A RESEARCH ON NON COOPERATIVE HYBRID SPECTRUM SENSING TECHNIQUE

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International Journal of Electronics and Communication Engineering and Technology (IJECET)
Volume 8, Issue 1, January - February 2017, pp. 67–78, Article ID: IJECET_08_01_008
Available online at
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=8&IType=1
ISSN Print: 0976-6464 and ISSN Online: 0976-6472
© IAEME Publication
A RESEARCH ON NON COOPERATIVE HYBRID
SPECTRUM SENSING TECHNIQUE
Ramandeep Kaur
M-Tech Student, Department of Electronics & Communication
Chandigarh Engineering College Landran, Mohali, India
Sakshi Sharma
Assistant Professor, Department of Electronics & Communication
Chandigarh Engineering College Landran, Mohali, India
ABSTRACT
The research designed in this paper is to purpose and implement a Hybrid spectrum sensing
technique. As the utilization of wireless devices has been increased, there is a great demand for the
radio spectrum .Cognitive Radio is a technology which can sense the spectrum to make the efficient
use of resources of spectrum. Sensing of spectrum can be done by using matched filter, energy
detection, waveform based detection, cyclostationary feature. Hybrid model is implemented by
taking the assumptions for the distance and the SNR value, so it does not require unnecessary time
for sensing of every frequency band. Results are formulated on the bases of two parameters
probability of false detection and probability of correct detection. The proposed methodology has
been implemented in MATLAB and the results obtained are in the form of improvement in
Throughput, Energy consumption, Accuracy and improvement in Error.
The proposed model has been found efficient when compared to the other spectrum sensing
techniques. It has been proved the effective improvement in throughput is by 9.9135% .Thus the
results obtained are excellent and this will definitely help researcher for the future development of
Cognitive Radio.
Key words: Cognitive Radio (CR), Spectrum sensing, Cyclostationary feature detection, Energy
feature detection, Matched filter detection, Hybrid model, Primary user (PUs) and Secondary user
(SUs).
Cite this Article: Ramandeep Kaur and Sakshi Sharma, A Research on Non Cooperative Hybrid
Spectrum Sensing Technique, International Journal of Electronics and Communication
Engineering and Technology, 8(1), 2017, pp. 67–78.
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=8&IType=1
1. INTRODUCTION
1.1. Cognitive Radio
Cognitive Radio (CR) is one of the new developments which is being started in 1998 for the radio
communications. It is the solution for providing the unused frequency band to secondary users. Tasks of
CR are- sensing of spectrum, management of spectrum, mobility and the sharing of spectrum resources.

Ramandeep Kaur and Sakshi Sharma
The primary cognitive radio requirements are:
(a) Low interference to the licensed users.
(b) Adjustable data rate, adaptive transmit power, information security, and the limited cost.
1.2. Meaning of Commonly Used Terms
Primary user: These are the wireless devices who holds the primary license and has more priority or
rights for using the radio spectrum.
Secondary user: These are the wireless devices who do not holds the primary license they access the
spectrum of primary users.
Spectrum Sensing: It is defined as the capability of the Cognitive Radio for allocating the free (licensed
spectrum) to the secondary users.
False Detection: The chances or the probability of false alarm happens when we found primary signals
are not present in the spectrum but we get the idea that they are present somewhere, hence we do not
allocate it to other SUs.
Dynamic Spectrum Access: As the licensed users have their own licensed spectrum .The function of
dynamic spectrum access is to search for the spectrum holes to allow secondary users to use these
resources by reusing unused spectrum [9] .
2. TYPES OF COGNITIVE RADIO SPECTRUM SENSING
Cognitive Radio spectrum sensing [6] performed, falls under the two categories: First, is the Non-
cooperative spectrum sensing and the Second is Cooperative spectrum sensing.
2.1. Non-cooperative spectrum sensing
In Non cooperative sensing or in the transmitter detection, Cognitive Radio acts by its own for the
occupancy of spectrum. Energy detection, matched filter detection, cyclostationary detection falls under
this category.
2.2. Cooperative spectrum sensing
In this method of spectrum sensing, the sensing will be done by many radios within the network and
central station will collect the information from several radios in the network.
In this research, we will emphasize on the Non cooperative spectrum techniques.
3. NON COOPERATIVE SPECTRUM SENSING TECHNIQUES
The Non cooperative spectrum sensing techniques are
 Energy Detection
 Cyclostationary Detection
 Matched filter Detection
Literature analysis [1], made it clear out of the various spectrum sensing techniques the most familiar
ones are Energy based detection and the cyclostationary based detection, lots of work has already been
done on these techniques in the previous research .For Energy detection method, the accuracy of correct
detection is found to be less and the decreased SNR causes several problems. The disadvantage of the
Cyclostationary detection is high computational complexity and long sensing time. So in our proposed
work we are going to combine the advantages of above mentioned methods.

