Spectrum Sensing with Energy Detection in Cognitive Radio Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
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Spectrum Sensing with Energy Detection in Cognitive Radio Networks
Milan Patel1, Kirtan Patel2, Sagar Patel3
1B.Tech Student, Electronics and Communication, Chandubhai S. Patel Institute of Technology, Gujarat, India
2B.Tech Student, Electronics and Communication, Chandubhai S. Patel Institute of Technology, Gujarat, India
3Assi. Professor, Electronics and Communication, Chandubhai S. Patel Institute of Technology, Gujarat, India
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Abstract - Cognitive radio could be a low-price
communication system, which might opt for the on the
market frequencies and waveforms mechanically on the
boundaries of avoiding interrupting the authorized users. The
spectrum sensing is that the key modify technology
in psychological feature radio networks. It ready
to fill area within the wireless spectrum and might
dramatically increase spectral potency. The author use
matlab to breed the received signals from the psychological
feature radio networks associated an energy observe or to
detect whether or not the spectrum is being employed. The
report additionally parallelsthe onpaper worth andtherefore
the simulated result so describes the connection between the
signal to noise quantitative relation and therefore
the detections. At last, the method, energy detection and
simulation and result are mentioned that is taken into
account because the pointers for the longer term work.
1. INTRODUCTION
1.1 Background
Today with the technology and therefore the science
developing the spectrum was nearly totally occupied. But
there area until still sizable amount of multiple allocations
required producing enough capability for the numerous
wireless services for money and non-financial application,
like defense, traffic, and scientific exploration.
Fig-1: Spectrum allocations in the US [1]
Figure (1) shows the spectrum allocations within the US.
every color stands for a service kind which is allotted to the
special band within the US. Several of the first allocations
such as TV (TV), FM (FM) radio, international positioning
systems (GPS), Wi-Fi, Bluetooth, etc. area unit identical. 2.4
GHz and 5 GHz bands area unit usually used for wireless
laptop networking, these bands, and a few others area unit
referred to as the economic, scientific, medical (ISM) bands.
Fig-2: Received power vs. frequency in Singapore [2]
Fig-3: Band by band average spectrum occupancy in
Singapore [2]
Meanwhile, in Singapore, low frequency spectrum bands
area unit used crowdedly however high frequencyspectrum
bands area unit used partly. Figure(2) and Figure(3) show
the occupancy of spectrum bands in Singapore, we are able

to get from the figure that the use of the spectrum is
extremely low in high frequencies and also the utilization of
the spectrum in low frequencies is far higher. A survey in [2]
shows the common occupancy of the spectrum is 4.54% in
Singapore.
Under these things, a way to improve the employmentofthe
spectrum bands could be a matter of nice urgency. The
simplest thanks to improve the employment potency of the
spectrum allocationsisthatthepsychological cognitive radio
(CR) technology, and therefore the definition of CR may be
expressed as:
“Cognitive radio is a radio of an intelligent wireless
communication system that senses and is aware of its
surrounding environment and capable to use or share the
spectrum in an opportunistic mannerwithoutinterfecing the
licensed users” [4]
In [5], it's given that in 1999, Joseph Mitola giventheconcept
of CR 1st, in an exceedingly seminar at KTH, The Royal
Institute of Technology, then Mitola and Gerald Q. Maguire,J
printed CR later in an article. CR has been thought-aboutasa
promising and effective technology to radio technology.
The inefficient mistreatment ofallottedspectrumtoprimary
licensed users may be a quite common development,andCR
technology has been thought-about as a key answer for that.
It permits unauthorized or secondary users WHO square
measure referred as CR users to access spectrum bands that
are allotted to be licensed. To get the aim of accessing
spectrum bands, CR users sense the spectrum so that they
will sight the standing through a primary transmitter signal.
Thus, spectrum sensing is one in every of the foremost vital
problems in CR networks [6].
