
to the licensed system’s transmission parameters regard-
ing channel bandwidth, maximum channel allocation
duration, transmission power, etc. Static system parame-
ters, e.g., channel bandwidth, can be defined in a data-
base available for each rental user. But for dynamic pa-
rameters, e.g., current channel allocation, licensed user’s
allocation statistics have to be observed and analyzed at
present. So, a continuous spectral observation has to be
done by the overlay system. In order to get information
about the allocation of frequency bands, energy detection
can be used. Comparing the received signal power with
the noise level general channel allocation information
can be collected. If the signal power is higher than the
measured noise level the channel is already occupied.
Especially, in case of weak signals that are close to noise
level the power detection can fail. So, analyzing signal’s
higher order statistics or other feature detectors may
overcome this drawback [14].
Having a look to the IEEE 802.22 specification, the
communication channel bandwidth is Bch = 6 … 8 MHz
and the frequency range specified for allocation is be-
tween 41 MHz and 910 MHz with respect to national
regulations [6]. So, the overall system bandwidth, that
has to be observed, is BS = 869 MHz. This is more the
100 times the signal’s bandwidth Bch. In order to provide
a flexible CR system which is able to optimize spectral
utilization, the full frequency range has to be supported
by mobile terminals. This also includes the ability for a
fast and efficient sensing of wide ranges in order to adapt
transmission parameters to the licensed user’s allocation.
Supporting high signal bandwidth directly affects the
hardware architecture of a terminal. Especially the ana-
log signal processing and the ADC limit the supported
bandwidth. As it is described in Section 2, there is a
trade-off between the signal’s bandwidth and the dy-
namic range of the converter.
Having a look to the receiving signals within this fre-
quency range, several allocation characteristics can be
pointed out. In Figure 1 the received signal power per
frequency averaged over a sensing period of 3 h is de-
picted. The frequency range of f = 41…910 MHz repre-
sents the overall range specified for IEEE 802.22. As it
can easily be seen, there are wide frequency ranges
where a very low averaged signal energy is detected. But
also high utilized bands can be pointed out. Between 88
MHz and 108 MHz the European FM radio broadcast
service is allocated (cf. ch 1). Furthermore, some TV
broadcast signals as well as temporarily allocated chan-
nels can be noticed. For describing the channel utiliza-
tion during sensing time a binary spectrogram can be
, (2)
1for {()}|
{()}| 0for {()}|
Oxt SxtP
where x(t) is the received signal, S{x(t)}|f=fm describes the
spectrogram of x(t) at frequency fm, and Pth describes the
detection threshold. The resulting binary description of
the spectral allocation can be averaged over sensing time
using a window length Nw. So, the averaged channel
utilization can be written as
nm nm
tff tff
, (3)
where tn is the time index. The resulting characteristics
for three different frequencies are depicted in Figure 2.
The curve of channel 1 describes a typical broadcast
channel utilization. Channel 2 offers a varying averaged
occupation between 0.01 and 0.92. In channel 3 an aver-
aged utilization of 0 can be noticed. During this period
the channel is not used by the licensed user and would be
interesting for CR resource allocation. In order to detect
Figure 1. Averaged power vs. Frequency in IEEE 802.22
frequency range.
opyright © 2009 SciRes. WSN
Copyright © 2009 SciRes. WSN
scribed in the following subsections. Their ability for
implementation in a mobile CR receiver will also be
4.1. Basics on Spectral Analysis
Due to the fact that currently available ADCs possess a
limited bandwidth, it is not possible to digitize large fre-
quency spans at once. Therefore, so-called fast Fourier
transform (FFT) analyzers are only usable for low fre-
quency signals. In order to investigate high frequency
signals, superheterodyne receivers must be applied. Here,
the overall input frequency span is mixed to a common
intermediate frequency (IF) by a tunable local oscillator
[15]. Spectral resolution is directly determined by the IF
filter. The smaller the resolution bandwidth BR, the hig-
her the spectral resolution. A well-known problem oc-
curs if the input frequency range is more than two times
bigger than the IF, because then suppression of the image
frequency is not possible without affecting the input sig-
nal. Hence, a tunable bandpass is necessary. This prob-
lem can be overcome if several IF stages are used, where
the first one transforms the input signal to a higher fre-
quency. It is then possible to suppress the image fre-
quency without affecting the input signal.
