A quick Peek Of Cognitive Radio

2. M.Owais Asghar Jara
Awais.jara@yahoo.com

3. COGNITIVE RADIO (CR)
Ten Years Of Research in spectrum sensing
and sharing in cognitive radio.
Cognitive radio is an intelligent radio that
can be programmed and configured
dynamically.
Its transceiver is designed to use the best
wireless channels in its vicinity.

4.  Firstly we will learn some basic concepts of
Cognitive Radio

5. SPECTRUM
1. A band of colours, as seen in a rainbow,
produced by separation of the
components of light by their different
degrees of refraction according to
wavelength.
2. Used to classify something in terms of
its position on a scale between two
extreme points.

6. Cognitive radio (CR) can successfully
deal with the growing demand and
scarcity of the wireless spectrum. To
exploit limited spectrum efficiently.
CR technology allows unlicensed users
to access licensed spectrum bands.

7. Types Of Users:
Primary Users (PUs):
Licensed Users having priority of using the
spectrum even in the presence of secondary
users.
Secondary Users (SUs):
Unlicensed users having access of using the
spectrum outside of the area used by primary
users.

8. SPECTRUM SENSING
In practice, the unlicensed users, also
called secondary users (SUs), need to
continuously monitor the activities of the
licensed users, also called primary users
(PUs),to find the spectrum holes (SHs),
which is defined as the spectrum bands
that can be used by the SUs without
interfering with the PUs. This procedure is
called spectrum sensing

9. SPECTRUM HOLES:
The Part Of The band Which Is Not Used By the Primary
Users. Also called white spaces.

10. Types Of Spectrum Holes (SHs)
1. Temporal SH:
A temporal SH appears when there is no PU
transmission during a certain time period
and the SUs can use the spectrum for
transmission.
2. Spatial SH:
A spatial SH appears when the PU
transmission is within an area and the SUs
can use the spectrum outside that area.

11. Techniques Of Detecting Spectrum
Holes:
1.Matched Filtering Detection
2.Energy Detection
3.Feature Detection
We will discuss these soon.

12. FACTORS EFFECTING PERFORMANCE OF A
SPECTRUM:
1.Noise Uncertainty
2.Multipath Fading
3.Shadowing

14. Since the PU receiver detection is difficult,
most study focuses on PU transmitter
detection.
In general, it is difficult for the SUs to
differentiate the PU signals from other pre-
existing SU transmitter signals. Therefore, we
treat them all as one received signal, s(t). The
received signal at the SU, x(t), can be
expressed as:

15.  Where n(t) is additive white Gaussian noise(AWGN).
 H0 denotes the hypothesis of the absence PU signal
while H1 denotes the hypothesis of the presence of the
PU signal.
 The Objective for spectrum is to decide between the H0
and H1 based on the observation x(t).

16.  The detection performance is characterized
by the probabilities of detection, Pd, and
false-alarm, Pf. Pd is the probability that the
decision is H1 , while H1 is true; Pf denotes
the probability that the decision is H1,while
H0 is true. Based on Pd, the probability of
misdetection Pm can be obtained by
Pm = 1 - Pd.

17.  There are two basic hypothesis testing
criteria in spectrum sensing:
1. The Neyman-Pearson Test
2. Bayes Test

18.  In This test we assume that he probability of
detection i-e Pd is maximum.
 The NP test aims at maximizing Pd (or
minimizing Pm) under the constraint of Pf≤
α, where α is the maximum false alarm
probability.

19.  Bayes test minimizes the average cost given
that the prior probability of hypothesis Pr(Hi)
and Pr(Hi/Hj) is the probability of declaring
Hi and Hj is true.
 Both Of them are equivalent to likelihood
ratio.
In both of the tests the distribution of P(x/Hi)
are known i-e i ε {0 , 1}

20.  For Unknown parameters we have two tests.
1. Generalized likelihood ratio test (GLRT)
2. The Sequential Probability Ratio Test (SPRT)

21.  In the GLRT, the unknown parameters are
determined by the maximum likelihood estimates.
 GLRT detectors have been proposed for multi
antenna systems in and for sensing
OFDM(Orthogonal frequency division multiple
access) signals in by taking some of the system
parameters, such as channel gains, noise variance,
and PU signal variance as the unknown parameters.

22.  In the SPRT, samples are taken sequentially
and the test statistics are compared with two
threshold g0 and g1 (g0 <g1), which are
determined by the detection requirements.
Using the SPRT, the SU makes decisions
according to that requirement.

