Overview of Cognitive Radio Basics and
Spectrum Sensing
CN-S2013
Jan.29, 2013
Suzan Bayhan
Faculty of Science
Department of Computer Science
www.cs.helsinki.fi
1
Summary of Today’s Class
§  Cognitive radio: What, why, and how
§  Spectrum Sensing: Basics and challenges
Faculty of Science
Department of Computer Science
CN-S2013
2
Cognitive Radio: Definition and
History
u Joseph Mitola III and Gerald Q. Maguire, Jr. (KTH, Sweden), Aug.
1999 IEEE Personal Communications, Cognitive Radio: Making
Software Radios More Personal
u Simon Haykin, Feb. 2005, IEEE Journal on Selected Areas in
Communications, Cognitive Radio: Brain-Empowered Wireless
Communications
“an intelligent wireless communication system that is aware of its
environment and uses the methodology of understanding-bybuilding to learn from the environment and adapt to statistical
variations in the input stimuli, with two primary objectives in mind: (1)
highly reliable communication whenever and wherever needed; (2)
efficient utilization of the radio spectrum”
Faculty of Science
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2011 Year in Review and Outlook for 2012
Mobile Data Traffic More Than Doubled in 2011
Wireless data consumption
increases (from Cisco’s report)
Global mobile data traffic more than doubled (2.3-­fold growth, or 133 percent increase) in 2011, for the fourth
year in a row. It is a testament to the momentum of the mobile industry that this growth persisted despite
global economic uncertainties, the broad implementation of tiered mobile data packages, and an increase in
the amount of mobile traffic offloaded to the fixed network.
Mobile Data Traffic Will Double Again in 2012
Cisco estimates that traffic in 2012 will grow 2.1-­fold (110 percent), reflecting a continuation in the tapering of
growth rates. The evolving device mix and the migration of traffic from the fixed network to the mobile network
have the potential to bring the growth rate higher, while tiered pricing and traffic offload may reduce this
effect. The current growth rates of mobile data traffic resemble those of the fixed network from 1997 through
2001, when the average yearly growth was 150 percent (Table 1). In the case of the fixed network, the growth
rate remained in the range of 150 percent for 5 years.
By 2012, the number of mobile-connected devices will exceed the world's
population.
Table 1. Global Mobile Data Growth Today is Similar to Global Internet Growth in the Late 1990s
Global Internet Traffic Growth (Fixed)
Global Mobile Data Traffic Growth
1997
178%
2009
140%
1998
124%
2010
159%
1999
128%
2011
133%
2000
195%
2012 (estimate)
110%
2001
133%
2013 (estimate)
90%
2002
103%
2014 (estimate)
78%
Source: Cisco VNI Mobile, 2012
Report: In the long term, mobile data and fixed traffic should settle into the same growth rate, although the mobile
http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/
data growth rate is likely to remain higher than the fixed growth rate over the next decade.
Cisco
white_paper_c11-520862.html
CN-S2013
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How is the wireless spectrum is
managed?
u Radio spectrum: 3kHz to
300 GHz
u The use of radio spectrum
for communication dates back
to
u 1895: Guglielmo Marconi,
radio signal transmission
using telegraph codes over
1,25 mile distance
Image from http://kids.britannica.com/elementary/
art-87886/Guglielmo-Marconi-is-pictured-with-histelegraph-equipment
u Static Spectrum Access
Faculty of Science
Department of Computer Science
CN-S2013
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Use of Radio Frequencies in
Finland (www.ficora.fi)
Use of radio frequencies
Electromagnetic spectrum [Hz]
1023
1022
1021
1020
EHF
1019
Not allocated
18
10
30 GHz
100 GHz
200 GHz
300 GHz
1017
SHF
1016
RLAN
WLAN
FWA
1015
FWA
FWA
14
10
3 GHz
10 GHz
20 GHz
30 GHz
1012
UHF
1012
Virve
PMR
TV and Digital TV
GSM
900
11
10
1010
9
10
108
7
10
106
105
R
a
d
i
o
s
p
e
c
t
r
u
m
104
300 MHz
GSM1800 DECT UMTS
Sat.
nav.