Figure 2. Averaged channel utilization of channel 1, 2 and 3.
such periods that are also called white spaces [4] a suit-
able observation of wide frequency ranges has to be pro-
vided. As it can be noticed in Figure 1, high differences
in the dynamic range of the depicted signals has to be
considered. During measurements a maximum signal
power of –41.8 dBm at channel 1 was observed. The
general noise level is –92 dBm measured in unallocated
sub-bands. So, the overall dynamic range compared to
the bandwidth of more than 850 MHz marks the main
challenge in finding suitable solutions for mobile CR
receivers supporting this wide frequency range. In the
next section two general sensing methods supporting
these requirements will be described.
Figure 3 shows a superheterodyne receiver with two
IF chains for the IEEE 802.22 specification. The RF in-
put signal first passes RF attenuation that helps to pre-
vent overload and distortion. Afterwards, a preselector
lowpass filters out higher frequency signals. In order to
mix the RF signal up to 1000 MHz (fIF 1), the first local
oscillator (LO 1) must operate in a frequency range from
1041 MHz to 1910 MHz. This leads to an image fre-
quency that ranges from 2041 MHz to 2910 MHz which
can easily be suppressed by the following IF 1 filter.
After this, a second local oscillator (LO 2) with a fre-
quency of 970 MHz mixes the 1000 MHz signal down to
fIF 2 = 30 MHz. Image frequency is 940 MHz which
4. Spectrum Sensing Methods
Several methods can be used for analyzing wide ranges
of radio spectrum. The two general concepts, sweeping a
small detection window over the observed frequency
band and wide band analog to digital conversion fol-
lowed by energy or feature detection are briefly de-
Figure 3. Superheterodyne receiver with two IF chains and low frequency ADC fort he IEEE 802.22 specification.
again can easily be filtered out by the IF 2 filter. Both
LOs are controlled by PLLs that are connected to a ref-
erence oscillator to increase frequency accuracy. The
automatic gain control (AGC) block is followed by a
tunable bandpass filter that determines the resolution
bandwidth. The ideal case for the resolution filter is a
rectangular filter with bandwidth BR. In order to achieve
short measurement times, however, optimized Gaussian
filters are used that are temperature stable and possess a
higher bandwidth accuracy as well. Nevertheless, BR
influences the sweep time Tsw that is necessary to scan
the whole frequency range BS. If measurement time falls
below Tsw, amplitude losses and signal distortions occur
that eventually lead to frequency offsets. Subsequent to
filtering is the envelope detector and ADC. The video
filter is a lowpass that suppresses noise and helps to
smooth the signal spectrum. The video bandwidth BV
acts inversely proportional to the sweep time Tsw. The
micro-processing block (µP) includes, e.g., averaging
and threshold decision making.
For the definition of the required sweep time, two
cases must be taken into consideration, one where the
video bandwidth is higher than the resolution bandwidth
and vice versa [15]:
opyright © 2009 SciRes. WSN
, (4)
where the parameter k denotes a proportional factor that
is usually in the range of 1 to 3. The first case of Equa-
tion (4) is illustrated in Figure 4. System parameters
were chosen according to the IEEE 802.22 specification.
The vertical dashed lines define the channel bandwidth
of Bch = 6 … 8 MHz [16]. It can easily be seen that for one
Figure 4. Sweep time Tsw in relation to resolution band-
width BR with parameter k=1,2,3.
single scan through the whole frequency range of 869
MHz approximately 3 10-4 s are necessary if we consider
a resolution bandwidth of BR = 2.5 MHz. (This means
that we take two to four frequency bins per channel.)
As it can be seen in Figure 3, the ADC is located after
the resolution filter and the envelope detector. This
means that only a bandwidth of BR has to be digitized
which leads to a higher amplitude resolution. Moreover,
a quite simple ADC is sufficient in that case. Drawbacks
are, however, that pretty much analog hardware is nec-
essary and that the measurement time is increased (cf.