23.  A new limitation, called risk floor, has been
discovered for traditional physical layer
sensing schemes, which is caused by finite
channel dwell time, where longer
observation windows are more likely to mix
the PU’s behaviour from multiple states,
leading to degraded performance.

24.  Matched Filter Detector
 Energy Detector
 Feature Detector
 Other Techniques
1. Eigen Value Based
2. Moment based Detector

25.  If the SUs know information about the PU
signal, the optimal detection method is
matched filtering which correlates the known
primary signal with the received signal to
detect the presence of the PU signal and
thus maximize the signal-to-noise ratio(SNR).

27.  Energy detector is the most common
spectrum sensing method. The decision
statistics of the energy detector are defined
as the average energy of the observed
samples.
 The Decision is made by
Comparing Y with Threshold(γ)
If Y>γ PU is present other wise
Absent.

28.  No Doubt it is easier to implement but below
threshold a reliable detection can not be
achieved due to sensing duration.
 The energy detector cannot distinguish the
PU signal from the noise and other
interference signals, which may lead to a
high false-alarm probability.

29.  A cyclostationary process is signal having
statistical properties that vary cyclically with
time.
 Cyclostationary detector is one of the
feature detectors that utilize the
cyclostationary feature of the signals for
spectrum sensing.

30.  Generally, feature detector can
distinguish noise from the PU signals and
can be used for detecting weak signals at
a very low SNR region, where the energy
detection and matched filtering detection
are not applicable.
 To capture the advantages of the energy
detector and the cyclostationary detector
while avoiding the disadvantages of them,
a hybrid architecture, associating both
othem, for spectrum sensing has been
proposed.

31.  Eigen-Value Based Detector:
 In a multiple-antenna system, eigenvalue-based
detection can be used for spectrum sensing. In
maximum-minimum eigenvalue and energy with
minimum eigenvalue detectors have been proposed,
which can simultaneously achieve both high
probability of detection and low probability of
false-alarm without requiring information of the PU
signals and noise power

32. 1. Wideband Sensing:
The main challenge is when there are
limited spectrums like in mobile phones
then sensing the spectrum holes is much
difficult.
Energy detector for narrowband sensing,
the sensing time and detection thresholds for
each narrowband detector are optimized
jointly, which is different from the
multiband joint detection framework

33. 2. Synchronization:
Besides the synchronization issue for quiet
sensing period, spectrum synchronization
before the data transmission for non-
contiguous OFDM (Orthogonal frequency
division multiple access) based systems is
also a challenge.

34.  The performance of spectrum sensing is
limited by noise uncertainty, multipath
fading, and shadowing which are the
fundamental characteristics of wireless
channels.
 Firstly we will learn some definitions.

35.  Multipath fading occurs in any environment
where there is multipath propagation and
there is some movement of elements within
the radio communications system. This may
include the radio transmitter or receiver
position, or in the elements that give rise to
the reflections.
 Shadowing is the effect that the received
signal power fluctuates due to objects
(Obstacles) obstructing the propagation path
between transmitter and receiver

37.  Centralized CSS
 Distributed CSS
 A centralized CSS system consists of a
secondary base station (SBS) and a number of
SUs. In this system, the SUs first send back
the sensing information to the SBS. After
that, the SBS will make a decision on the
presence or absence of the PU signal based
on its received information and informs the
SUs about the decision.

38.  Data fusion: Process of combining the
reported shared sensing results.
 Different data fusion schemes for CSS have
been studied. Reporting data from the SUs
may be of different forms, types, and sizes:
 Soft Combination
 Hard combination

39.  In soft combination, the SUs can send their
original or processed sensing data to the SBS.
 To reduce the feedback overhead and
computational complexity, various soft
combination schemes based on energy
detection have been investigated.
 In these schemes, each SU sends its
quantized observed energy of the received
signal to the SBS. By utilizing LRT at the SBS,
the obtained optimal soft combination
decision is based on a weighted summation
of those energies.

40.  Where Yj is the local test statistics, wj is the
weight.
 Soft combination schemes can provide good
detection performance, the overhead for
feedback information is high.
 It makes the CSS impractical under a large
number of cooperative SUs. A soften-hard
combination with two-bit overhead has been
proposed to provide comparable
performance with less complexity and
overhead

41.  For hard combination, the SUs feedback their own binary decision results to the SBS
Less channel bandwidth
Methods:
 And
 OR
 Majority
User selection:
 SUs are located differently and strengths of received PU signals are different.
 The optimal detection/false-alarm probabilities are achieved by selectively
cooperating among SUs with high detection SNRs of the PU signal.
 Data-fusion range is identified as a key factor that enables effective CSS
 The SUs in the data-fusion range cooperate to sense PU signals while others do not.