GPS
RLAN
WLAN
BlueTooth
UMTS
Wind profiler
radars
1 GHz
IMT-2000/UMTS
expansion band
2 GHz
3 GHz
VHF
TV
PMR
PMR
FM-radio
TV
Terrestrial digital audio broadcasting
RHA68
30 MHz
100 MHz
VLF
200 MHz
LF
300 MHz
MF
HF
Not
allocated
LA PR-27 CB
103
3 kHz
30 kHz
300 kHz
3 MHz
30 MHz
102
101
FICORA, 16.2.2005
Mobile
Fixed-satellite
Radionavigation-satellite
Maritime mobile
Mobile-satellite
Maritime radionavigation
Aeronautical mobile
Broadcasting-satellite
Aeronautical radionavigation
Land mobile
Meteorological-satellite
Radionavigation
Broadcasting
Earth exploration-satellite
Radiolocation
Amateur
Space operation
Space research
Radio astronomy
Inter-satellite
Fixed
Faculty of Science
Department of Computer Science
VLF
LF
MF
HF
(Very Low Frequency)
(Low Frequency)
(Medium Frequency)
(High Frequency)
VHF (Very High Frequency)
UHF (Ultra High Frequency)
SHF (Super High Frequency)
EHF (Extremely High Frequency)
Note: The division of frequencies between services and the usage
indicated in the picture only gives an overview of the frequency
utilisation. More detailed information can be obtained from
FICORA’s Regulation 4 and the annexed Frequency Allocation
Table (links from this picture).
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Shortcomings of current spectrum
management
u License for a large region, usually country-wide
u Large chunk of licensed spectrum (expensive licenses)
u Barriers to new ideas
u Prohibited spectrum access by unlicensed users
u ISM bands are unlicensed à WLAN bands at 2.4 GHz, 5 GHz
u Temporary short range licenses
Faculty of Science
Department of Computer Science
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Radio Spectrum Use in Finland
u The Finnish Communications Regulatory Authority (FICORA)
u International Telecommunication Union (ITU)
u European Telecommunications Standards Institute (ETSI)
Faculty of Science
Department of Computer Science
CN-S2013
8
Ficora allocates spectrum in
Finland
How much is this frequency? Calculate the fee for frequency!
http://www.ficora.fi/en/index/luvat/taajuusmaksut/laskentakaavatjakertoimet.html
You can check from this document:
http://www.ficora.fi/attachments/englantiav/673vb43bJ/TJTen_20042012.pdf
You can find radio spectrum regulations in Finland here:
http://www.ficora.fi/en/index/palvelut/palvelutaiheittain/radiotaajuudet.html
Faculty of Science
Department of Computer Science
CN-S2013
9
Spectrum Measurements
Image from http://www.cmpe.boun.edu.tr/WiCo/doku.php?
id=research#cognitive_radio
Image from RWTH http://www.inets.rwthaachen.de/static-spectrum.html
Faculty of Science
Department of Computer Science
u Measurement campaigns have
shown that there is plenty of
unused spectrum!
u Working time vs. night time
usage
u City-center to suburb usage
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Cognitive Radio (CR)
u There is a huge demand for spectrum, but there is unused
spectrum à Radio spectrum is inefficiently used.
§ Change in ownership; a resource is owned by the one who
uses it. Sharing for sustainability.
§ Static spectrum management since 1900s.
§ Imagine a world with no-lane-changing.