Section 5). Additionally, no feature detection is possible
since only energy detection is performed.
4.2. Wide-Band Sensing
Another possibility for energy detection is the direct
conversion of the wide-band input signal. This reduces
the number of intermediate frequency stages required for
sweeping the detection window but significantly in-
creases the performance requirements at the ADC.
Ideally, the incoming analog signal is bandpass fil-
tered by a bandpass with the system bandwidth BS. Af-
terwards, the signal is amplified and down converted
from the radio frequency fRF to an intermediate fre-
quency fIF. Subsequent to a second filtering and AGC,
the analog to digital conversion is done. The following
data processing includes an FFT in order to extract the
current power allocation over the frequency.
After digitization the signal contains information of
the complete observed frequency range. Of course, the
information depth is characterized by the resolution per-
formance of the ADC, which is a main drawback of this
approach. As it was shown in the section before, the ob-
served signal is characterized by a high variation of the
spectral power density. The overall dynamic range is
more than 50 dB. Furthermore, under-utilized small-band
signals with weak signal amplitudes can be noticed. In
order to provide a reliable detection of the licensed user,
these signals still have to be noticeable after the ADC,
otherwise the signal detection fails. Therefore, the dy-
namic range of the ADC is an important parameter for
CR terminals.
Based on Equation (1) the presented measurements
would require a minimum resolution of Neff = 9 Bits.
Besides a high bit resolution, also a high sampling rate is
required for a wide band digitization. Following the ex-
ample of IEEE 802.22, the sampling rate is fsamp =
1.82 Gsps meeting the Nyquist criterion. Due to the
overall frequency range from 41 MHz up to 910 MHz, a
bandpass sub-sampling cannot be used for reduction of
As stated in [9], the maximum sampling rate for an
effective resolution of Neff = 9 Bits is about fsamp = 500
Msps … 1 Gsps. The analysis in [8] and [9] also figure
out those sampling rates significantly higher than 100
Msps can only be handled by Flash or Pipelined con-
verters. As described in Section 2, these two architec-
tures are characterized by a high power consumption and,
therefore, not preferable for an implementation in mobile
terminals. The group of -converters offer lower power
consumption but cannot provide the high sampling rate
available for Flash converter. In order to suppress the
undesired signals also analog notch filter can be applied.
This requires a first scan for in identification of the
strongest signals. After tuning the notch filter to these
frequencies a second scan is used for detection of weak
signals. Besides doubling the scan time also the analog
hardware effort increases significantly.
Compared to the energy detection described above, a
wide band digitization offers a better time resolution in a
wide frequency band. Thus, a detailed extraction of
temporal features is possible. A high time resolution be-
comes important for detection of very short channel al-
locations or for detailed analyzing of the licensed user’s
allocation statistics.
Both concepts offer different advantages which are
required for a successful operation of flexible overlay
systems. On the other side, each concept has significant
drawbacks that can not be solved within the next couple
of years and preclude an implementation in mobile ter-
minals. So, a combination of both could give the oppor-
tunity to combine the advantages. This approach is dis-
cussed in Section 5.
5. Wide-Band CR Receiver for IEEE 802.22
5.1. Sub-Band Spectrum Sensing
As we presented above, a digitization of the complete
system bandwidth BS is not useful regarding to technical
and economical constraints. On the other side, the exten-
sion of spectral sensing described for CRs will support
an increased spectral utilization. In order to simplify the
spectrum sensing and to reduce the hardware require-
ments described in Subsection 4.1., we will analyze the
measurement results with respect to signal characteristics
expectable in the IEEE 802.22 frequency range. Having
a closer look to the results depicted in Figure 1 and Fig-
ure 2, it can be seen that channel utilization in the
sub-band allocated by broadcast services (cf. ch 1) does
not change during the measurement time. Furthermore,
the received signal power of broadcast transmitters av-
eraged over the sensing time is significantly higher than
the noise level. In general, the average power level of the
broadcast signals is higher than –70 dBm and even
higher than –50 dBm considering the strongest signal.