1.2 Research Aim
The aim of the analysis is to grasp the use of spectrum
sensing in psychological cognitive radio networks, and
investigate the technique of the spectrum sensing. We’ll use
matlab to simulate the signalsfromthe psychological feature
radio networks associate degreed an energy detector to see
the standing of the first users.
After obtaining the result, try and notice the connection
between the issue SNR to and therefore the final detections
and investigate however the SNR influence the detections.
Comparison the theoretical price and therefore the
measured price to see whether or not the simulation
operating with success.
2. THEORY
The Gaussian distribution, Maximum a Posteriori Energy
Detection for Spectrum Sensing Additive white Gaussian
noise and Q-function are mainly used through the whole
process. This part indicates the details of the theories.
2.1 Gaussian Distribution
In applied math, the Gaussian distribution could be
a continuous like distribution that's usually used as a
primary approx. to explain real valued random variables
with 2 parameters value μ and also the variance , the
previous makes the placement of the height and also
the latter makes the breadth of the distribution.
(1)
Fig-4: Graph of Gaussian distribution.
The Gaussian distribution graph with numerous norm and
variance is given in Figure. As we all know that the Gaussian
distribution may be a important chance distributionin radio
space. The signaling from the measuring system, obeys the
Gaussian distribution, and that we can categorical the
signaling in Gaussian distribution later.

2.2 Addictive white Gaussian noise
Additive white Gaussian noise could be a style of noise that
exists within the communication channels typically. In an
Additive white Gaussian noise channel model, we tend to
continually assume that there's no alternative distortion or
effects from other sources. Additive white Gaussian noise
could be a model for the thermal noise generatedbyrandom
negatron movement within the receiver [7]. Here is an
example signal with Additive white GaussiannoiseinFigure.
Fig-5: Graph of Additive white Gaussian noise
Figure shows the instance signal with AWGN, the bluesignal
is that the example and therefore the inexperienced signal is
that the signal with AWGN. There will be continually AWGN
throughout the transmission of signals in metallic element
network. Wherever we all simulate the received signal, the
AWGN ought to be supplemental to the received signal.
2.3 Q-function
The Q-function may be a convenient thanks to categorical
right-tail possibilities for Gaussian random variables. For
x∈R, letter (x) is outlined because the chance that a typical
traditional chance exceeds x:
(2)
The qualities of the Q-function are as follows:
Q(a)=
Fig-6: Graph of Q-function
The graph of the Q-function is shown in Figure that is drawn
by matlab. Wherever we are going to derive the warning
chance and therefore the threshold, since they conform the
statistical distribution, the previous are going to be
expressed in terms of the letter of the alphabet operate and
therefore the latter are going to be expressed in terms of the
inverse of the letter of the alphabet operate.
2.4 Maximum Posteriori Energy Detection for
Spectrum Sensing
The maximum a posteriori detector is known to be optimum
in CR networks [8]. Once CR users begin the spectrum
sensing to search out the primary users standing, the received
signal r (t).
= (3)
Where stands for no signal transmitted, and stands for
signal transmitter, s(t) is that the signal wave, and
additionally the n(t) is also a zero-mean AWGN. The
detection chance and additionally the warning chance are
going to be expressed in Equation through the MAP
detection:
(4)
In Equation (4), λ is also a decision threshold of MAP
detection. Caught to be unbroken as small as potential to

avoid underutilization of transmission opportunities, in
another hand, have to be compelled to be unbroken as bigas
potential for constant reason.