§ Smarter schemes: Dynamic spectrum access (DSA)
Faculty of Science
Department of Computer Science
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Cognitive Radio in Brief
Basic Definitions
Primary User, Secondary User
Power
Primary
User (PU),
q  Licensed,
primary,
Licensed
User,higher-priority
incumbent,
Incumbent
user: PU User
Frequency
Spectrum opportunity,
white space, hole, gap
q Secondary, cognitive,
Secondary
User
(SU),SU, CR
unlicensed
user:
Cognitive Radio (CR)
Time
PU transmission
What: A Cognitive
q Spectrum
hole,
white
Radio
(CR): smart
radio,
space,
white spectrum,
DSA
capability,
idle frequency/channel/
environment-aware,
band
self-aware,
adaptive
CR
Suzan Bayhan (HIIT)
Energy-Efficient Scheduling for Cellular CRNs
Faculty of Science
Department of Computer Science
October 2012
CN-S2013
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12
Software Defined Radio (SDR)
u Hardware: Static, once designed at the factory, never
changed
u SDR: Reconfigurable radio (e.g. operation frequency,
modulation type)
u Multiple standards
u Multiple bands
SDR is the building block of the CR.
Faculty of Science
Department of Computer Science
CN-S2013
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How does cognitive radio work?
u  Cognitive Cycle
Cognitive Radio in Brief
Cognitive Cycle
SPECTRUM
SENSING
Radio Environment
Transmission power
Transmission duration
Transmission bandwidth
Modulation and coding
Antenna orientation
RF input
Signal analysis scheme
RF front-end capabilities
MAC
Operation mode (sense,
sleep, idle or transmit)
Type of sensing (proactive or
reactive)
Period of sensing
Sensing duration
Scheduling of the sensing
intervals
Sensing architecture
Relability of sensing
(Probability of detection,
Probability of false alarm)
Transmission
Channel quality
Interference generated
PU detection
Spectrum
Handover
Spectrum
Sharing
Spectrum
Sensing
Spectrum hole
discovery
Spectrum
Decision
CR:
Image from http://pgcoaching.nl
PHY
a wireless device that can switch from one frequency to another.
Faculty of Science
Department of Computer Science
CN-S2013
14
Spectrum Sensing Reading
Material
Reading Material:
- T. Yucek and H. Arslan A
survey of spectrum sensing algorithms for cognitive radio
applications, IEEE Communications Surveys and Tutorials, vol. 11,
no. 1, pp. 116-130, 2009.
- Ghasemi, Amir, and Elvino S. Sousa.
Spectrum sensing in cognitive radio networks: requirements,
challenges and design trade-offs. IEEE Communications Magazine,
46.4 (2008): 32-39.
Faculty of Science
Department of Computer Science
CN-S2013
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What is spectrum sensing?
Time
1- Sense:
2- Sense:
3-IDLE
Sense: PU
There is PU
Faculty of Science
Department of Computer Science
PU collision:
Interference or
harmful interference
CN-S2013
Time
16
Spectrum Sensing
1- Sense for vacating the band if PU arrives. CR must not harm
PUs
2- Sense for finding unused spectrum
How to measure quality of sensing?
• Probability of detection (Pd) à Higher is better
• Probability of false alarm (Pf) à Lower is better
Faculty of Science
Department of Computer Science
CN-S2013
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Various aspects of spectrum
sensing
YÜCEK and ARSLAN: A SURVEY OF SPECTRUM SENSING ALGORITHMS FOR COGNITIVE RADIO APPLICATIONS
Hardware Requirements
117
Reactive/Proactive sensing
Hidden Primary User Problem
Spread Spectrum Users
IEEE 802.11k
Challenges
Standards that employ sensing
Decision Fusion
Bluetooth
Security
Internal (Collacotaed) Sensing
Sensing Frequency and Duration
Spectrum Sensing
Matched Filtering
Approaches
Energy Detector
Spectral Correlation (Cyclostationarity)
IEEE 802.22
External Sensing
Beacon
Geo-location + Database
Enabling Algorithms
Local (Device-centric)
Radio Identification Bas ed Sensing
Waveform Bas ed Sensing
Centralized
Cooperative Sensing
Cooperative
Multi-Dimens ional Spectrum Sensing
Dis tributed
External Sensing
Fig. 1. Various aspects of spectrum sensing for cognitive radio.