Therefore, a continuous sensing of these frequency
ranges does not offer any additional information for an
operating CR system. This leads to the opportunity to
exclude such quasi-static frequency ranges considering
its information entropy during the spectrum sensing
phase. Furthermore, a decreased dynamic range of the
input signal reduces the ADC’s hardware recommenda-
tions. Without loss of generality statistical independence
of observed communication channels can be assumed. So,
the spectrum that is observed for detection of averaged
channel allocation can be divided into several sub-bands.
These sub-bands do not need to be observed at the same
time but can be sensed sequentially as long as the sens-
ing is repeated periodically and the sensing interval as
well as the sensing period is suitable to the licensed
user’s signal. Based on this knowledge and assuming a
suitable sensing sequence the full system band can be
split into M sub-bands:
Each sub-band is separately digitized reducing the
sampling rate of the ADC. Due to decoupling highly
utilized communication channels and frequency bands
with low spectral utilization, the dynamic range of the
ADC’s input signal can be optimized as well, which re-
sults in an enhanced sensing of weak signals. Addition-
ally, the suppression of strong signals using analog notch
filter is not necessary, because the reduced sampling rate
enables higher bit resolution.
5.2. Receiver Structure
The system architecture described in Subsection 4.1 de-
fines the bases for our wide-band CR supporting sub-
band sensing. In contrast to the structure depicted in Fig-
ure 3 the ADC is placed directly after the IF 2 filter. This
position is marked with the letter ‘A’. The adapted signal
processing of the wide-band CR receiver is depicted in
Figure 5. Until the marker ‘A’ the analog signal proc-
essing is the same as described in Subsection 4.1. In or-
der to support the IEEE 802.22 specifications the
sub-band bandwidth is defined to Bsub = 50 MHz includ-
ing six sub-channels at a bandwidth of 8 MHz up to eight
sub-channels at a bandwidth of 6 MHz, respectively.
Similar to the number of sweep points defined in ana-
lyzer detection the LO 1 can be tuned to 20 predefined
frequency steps resulting in a small sub-band overlap.
For a more flexible sub-band configuration, a continuous
oscillator tuning could also be implemented. Generally,
digitization of a 50 MHz sub-band requires a sampling
rate of about 100 Msps. In the following digital sensing
processing energy detection as well as feature detection
or other signal detection algorithms could be applied. In
Figure 5 the block structure for energy detection is de-
picted. Due to the digital processing the single scan time
Copyright © 2009 SciRes. WSN
can be reduced by factor 1/20 compared to the analog
processing using BR = 2.5 MHz.
Besides the reduction of scan time the proposed
sub-band digitization offers the possibility of sensing
adjacent channels of the currently allocated communica-
tion channel. As it is depicted in Figure 5 the received
signal is used for communications and sensing process-
ing in parallel. This means that all spectral information
within the actual sub-band can be collected along with
current data transmission. Furthermore, the additional
time released by the parallel sensing and communication
processing can be used for additional sensing of other
sub-bands. The additionally obtained information in-
creases the CR’s knowledge about its spectral environ-
ment. But besides an efficient sensing algorithm, also a
suitable information processing and knowledge storage
has to be applied in mobile CR terminals. In the next
subsection an algorithm is presented what bases on the
proposed sub-band sensing.
5.3. Information Processing
Another question in the context of CRs deals with proc-
essing the data gained from spectrum sensing. As it was
described by Mitola [3], one important enhancement of
CRs compared to SDRs is the implementation of rea-
soning algorithms. Reasoning can be applied for user
centric applications like user interface adaptation or pro-
viding user-specific local information and news services.