Among the MAP detection, the output of the measuring
instrument is known as a result of the Chi-square
distribution [10]. If the amount of samples is large, with the
central limit theorem, we have a tendency to area unit able
to assume that the Chi-square distributionisapproximate as
scientist distribution[9]:
Y (5)
Where n is that the variability of the samples, is that the
variance of the noise, the is that the variance of the
received signal s(t), as we know, the minimum rate got to be
2W from the Nyquist sampling theorem, so n are going to be
delineated as a try of W, where is that the observation time
and W is that the data live of the spectrum. From Equation
(4)(5). Expressed as follows [8]:
(6)
From the relative atomic mass we are able to get that
the warning chances varies with the W and also
the observation time. We are able to get the edge as:
(7)
The parameters, the warning likelihood and also the
threshold square measure the vital parametersinchromium
networks, the warning likelihood ought to be unbroken as
tiny as potential to avoid underutilization of transmission
opportunities, and that we can take a look atthedetectionsif
it's affordable below the warning direction[8].
3. TYPES OF COGNITIVE RADIO
Depending on transmission and reception parameters,there
are two prime types of cognitive radio:
Full Cognitive Radio (Mitola radio), during
which each doable parameter discernible by a wireless
node is considered.
Spectrum Sensing CognitiveRadio inwhichonlythe radio-
frequency spectrum is considered.
Other types are dependent on parts of the spectrum
available for cognitive radio:
Licensed Band Cognitive Radio, capable of using bands
assigned to licensed users which will operate on unused
television channels.
Unlicensed-Band Cognitive Radio, which can only use
unlicensed parts of the radio frequency spectrum. One such
system is described in the IEEE 802.15 Task Group 2
specifications, which focus on the coisidence of IEEE
802.11 and Bluetooth.
Spectrum mobility: method by whichevera cognitive-radio
user changes its frequency of operation. Cognitive-radio
networks aim to use the spectruminan exceedinglydynamic
look by permitting radio terminals to work within the best
offered band, maintaining uninterrupted communication
needs throughout transitions to higher spectrum.
Spectrum sharing: Spectrum sharing psychological
feature radio networks grants psychological feature radio
users to share the spectrum bands of the licensed-band
users. However, the psychological feature radio users ought
to prohibit their transmit power in order that the checking
caused to the licensed-band users is unbroken below a
precise threshold.
Sensing-based Spectrum sharing: In sensing-based
spectrum allocation psychological feature radio networks,
psychological featureradiousersinitial receivethespectrum
allotted to the accredited users to sight the state of the
accredited users. Supported the detection results,
psychological feature radio users set their transmission
methodology. If the accredited users don't seem to be
victimization the bands, psychological feature radio users
can transmit over those bands. If the certifiedusersarea unit
victimization the bands, psychological feature radio users
share the spectrum bands with the accredited users by
proscribing their transmit power.
4. PROCESS AND RESULT
In the methodology of the experiment, we've got a bent to
write the signal in matlab to simulate the sign from the
measuring instrument. It consists of the energy values of
each samples signal. Then vogue associate energy notice or
to sight the energy of assorted samples from the simulated

signal we've got a bent to induce. Comparison the energy
we've got a bent to sight with the sting.
We know that the minimum SNR is that the smallest amount
amplitude needed to rewrite the received signals. So, we
tend to modify the SNR to look at the affiliation between the
SNR and thus the ultimate detections.Inthe end,comparison
the theoretical value and thus the simulated value to
examine whether or not or not the simulation operative
commonly and thus the detectionswe'vegota benttoinduce
is reasonable.
3.1 Encode the Energy Detector
Then we tend to came upon the energy observe or in matlab
to detect and compare the signaling that is simulated with
threshold. the concept of the energy observe or is to detect
the energy of the various samples signal and so examination
the energy with the brink to ascertain if there'sprimaryuser
or not.
In this experiment, we tend to take the SNR = 10 dB and the
false alarm probability = 0.01 and take a hundred samples
from the simulated signaling to calculate their energy
compared with the brink severally to work out whether or
not a authorized user is gift or not, and add all the samples
that area unit detected. The every samples energy is shown
in Table.
Through the detection we tend to get the every samples
energy and also the threshold lambda =0.2147.Examination
with the detections, there are a unit 3 samples energy larger
than threshold thus there are a unit three spectrums
occupied and there three detections.