Faculty of Science
Department of Computer Science
CN-S2013
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Sensing: PHY and MAC Layer
Issues
MAC Sensing
Sensing and access
strategy
PHY Sensing
Spectrum Sensor
at PHY
CR SENSINGFaculty
DESIGN
= SENSOR + SENSING STRATEGY + ACCESS
of Science
Department of Computer Science
CN-S2013
11 April 2012
19
PHY Sensing
u Energy Detector: Measures the energy received on a primary
band during an observation interval and declares a white space if
the measured energy is less than a properly set threshold. (2) Do
not differentiate PU and CR signals (3) Low complexity
u Waveform-based Sensing: (1) Preambles, midambles can be
used to detect PU signals. (2) Short measurement time;
Susceptible to synchronization errors
u Match Filtering MF: (1) If transmitted signal is known, test using
filters. (2) Dedicated circuitry for each primary licensee
u Radio Identification: Identifying the transmission technologies
used by PUs, channel bandwidth, coverage etc.
u Cyclostationary: PU signal differentiated from noise
Faculty of Science
Department of Computer Science
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Probability
aussian noise (AWGN) sample, and n is the sample
0.4
Note that s(n) = 0 when
thereperformance
is no transmission
bysignal-to-noise ratio (SNR)
and poor
under low
0.3
user. The decision metric
for
the
energy
detector
can
values [48]. Moreover, energy detectors do not work efficiently 9
en as
for
detecting spread spectrum signals [26],0.2[59].
N
!
Let
us 2assume
that the received
M=
|y(n)|
,
(2) signal0.1has the following
simple form
n=0
y(n) = s(n) + w(n) 00
0.2 (1)
0.4
Probabilit
N is the size of the observation vector. The decision
where
s(n)
is the
signal
to PU
be detected,
w(n) is the additive
occupancy
a band
can be
byiscomparing
u H0:ofThe
frequency
is obtained
idle,
there
no
signal
Figure
2.2. Block
diagram
of conventional
energy detector.
Fig. and
3. nROC
curves
for energy d
whiteaGaussian
noise (AWGN)
sample,
is the
sample
ision u H
metric
M against
fixed threshold
λE .isThis
PU
signal
1: The frequency is occupied, there
different
values.
Note
that
= Measured
0 when
is noNSNR
transmission
valent u w(n):
to distinguishing
between
thes(n)
following
twothere
Noise,index.
s(n):
PU signal,
y(n):
signal,
number
of by
2.2.1. Conventional
Energy Detection in AWGN Channel
ses:
samples primary user. The decision metric for the energy detector can
be written as
N (3)
2T
!W 2
!
H0 : y(n) = w(n),
Under AWGN channel, energy received
Yij2For
) ,by energy
secondary
user i (2)
follows
detector,
the pro
M =(Oi = |y(n)|
j=1
H1 : y(n) = s(n) + w(n).
(4) calculated as [41]1
n=0
the distribution
H0 or H1? #
where N is
the sizecan
of the
vector. The decision
performance of the detection
algorithm
be observation
sumPF = 1 − Γ L
on the
occupancyofof
aχ2band can
with two probabilities:
probability
detection
PDHbe0 obtained by comparing

2T W
#
f
(O
|γ)
≈
the Pdecision
metric
M
against a fixed threshold λE . This(2.2)Fig.
i
bability of false alarm
.
P
is
the
probability
F
D
 χ2 (2γ) ofH
differ
1
2T Wit truly
is equivalent
to distinguishing
between
the following
g a signal on the considered
frequency
when
PD =two
1−Γ L
hypotheses:
nt. Thus, a large detection
probability is desired. It can
ulated
as χ22T W and Faculty
of Science
where
χ22T W
(2γ) represent central
and=non-central
chiλE
square
where
is thedistributions
decision
: y(n)
w(n),
(3) thres
Department of Computer Science H0
CN-S2013
21
plete gamma function as For
give
Probability of False Alarm (PF)
Energy Detector:
Binary Hypothesis Test
Effect of Signal to Noise Ratio
(SNR)
Decibel: 10log10(P2/P1)
Generally, sensing performance increases under increasing SNR.