42.  SPRT can opportunistically reduce the sample
size required to meet the reliability target
 sequential detection scheme has been designed
to minimize the detection time.
 each SU calculates the log-likelihood ratio of its
measurement and the SBS accumulates these
statistics to determine whether or not to stop
making measurements.
 A stopping policy and an access policy are given
to maximize the total achievable rate of the SU
system under a misdetection probability
constraint for each channel.

43.  An alternative to reduce the sensing and
feedback overhead.
 Each SU senses linear combinations of multiple
narrow bands by selective filters.
 The results are reported to the SBS.
Drawback of centralized CSS:
The cooperative
SUs need to feed back information to the SBS,
which may incur high communication overhead
and make the whole network vulnerable to node
failure.

44.  Doesn’t rely on FC(fusion centre) for cooperative
decision.
 CR users communicate among themselves and
converge to unified decision on the presence and
absence of PUs
 By iterations
 Based on a distributed algorithm, each CR user
 Send its own sensing data to other users.
 Combines its data with the received data
 Decides whether the PU is present or absent
 Send its decision
 Repeat until converge.

45.  one SU works as a amplify-and-forward (AF)
relay for another SU to get the agility gain
when the relay user detects the high PU
signal power and the link between two SUs is
good.
 Detect-and-relay (DR) scheme has been
proposed, where only the relay SUs that
detect the present of the PU signals forward
the received signals to the SU transmitter.

46.  CR networks may be equipped location and
environmental awareness features to further
improve the performance.
 The location information of PUs and SUs can
be used for determining spatial SHs.
 it is very important in public safety CR
systems to detect and locate victims.

47.  Common control channel
 Synchronization
 Non-ideal information

48.  Common control channel between the SUs
and the SBS is assumed in most of existing
work, which requires extra channel resources
and introduces additional complexity.
 it is difficult to establish a control channel at
the beginning of the sensing stage and the
change of the PUs’ activities may affect the
established control channel.

49.  SUs locate at different places in practical CR
systems, resulting in a synchronization
problem for data fusion
 To enable combination of both synchronous
and asynchronous sensing information from
different SUs, a probability-based
combination method has been proposed.

50.  Performance of CSS based on the perfect
knowledge of the average received SNR of the
PU transmitter signal which is not always the
case
 In the noiseless-sample-based case, The
probability of false alarm decreases as the
average SNR estimation error decreases for both
independent and correlated shadowing's
 In the noise-sample-based Below the threshold,
the probability of false alarm increases as the
noise level increases, where the probability
decreases as the noise increases above the
threshold.

51.  Spectrum sharing is a technique for the sake
of secondary users to allocate the spectrum
holes of primary user area so that they can
share the spectrum together.
 But secondary users must have correlation
between them.

52.  Based on the spectrum bands, there are two
types:
1. Open Spectrum Sharing
2. Licensed spectrum sharing

53.  In this technique All the users have the equal
right to access among the channels. The
spectrum sharing among Secondary Users for
the unlicensed bands belongs to this type.

54.  This type is for the Secondary Users, they
need to adjust their parameters, such as
transmit power and transmission strategy, to
avoid the interruption to the Primary Users.

55.  In order to limit interference to the Primary users
created by the Secondary users, various resource
allocation and power control schemes have been
proposed for the CR networks.
1. Single Carrier and single antenna system.
2. Multi Carrier and multi channel system
3. Multi Antenna System
4. Multi Hop System

56.  In this type as the name indicates only a
single transmitter channel is used and
Secondary users try to access that channel.
 In such a system the most important
constraint is threshold. So we need to take
care of power transmitters.

57.  In a multi-carrier or multi-channel system,
interference generated by the Secondary
User to the Primary User can be considered
either in the whole bands/channels or each
sub-band (sub channel) separately.
 The IPCs(interference power constraints) for
the Primary User can be divided into two
types: peak and average Interference Power
Constraints.

58.  Another powerful technique that uses
multiple antennas in the CR network, which
we refer to as CR-Multiple Input Multiple
Output, has caught the attention of many
researchers. By adjusting the weights of the
multiple antennas, the Secondary User
transmitter steers the transmit beam forming
vector, originally pointing toward the
Secondary User receiver, such that the
interference to the PU receiver becomes
zero. I will explain this through figure:

59.  In this type a signal is divided into different
part of interests and we will select our
suitable path.
It can use different nodes and different bands
by different paths.
We use the path of our concern.

