spectrum sensing with energy detection in cognitive radio ... · “cognitive radio is a radio of...

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

© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | 301

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 parallels the on paper worth and therefore 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 frequency spectrum

bands area unit used partly. Figure(2) and Figure(3) show

the occupancy of spectrum bands in Singapore, we are able



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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 employment of the

spectrum bands could be a matter of nice urgency. The

simplest thanks to improve the employment potency of the

spectrum allocations is that the psychological 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 manner without interfecing the

licensed users” [4]

In [5], it's given that in 1999, Joseph Mitola given the concept

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-about as a

promising and effective technology to radio technology.

The inefficient mistreatment of allotted spectrum to primary

licensed users may be a quite common development, and CR

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 signals from the 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 distribution in 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 generated by random

negatron movement within the receiver [7]. Here is an

example signal with Additive white Gaussian noise in Figure.

Fig-5: Graph of Additive white Gaussian noise

Figure shows the instance signal with AWGN, the blue signal

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 big as

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 distribution is approximate 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 parameters in chromium

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 at the detections if

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 Cognitive Radio in which only the 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 whichever a cognitive-radio

user changes its frequency of operation. Cognitive-radio

networks aim to use the spectrum in an exceedingly dynamic

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 feature radio users initial receive the spectrum

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 certified users area 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

https://en.wikipedia.org/wiki/IEEE_802.15




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. In the end, comparison

the theoretical value and thus the simulated value to

examine whether or not or not the simulation operative

commonly and thus the detections we've got a bent to induce

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's primary user

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 minimum SNR

is that the least amplitude required rewriting the received

signals, thus SNR perhaps influence the result we tend to get.

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 the SNR = 10

dB for Associate in example, however we tend to get the

result that is completely totally different with the theoretical

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 spectrums that square measure occupied

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 worth and therefore

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 false alarm

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 the result we have

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. That the SNR are often thought of as a crucial issue to

influence the detections. By dynamical the worth of the

SNR, we have a tendency to get the link between the SNR 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. Therefore we have a 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. By comparison 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 the failure between the brink and also the detected energy to tell apart the result 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 Inc.2006, pp. 1-28. [1] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.

[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, “Implementation Issues in Spectrum Sensing for Cognitive Radios,” Signals, Systs .and comput., vol.1, no. 38, pp. 772-776, Nov. 2004.

[4] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.

[5] J. Mitola III and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.

[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 International Conference on 11-15 May 2003

[10] J. G. Proakis, Digital communications, fourth ed. McGraw-Hill, 2001.




[11] A. B. Idris, R. F. B. Rahim and D. B. M. Ali, “The Effect of Additive White Gaussian Noise and Multipath Rayleigh Fading on BER Statistic in Digital Cellular Network,” RF and Microwave, pp. 97- 100, Sep. 2006.

[12] Jigar Kumar,Arpita Patel and Sagar Patel, “Multiuser-MIMO Broadcast Channel techniques”,International Research Journal of Engineering and Technology,vol. 3,no. 2, pp. 1587-1590, Feb. 2016.

[13] Sagar Patel, DevangkumarPatil andDhvanit Mehta, “Performance of 8×1 COSTBC MIMO System under Different Transmit Antenna Selection Scheme with Reduced feedback bit”,International Journal of Wireless Communication,vol. 7,no. 10, pp. 307-311, Oct. 2015.

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, especially in cognitive 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 Electronics and Communication Engineering from, Chandubhai S. Patel Institute of Technology changa, His research areas are wireless communication engineering, especially in cognitive radio. He is presently studying in the department of EC engineering at Charotar University of Science and Technology Changa Gujarat India since July 2013." “

“Sagar Patel did his B.E. in Electronics and Communication Engineering from North Maharashtra Uni. His research areas are wireless communication engineering, especially in MIMO - STBC. He is presently serving in the department of EC engineering at Charotar University of Science and Technology Changa Gujarat 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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spectrum sensing with energy detection in cognitive radio ... · “cognitive radio is a radio of...

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