Faculty of Science
Department of Computer Science
CN-S2013
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Comparison of Sensing Schemes
Accuracy
124
IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 11,
ably. In addition, cooperation can solv
problemDetector
and it can decrease sensing t
1.  Energy
Waveform-based
Match
The interference toSensing
primary users ca
2.  Waveform-based
Sensing
Filtering
devices employing spectrum access m
3.  Match
Filtering
simple listen-before-talk (LBT) schem
Radio
4.  Radio
Identification
via analysis
and computer simulatio
Identification
even simple local sensing can be used
5.  Cyclostationary
Cyclostationary
spectrum without causing interference
the other hand, it is shown analytically
Energy
results that collaborative sensing provi
Detector
spectrum capacity gains than local
cognitive radio acts without any knowl
Complexity
of the primary users in local sensing
performance.
Fig. 4. Main sensing methods in terms of their sensing accuracies and
Challenges of cooperative sensing i
complexities.
cient information sharing algorithms a
ity [101], [102]. In cooperative sensing
Faculty of Science
Department of Computer Science
CN-S2013
23
be a priori information about the primary user’s characteristics trol channel (pilot channel) can be impl
Types of Spectrum Sensing
Parallel
Sequential
Proactive
Synchroni
ous
Asynchro
nious
Reactive
SPECTRUM
SENSING
Out-ofband
Local
Cooperative
In-band
Distributed
Faculty of Science
Department of Computer Science
Centralized
CN-S2013
24
Parallel vs. Sequential Sensing
Parallel
If there are N frequency channels
Sequential
Proactive
Reactive
Local
Cooperative
Sense channels 1 to N at the same time (parallel)à
requires N sensing device!
Centralized
Distributed
Synchronous
Asynchron.
Sequential: Sense channels one by one. Which order?
May take too long to find an empty channel.
In-band
Out-of-band
CN-S2013
Proactive vs. Reactive Sensing
Parallel
Sequential
Proactive
Reactive
Local
Cooperative
Proactive Sensing:
CR senses even if it will not transmit immediately,
e.g. periodic sensing.
q Trade-off
collected information about the channels vs. sensing
cost
Centralized
Distributed
Synchronous
Asynchron.
In-band
Out-of-band
Reactive Sensing:
CR senses only if it will transmit or receive
q  Energy-efficient, time to find an idle channel may
be longer than Proactive Sensing.
CN-S2013
Cooperative vs. Noncooperative Sensing
Parallel
Sequential
Proactive
Reactive
Local Sensing:
Each CR senses itself and uses its sensing data to
give a decision on channel state, i.e. idle or busy
q What if hidden node or bad channel conditions?
Local
Cooperative
Centralized
Distributed
Synchronous
Asynchron.
Cooperative Sensing:
CR shares its sensing data with others and utilize the
sensing outcomes of others to give a decision
q  Robust to sensing errors due to hidden node or
fading channels.
q  Cost of cooperation?
In-band
Out-of-band
CN-S2013
Cooperative Sensing
OR COGNITIVE RADIO APPLICATIONS
ATIONS
er
or
ms
re
G
ed
119
119
Fig. 2. Illustration of hidden primary user problem in cognitive radio systems.
u More robust to
sensing errors.
u Hidden node
problem
Cooperate with
this user!
PU is hidden to the CR. CR’s transmission will result in
interference at the PU receiver.
Faculty of Science
in Section II. However,
it is not straightforward to design
Department of Computer Science
algorithms that can do the estimation in code dimension.
CN-S2013
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Centralized vs. Distributed Sensing
Parallel
Sequential
Proactive
Reactive
Local
Cooperative
Centralized
Centralized
A Central Manager (BS or AP) collects CR
sensing data and makes a decision on channel
state, i.e. idle or busy
q Cost of transmission sensing data?
q  What if the Central Manager fails? Single
Point of Failure.
Distributed
Synchronous
Asynchron.