60.  In a cognitive overlay network, multiple
secondary users compete to share the
spectrum holes (i.e., channels unused by the
primary users). We model such spectrum
sharing using the non cooperative game
theory. The total CR network capacity is
maximized when the game reaches a Nash
equilibrium (NE). The above study considers
SU system only.

61.  If there are multiple available bands, the CR
networks should be able to decide the best
one for each SU. This procedure is called
spectrum decision.
 Two Main Types:
1. Channel Characterizing
2. Spectrum Assigning

62.  During the transmission of SUs, the PUs may
appear to claim their assigned channels.
When this occurs, SUs need to stop
transmission, vacate the channels in
contention, and find other available channels
to continue transmission, which is called
spectrum handoff.

63.  Secondary Users immediately use the
reserved spectrum bands when it is
necessary. However, the number of the
reserved bands should be chosen carefully to
balance the spectrum efficiency and handoff
performance.

64.  In CR networks, the transmission protocols of
the Secondary Users can be designed to
achieve satisfactory system performance
together with fulfilling the Interference
Power Constraints for the Primary Users.

66.  Joint sensing and access:
 Channel estimation

67.  With different objectives of Secondary User
systems, different protection levels for PU
systems are needed. It is beneficial to
consider these two parts joint sensing time
and power allocation schemeto maximize the
throughput of Secondary User systems

68.  The link condition between PU and SU is an
important parameter for most of techniques
in CR networks. To estimate the link
condition, the SU can observe PU’s behavior
by sending out a disturbing probing signal.

69.  Due to the rapid growth of wireless communications,
more and more spectrum resources are needed.
Within the current spectrum framework, most of the
spectrum bands are exclusively allocated to specific
licensed services. However, a lot of licensed bands,
such as those for TV broadcasting, are underutilized,
resulting in spectrum wastage . This has promoted
Federal Communications Commission (FCC) to open
the licensed bands to unlicensed users through the
use of cognitive radio (CR) technology .The IEEE
802.22 working group has been formed to develop the
air interference for opportunistic secondary access to
TV bands

70.  The applications of CR in cellular networks
are emerging in recent years. To overcome
the indoor coverage problem and adapt to
traffic growth, The concept of small cells,
such as femtocells, has been proposed in
3GPP LTE-Advanced (LTE-A and IEEE 802.16m,
and companies like Pico Chip driving
femtocell revolution. The femtocell unit has
the function of the Typical BS.

71.  Disasters and accidents, first re-sponders
need to detect and locate survivors and
maintain reliable communications between
responders and public safety agencies. The
infrastructure of the current wireless
communication systems is inadequate to
meet the future demands of emergency
response. The opportunistic spectrum usage
provided by CR techniques could be used to
realize efficient and reliable emergency
network transmissions

72.  . The FCC has designated a 700 MHz (698-806
MHz) frequency band for emergency use. The
public safety broadband licensee will have
priority to access these portions of the
commercial spectrum during emergency. Still,
there remain some challenges for emergency
networks. Possible applications for CR include
accurately locating survivors in extreme
environments and improving reliability and
efficiency of communications. Energy efficiency
is particularly important because battery life
limits successful operation

74.  CR is a must-have technique for military
usage. With CR, the users can recognize the
enemies’ communications and protect their
owns. Moreover, the users can search for
more transmission opportunities. The US
department of defense (DoD) has already
established programs such as Speakeasy radio
system and next Generation (XG) to exploit
the benefits of CR techniques

75.  Under severe conditions or extreme
situations, e.g., natural disasters and
accidents, first re-sponders need to detect
and locate survivors and maintain reliable
communications between responders and
public safety agencies. The infrastructure of
the current wireless communication systems
is inadequate to meet the future demands of
emergency response

76.  With spectrum sensing techniques, the SUs
are able to monitor the activities of the PUs.
 Based on the spectrum sensing results, the
SUs can access the spectrum bands under the
interference limit to the PUs

77.  Different spectrum sharing and allocation
schemes have been considered to increase
the spectrum efficiency. Even though many
critical issues in CR have been addressed in
the past decade, there are still some
challenges. Nevertheless, we believe the CR
technology will be applied to many real
systems in the near future.

78.  Due to the space limitation, we have left out
some topics, such as security, policy issues,
and CR implementation architecture.