Since we set the false alarm probability = 0.01, thus there
caught to be ninety nine detections in a hundred samples in
theory. However the simulated result we tend to get is three
detections. It’s wholly completely different compared with
the theoretical result. As we all know that the minimumSNR
is that the least amplitude required rewriting the received
signals, thus SNR perhaps influence the resultwetendtoget.
we are going to modification the SNR and so repeat the
simulation to ascertain the link between the SNR and also
the detections.
3.2 SNR and Detection
We know that In metallic element networks, to see the
spectrum convenience, metallic element user want applied
mathematics data on the received primary signals,therefore
the minimum SNR is that the least amplitude required to
decipher the received signals. 1 we tend to take theSNR=10
dB for Associate in example, however we tend to get the
result that is completely totally differentwiththetheoretical
result. Thus we modify the SNR from 10 dB to zero and find
the detections that square measure showed in Table.
Table-1: Detections get from different SNRs
SNR detection
-10 3
-9 4
-8 10
-7 18
-6 40
-5 71
-4 80
-3 96
-2 98
-1 99
0 100
Fig-7: detections get from different SNRs
As we will see from the Figure, with the increasing of the
SNR (from 10dB to 0) the detections we have a tendency to

get conjointly inflated and among 7 dB and 5 dB, the
increasing slope is that the largest. That the SNR influences
the detections. It indicates that with the increasing of the
SNR, the additional spectrumsthatsquaremeasureoccupied
we will sight.
3.3 Comparison with Theory and Simulated Result
We can ensure that the worth of SNR can influence the
detections we have a tendency to get. Thus during this
section, we will compare the theoretical worthandtherefore
the simulated result to urge an appropriate worth for SNR.
We modify the SNR from 10 dB to 0 then get the warning
likelihood in Equation and therefore the results showed in
Table.
(8)
Table-2: SNR & false alarm probability
SNR False alarm probability
-10 0.97
-9 0.96
-8 0.90
-7 0.82
-6 0.60
-5 0.29
-4 0.20
-3 0.04
-2 0.02
-1 0.01
0 0
Fig-8: SNR & false alarm probability
Figure shows the diagram between SNR and therefore
the warning chance. As we all know that that the falsealarm
probability = 0.01, the figure higher than shows
that once SNR is between 2 dB and 0, that the false alarm
probability= 0.02, 0.01, and 0, that square
measure the principally approximate to the theory value
= 0.01. Therefore once SNR is between-2 dB to 0,the energy
detector performs best. During this half we have a tendency
to take many times of the simulation to form the result we
have a tendency to get additional scientific. We have a
tendency to take the SNR = 1dB, which may let the energy
detector performs best that the false alarm probability
= 0.01, then we have a tendency to simulate ten times, and
that we get the result showed in Table.
Table-3: Times& detections
Times
we try
False alarm probability
1 0.03
2 0.01
3 0.02
4 0.03
5 0
6 0.02
7 0.01

8 0.02
9 0.01
10 0.02
Fig-9: false alarm probabilities in several times
The Figure shows the warning likelihood in 10 times of the
experiment with SNR = 1 dB, the average value of the 10
results is 0.017; therefore false alarm probability
is 0.017. It’s approximate to theory worth 0.01, it matches
the warning theoretical worth Pf= 0.01 so theresult wehave
a tendency to get is cheap.
3.4 Discussion of the Simulation and the Result
As we have a tendency to do the simulation, we have a
tendency to succeed obtaining the energy of a 100 samples
and calculate the warning chance. It’s totally
different from the idea price we have a tendency
to take. Thatthe SNR areoften thoughtof as a crucial issue to
influence the detections. By dynamical the worth of the
SNR, we have a tendencyto get thelink betweentheSNR and
therefore the detections, from the diagram, we are able
to see from 2dB to 0, SNR makes the energy detector
performs best. We decide a correct price of SNR and repeat
the simulation 10 times, we are able to the theory value
= 0.017, it matches the idea price = 0.01 inside acceptable
errors. Therefore, the results area unit satisfactory and
therefore the energy detector work with success within
the simulation.