Distributed (Decentralized)
Each CR makes decision itself.
In-band
Out-of-band
CN-S2013
Centralized/Distributed
Cooperative Sensing
119
FOR COGNITIVE RADIO APPLICATIONS
RLGORITHMS
COGNITIVE
RADIO APPLICATIONS
119
SENSING
GNITIVE RADIO APPLICATIONS
ATIONS
io
Decision
Fusion Center
119
119
119
119
GORITHMS
FOR COGNITIVE RADIO APPLICATIONS
pectrum effi119
119
sing accuracy
SENSING
t
wer consump-
mplexity
o
pectrum effi-
veraccuracy
the other
ng
mance and/or
Fig. 2. Illustration of hidden primary user problem in cognitive radio systems.
wer
consumpser problem
in cognitive radio systems.
Increased sensing reliability at the expense of increased
are platforms
communication overhead
plexity
rsal Software
Section II. However,
it is not straightforward
to design(CCC)
Common
control channels
ectrum’sHow
XG toin communicate:
not straightforward to design
mation
code dimension.
ver
the in
other
Faculty of Science
ts simplicity.
Department of Computer Science
mance and/or
algorithms
that
can
do the estimation
in code
dimension.
den
cognitive
primary
radio
usersystems.
problem
in cognitive
radio
systems.
Fig.
2. Illustration
of hidden
primary
user
problem
in cognitive radio
systems.
etector
based
CN-S2013
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R
DIO
NSCOGNITIVE
APPLICATIONS
119 RADIO APPLICATIONS
119
r
r
119
119
119
Decision Fusion: How to decide?
Yes,
there
is PU
No, it is
IDLE
Yes
Yes
No
How to decide? (DECISION FUSION LOGIC)
u  AND
u  OR
u  MAJORITY
u  K-of-N
q  Soft or Hard Decision Combining: Yes or No answers (0-1), or Received Signal
Strength
Faculty of Science
Department of Computer Science
CN-S2013
31
Fig.
2.
Illustration
of
hidden
primary
user
problem
in
cognitive
radio
systems
primary
radio
lem
ration
insystems.
of
cognitive
user
hidden
problem
primary
radioin
systems.
cognitive
user problem
radioinsystems.
cognitive radio systems.
rade-offs
pectrum
adio netnce these
n-specific
exity, and
nate sensions.
Sensitivity r
reliabilimay preccessing a
the band
e, further
gn of efficognitive
–14
–16
–18
–20
1
3
4
5
6
7
Number of cooperating users
8
9
10
■ Figure 4. Required sensitivity of individual cognitive radios to achieve an
overall detection sensitivity of –20 dB under Rayleigh fading vs. the number of
cooperating users.
103
E-OFF
102
Sensing time (ms)
the numction sensensitive
a certain
ement is
and hence
is depicte of local
an overall
9 percent
e number
Rayleigh
n among
the chan-
2
Number of Cooperating Users vs.
Sensing Time
101
Single CR
or 5 CRs
u Cooperation overhead generally
increases with the number of
cooperating
u Optimal number of cooperating
users
100
10–1
ses a nat10–2
3
4
5
6
7
8
9
10
1
2
ssing and
Number of cooperating users
n order to
n particu■ Figure 5. Cooperation-processing trade-off under Rayleigh fading.
increases
due to the
be reportGhasemi
and Elvinooverhead
S. Sousa,
Spectrum Sensing in Cognitive
However,
the communication
associatthe band Amir
Requirements,Challenges
and Design Trade-offs
ed with this
method increases linearly with the
trade-off Networks:
number of users. A more efficient technique has
d and the
been proposed in [13] where all sensing data is
dd to the
collected simultaneously, thereby allowing a
y be balhigher cooperation level at the cost of increased
f processprotocol complexity. Moreover, the cooperation
al sensing
level should be adapted to the fading characterFaculty of Science
istics. In particular, as the fading becomes less
cooperatDepartment of Computer Science
severe (e.g., if there is a line of sight to the prihe undermary user), the optimum trade-off between local
stance, a
Radio
CN-S2013
11 April 2012
32
Synchronous vs. Asynchronous
Sensing
Parallel
Sequential
Proactive
Reactive
Local
Cooperative
Centralized
Distributed
Synchronous
Asynchron.