Moreover, processing of experiences can also be used for
optimizing the spectral sensing procedure. As long as a
predefined performance can be fulfilled, it is not neces-
sary to change the terminal’s configuration or the allo-
cated communication channel. Hence, sensing the full
spectral range is not necessary for most of the time. In
case that the currently occupied sub-band cannot longer
be allocated by rental users the information of the next
most suitable sub-band is required. Due to the possibility
of a direct sub-band scanning described above the scan-
ning procedure can be limited to such sub-bands which
offered low channel utilization in the past. In order to get
Figure 5. Structure of wide-band CR receiver supporting
sub-band sensing.
a first overview of a sub-band, the averaged utilization
(cf. (3)) of a communication channel or a complete
sub-band could be considered. Due to the digitization of
a full sub-band Bsub, all communication channels in this
band can be observed simultaneously. As long as the
current sub-band can offer some free radio resources to
the CR overlay system, other sub-bands need not be ob-
served continuously. A periodical short scan provides
information to approximately trace the sub-band utiliza-
tion. Based on this sensing result, the sub-band can be
ordered with respect to the current utilization. In case of
shifting overlay users to another sub-band, the sub-band
offering the lowest utilization can be observed in detail.
Since spectrum sensing is only one task of a CR ter-
minal, besides radio communication, an intelligent
scheduling of the sensing periods is necessary. In case of
a feasible number of CR nodes at one location, the qual-
ity of the detection result will not increase significantly
compared to the number of additional nodes. Thus, dis-
tributed sensing of different sub-bands that is provided
by several nodes at the same time will help gain more
information of the overall frequency range. In order to
use this advantage, the challenge of collecting the infor-
mation from all distributed nodes need to be solved.
In [13] an innovative approach for distributed sensing
that provides a solution to overcome the hidden-node
problem was proposed. In the described system all nodes
sense the same sub-band synchronously. After the sens-
ing, all binary detection results are collected at one mas-
ter station of the local network. The detection results of
each single channel within the observed sub-band are
coded in a one bit decision. These bits are sent simulta-
neously by all nodes that are connected to the master
station. For a detailed description of the algorithm the
reader may be referred to [13]. Basically, the simultane-
ous transmission leads to a superposition of all detection
results, which can be interpreted as a logical OR opera-
tion. Thus, a reliable detection of an increased area can
be provided.
Adopting this approach to the problem of increasing
the observed spectral range, we can use the following
strategy: Besides the signaling of the detection results
within the currently used sub-band another sensing and
signaling period can be added. During this period an ad-
ditional sensing and signaling of adjacent sub-bands may
be executed. Following the signaling method described
in [13] the sensing results are superposed. Thus, the cal-
culated sub-band utilization is a rough estimation of the
available resources. In case of a low utilization a detailed
sensing will follow. The measurement process support-
ing such a distributed sensing is depicted in Figure 6.
The data transmission including sensing and signaling
regarding [13] in the currently used sub-band is named
S1. During this phase dedicated nodes observe also ad-
jacent sub-bands. These sensing results are sent to the
opyright © 2009 SciRes. WSN
Copyright © 2009 SciRes. WSN
Figure 6. Sensing process for wide-band CRs supporting sub-band processing.
central control station during the phase S2. Furthermore,
a full range scan could be initiated, which is named S3.
Since the full scan needs, however, more time compared
to the normal periodic sub-band scan, the communication
in the CR system may be affected by the full scan. Due
to the ranking of the sub-band utilization, only the most
suitable sub-bands need to be considered. A full proc-
essing of allocation information is only required for the
current sub-band. All other sub-bands are characterized
by an averaged utilization index that reduces the memory
and processing effort in the mobile terminal. For a de-
tailed investigation of the proposed distributed sensing
method, e.g., bio-inspired algorithms could be taken into
on the sensing algorithms described in Section 4, a re-
ceiver for wide-band mobile CRs based on the superhet-
erodyne principle is presented in Section 5. Due to the
proposed distribution of the wide-band into 20 sub-bands
high-utilized sub-bands can be masked while sensing of
under-utilized sub-bands benefits from the increased
resolution of the ADC resulting from the decreased input
signal bandwidth. It combines this sub-band sensing
method with the presented information processing results
in a capable mobile receiver structure for IEEE 802.22
CR networks.
7. References
6. Conclusions [1] FCC, “Spectrum policy task force report, ET Docket No.
02-155,” Technical Report Series, November 2002.