4. CONCLUSIONS
We accomplish simulate the output signals, and that we
distinguish whether or not there are primary users or not,
we have a tendency to get however the SNR influences the
detections. We have a tendency to additionally get the
acceptable SNR for the energy detector. Thereforewehavea
mindset to around get the ultimate results of the spectrum
sensing for subjective feature radio supported Energy
Detection as we have a tendency to expected.Bycomparison
the theoretical price and also the simulated price we will get
that the result we have a tendency to get is cheap and
scientific. Considering the disadvantages and restriction of
the man oeuvre as mentioned in discussion half, we are
going to do further job. 1) Set the vary of thefailure between
the brink and also the detected energy to tell aparttheresult
at intervals the suitable errors. 2) Undertaking and
implement the energy detection in C code to sense the
spectrum in metallic element networks at intervals the
LINUX operating system.
REFERENCES
[1] A. F. Bruce, “History and Background of Cognitive Radio
Technology” in Cognitive Radio Technology, Elsevier
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[2] M. H. Islam, C. L. Koh, S. W. Oh, X. Qing, Y. Y. Lai, C. Wang,
Y. C. Liang, B. E. Toh, F. Chin, G. L. Tan, and W. Toh,
“Spectrum Survey in Singapore: Occupancy
Measurements and Analyses,” CrownCom, vol. 3, no. 14,
pp. 1-7, Jul. 2008
[3] D. Cabric, S. M. Mishra, and R. W. Brodersen,
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[4] S. Haykin, “Cognitive radio: Brain-empowered wireless
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software radios more personal,” IEEE Pers. Commun.,
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[6] N. T. Khajavi and S. M. S. Sadough, “Improved Spectrum
Sensing and Achieved Throughputs in Cognitive Radio
Networks,” WiAD, vol. 6, pp. 1-6, Jun. 2010
[7] H. Tang, “Some physical layer issues of wide-band
cognitive radio system,” DySPAN, pp. 151-159, Nov.
2005.
[8] W. Y. Lee and I. F. Akyildiz, “Optimal spectrum sensing
framework for cognitive radio networks” IEEE Trans.
Wireless Commun., vol. 7, pp. 3845-3857, Oct. 2008
[9] F. F. Digham, M. S. Alouini, and M. K. Simon, “On the
energy detection of unknown signals over fading
channels, Communications, 2003.ICC '03. IEEE
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[10] J. G. Proakis, Digital communications,fourthed.McGraw-
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[11] A. B. Idris, R. F. B. Rahim and D. B. M. Ali, “The Effect of
Additive White Gaussian Noise and Multipath Rayleigh
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[13] Sagar Patel, DevangkumarPatil andDhvanit Mehta,
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BIOGRAPHIES
“Milan Patel did his B.E. in
Electronics and Communication
Engineering from, Chandubhai S.
Patel Institute of Technology
changa, His research areas are
wireless communication
engineering, especiallyincognitive
radio. He is presently studying in
the department of EC engineering
at Charotar University of Science
and Technology Changa Gujarat
India since July 2013."
“Kirtan Patel did his B.E. in
Engineering from, Chandubhai S.
Patel Institute of Technology
changa, His research areas are
wireless communication
engineering, especiallyincognitive
radio. He is presently studying in
India since July 2013." “
“Sagar Patel did his B.E. in
Engineering from North
Maharashtra Uni. His research
areas are wireless communication
engineering, especially in MIMO -
STBC. He is presently serving in
India since Oct. 2008 and also
doing Ph.D. form CHARUSAT
University, Changa. He is teaching
the subjects of under graduate
engineering students of this
university.”
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