Synchronous
All CRs have the same sensing schedule to sense a
channel.
q How to synchronize?
q Stop transmission and sense the medium.
Asynchronous
Each CR has its own schedule to sense a channel.
q If other CRs are transmitting while this CR is
sensing, how to distinguish between SU and PU
signal.
In-band
Out-of-band
CN-S2013
In-band vs. Out-of-band Sensing
Parallel
Sequential
Proactive
Reactive
Local
Cooperative
Centralized
Distributed
Synchronous
In-band
CR senses the channel that it is already transmitting
-  To detect if a PU appears
Out-of-band
CR senses channels other than the channel it is in
q To find other spectrum holes
q To find another channel to switch since a PU has
already appeared.
Asynchron.
In-band
Out-of-band
CN-S2013
Challenges of Spectrum Sensing
u Hardware requirements:
§  High speed processing units (DSPs or FPGAs) performing
computationally demanding signal processing tasks with relatively low
delay.
§  Operation in a wide spectrum range
u Sensing-Transmission Tradeoff
u Security: a selfish or malicious user can modify its air interface to
mimic a primary user.
Faculty of Science
Department of Computer Science
CN-S2013
35
Summary
u  Static spectrum access is cumbersome!
u  CR facilitates unused spectrum to be used opportunistically.
u  Spectrum sensing facilitates discovery of unoccupied spectrum.
u  The spectrum sensing can be designed considering various
criteria at MAC and PHY layer.
u  The longer is the sensing duration, generally the higher is the
sensing reliability.
u  Cooperation increases sensing performance but has higher
overhead.
Faculty of Science
Department of Computer Science
CN-S2013
36
References
u  T. Yucek and H. Arslan, A survey of spectrum sensing algorithms for cognitive
radio applications, IEEE Communications Surveys and Tutorials, vol. 11, no. 1,
pp. 116-130, 2009.
u  Ghasemi, Amir, and Elvino S. Sousa. Spectrum sensing in cognitive radio
networks: requirements, challenges and design trade-offs. IEEE
Communications Magazine, 46.4 (2008): 32-39.
Faculty of Science
Department of Computer Science
CN-S2013
37
Questions?
Faculty of Science
Department of Computer Science
CN-S2013
38
Self-Study: Make sure you know
all the terms below
u  Primary User
u  Secondary User
u  Cognitive Radio
u  Spectrum Hole
u  Spectrum Sensing
u  Harmful Interference
u  SNR
u  Cooperative Sensing
u  Dynamic Spectrum Access
u  Static Spectrum Access
u  Spectrum Underutilization
u  Sensing-transmission trade-off
u  Decision fusion logic
Faculty of Science
Department of Computer Science
CN-S2013
11 April 2012
39
Presentation Schedule
Feb 5
Feb 12
Presentation 1: Cognitive Networks (CN)
Feb 19
Presentation 2: Routing in CR Ad Hoc Networks (RA)
Feb 26
No class
March 12
Presentation 3: Cognitive Capabilities in Non-Cognitive Networks (CC)
March 19
Presentation 4: Economics of Cognitive Radio (EC)
March 26
Presentation 5: Radio Environment Maps (REM)
April 2
Presentation 6: Security Issues in CRNs (SEC)
April 9
Presentation 7: Machine Learning for CR (ML)
April 16
Presentation 8: Distributed Spectrum Access (DA)
April 23
Presentation 9: Energy efficiency (EE) and Closing Remarks
!
Faculty of Science
Department of Computer Science
CN-S2013
40
Next week
q 2-Minute Madness Session: In two minutes present your topic’s
basic idea, questions, etc! Only 2 minutes.
Faculty of Science
Department of Computer Science
CN-S2013
41