In order to increase spectral utilization future CR receiv-
ers have to provide spectrum sensing capabilities. Ap-
plying DSA mechanisms require suitable spectrum sens-
ing capabilities in order to adapt the radio transmission
to the identified spectral environment. In the CR stan-
dard IEEE 802.22 a frequency range from 41 MHz to
910 MHz is specified. Within this spectral band CR
networks can be established under the limitation that the
already established radio services are not interfered un-
acceptably. In order to provide a reliable signal sensing
and detection in the CR terminals several preconditions
to the receiver’s front-end have to be fulfilled. In this
paper demands on ADCs in such wide-band scenarios
are discussed in detail. As presented in Section 2, the
general performance of ADCs is characterized by the
trade-off between supported bandwidth and dynamic
range defined by the effective number of bits. Today’s
ADC support sampling rates up to several Gsps at the
expense of low dynamic range and high power consump-
tion. But the demand for a high sensing quality in mobile
receivers leads to the contrary request for high dynamic
ranges at a low power consumption. Thus, the input
bandwidth has to be reduced. In Section 3 the different
radio services allocated in the considered frequency
range are analyzed. It is shown, that the utilization varies
significantly over the frequency. Thus, different demand
for sensing in the sub-bands can be observed. Especially
sub-bands allocated by broadcast radio services do not
offer additional radio transmission resources but increase
the demands on ADC’s performance significantly. Based
[2] Shared Spectrum Company, “Comprehensive spectrum
occupancy measurements over six different locations,”
August 2005,
[3] J. Mitola, “Cognitive radio-an integrated agent architec-
ture for software defined radio,” Ph.D. dissertation, Royal
Institute of Technology (KTH), Kista, Sweden, 2000.
[4] S. Haykin, “Cognitive radio: Brain-empowered wireless
communications,” Selected Areas in Communications,
IEEE Journal on, Vol. 23, No. 2, pp. 201–220, February
[5] P. Cordier et al., “E2R cognitive pilot channel concept,”
IST Mobile Summit, Mykonos, Greece, June 2006.
[6] IEEE, “P802.22: Cognitive radio, wide regional area
network,” Technical Specifications, May 2005.
[7] R. H. Walden, “Analog-to-digital converter technology
comparison,” IEEE GaAs IC Symposium Technical Di-
gest, pp. 217–219, October 1994.
[8] R. H. Walden, “Analog-to-digital converter survey and
analysis,” Selected Areas in Communications, IEEE
Journal on, Vol. 17, No. 4, pp. 539–550, April 1999.
[9] B. Le, T. W. Rondeau, J. H. Reed, and C. W. Bostian,
“Analog-to-digital converters,” Signal Processing Maga-
zine, IEEE, Vol. 22, No. 6, pp. 69–77, November 2005.
[10] R. Plassche, CMOS Integrated Analog-to-digital and
Digital-to-analog Converters, 2nd Edition, Kluwer Aca-
demic Publishers, Boston/ Dordrecht/London, 2003.
[11] B. Brannon, Software Defined Radio-Enabling Technol-
ogy, John Wiley and Sons, London, W. Tuttlebee, Ed., ch.
Data Conversion in Software Defined Radios, pp. 99–126.
[12] P. Leaves et al., “A summary of dynamic spectrum allo-
cation results from drive,” in IST Mobile and Wireless
Telecommunications Summit, pp. 245–250, June 2002.
[13] T. A. Weiss and F. Jondral, “Spectrum pooling: An in-
novative strategy for the enhancement of spectrum effi-
ciency,” Communications Magazine, IEEE, Vol. 42, No.
3, pp. 8–14, March 2004.
[14] D. Cabric et al., “Implementation issues in spectrum
sensing for cognitive radios,” in Signals, Systems and
Computers, 2004, Conference Record of the Thirty-
Eighth Asilomar Conference on, Vol. 1, pp. 772–776,
November 2004.
[15] C. Rauscher, Grundlagen der Spektrumanalyse, Rohde &
Schwarz, 2004.
[16] C. Cordeiro et al., “IEEE 802.22: An introduction to the
first wireless standard based on cognitive radios,” Journal
of Communications, Vol. 1, No. 1, April 2006.
opyright © 2009 SciRes. WSN