A Research on Non Cooperative Hybrid Spectrum Sensing Technique
4. OBJECTIVES OF THE WORK
 To study Energy feature detection and cyclostationary feature detection for sensing the spectrum in
Cognitive Radio.
 To purpose and implement a Hybrid spectrum sensing technique based on energy and cyclostationary
techniques.
 To test and compare the proposed results with the existing techniques.
5. HYBRID SPECTRUM SENSING
A possible way to obtain spectrum information with the improved parameters of minimum sensing time
and low computational complexity and reduction of error is to use hybrid sensing technique.
In our proposed work we have implemented Hybrid model which is combination of energy and
cyclostationary sensing approaches.
6. METHODOLOGY
In Hybrid model we have done a fusion of the energy detection technique and the cyclostationary detection
technique [1].
Figure 1 Flowchart of Methodology [1]

Figure 2 Formation of GUI and Deployation of nodes
6.1. Previous Work
In the previous research, first energy feature and energy status are checked if conditions are favourable
then the data is being transmitted , else it moves to the cyclostationary detection, and checks for the
favourable conditions and transmits data if the free band slot available there or wait for the next round of
the communication .
Step1: Initialization of the system.
Step2: For checking free band, energy detection is done.
Step3: In energy detection, we have done deployment of the number of nodes and source and sink nodes
are generated.
Step 4: The energy feature and energy status are checked if conditions are in favor of free band it will shift
and if it is not in favor it will not send.
CASES:
Case 1:
if(Ens_f_prb1>5)
if(Ens_s_prb1==0)
Case 2:
if(Ens_f_prb2>5)
if(Ens_s_prb2==0)
If energy feature and energy status both are in favor of private band 1 or private band 2 slot is free and
this is called true detection.
If energy feature is in favor but when we check for the energy status and it’s not in favor then it is
called false detection.
Private band 1 and the private band 2 not free check other bands. So check private band 1 and private
band 2 on the basis of cyclostationary feature.

Case 3:
if(Css_f_prb1>5)
if(Css_s_prb1==0)
Case 4:
if(Css_f_prb2>5)
if(Css_s_prb2==0)
If cyclostationary feature and cyclostationary status both are in favor of private band 1 or private band
2 slot is free and this is called true detection.
If cyclostationary feature is in favor but when we check cyclostationary status and it’s not in favor then
it is called false detection.
Otherwise private band 1 and private band 2 slot not free on the basis of cyclostationary feature and
status. Hence no free slot available so we cannot shift waits for the next round.
6.2. Proposed Work
In proposed research, we directly goes to the cyclostationary detection by taking assumptions. Here we do
not have to waste unnecessarily time while checking energy feature and energy status. If conditions are
favourable then the data is transmitted otherwise it moves to check another band and checks for free slot
and transmits data if free band slot available there else wait for next round of the communication .
Step 5: if Distance > 200 and SNR < 0.50 , SNR and the distance value are not in favour then , it will
directly moves to the cyclostationary detection.
Step6: In the cyclostationary based detection, first we have deployed the number of nodes and from this
source and sink nodes are being generated.
Case 1:
if(Css_f_prb1>5)
if(Css_s_prb1==0)
Case 2:
if(Css_f_prb2>5)
if(Css_s_prb2==0)
If cyclostationary feature and cyclostationary status both are in favor of private band 1 and private band
2 slot is free and this is called true detection.
Otherwise private band 1 and private band 2 slot not free on the basis of cyclostationary feature and
status. Hence no free slot available so we cannot shift.
Step7: if Distance<200 and SNR>0.50 or Distance>200 and SNR>0.50 or Distance<200 and SNR<0.50
then we use previous method of band shifting.
Case 3:
if(Ens_f_prb1>5)
if(Ens_s_prb1==0)
Case 4:
if(Ens_f_prb2>5)
if(Ens_s_prb2==0)
If energy feature and energy status both are in favor of private band 1 and private band 2 slot is free
and this is called true detection.

If the energy feature is in favor but when we check the energy status and it’s not in favor then it is
called false detection.
Otherwise private band 1 and the private band 2 slot not free on the basis of the energy feature and
therefore we check the energy status on the basis of the cyclostationary feature.
Case 5:
if(Css_f_prb1>5)
if(Css_s_prb1==0)
Case 6:
if(Css_f_prb2>5)
if(Css_s_prb2==0)
If cyclostationary feature and cyclostationary status both are in favor of private band 1 and private band
2 slot is free, this is called true detection.
Otherwise private band 1 and private band 2 slot is not free on basis of cyclostationary feature and
status. So no free slot available we cannot shift wait for the next round of communication.
Table 1 Simulation Parameters
7. CALCULATIONS
Rounds1:100
Variables for Calculation:
Det= 0; correct detection
FDet= 0; false detection
On the basis of cases formed before, the variables of false detection and true detection are incremented
Det=Det+1
FDet= FDet+1
ENERGY
E1(i)=energy(i)*rand(1)
E2(i)=energy(i)*rand(1)/2

THROUGHPUT
Throughput1=(E1)*10
Throughput2=(E2)*10
7.1. Probability Tables
(a) (b)
Table2 PREVIOUS TECHNIQUE (a) False detection (b) Correct detection
(a) (b)
Table 3 HYBRID TECHNIQUE (a) False detection (b) Correct detection

(a) (b)
Table 4 THROUGHPUT (a) Previous (b) Proposed
(a) (b)
Table 5 ENERGY (a) Previous (b) Proposed

7.2. Simulated graphs for probability of false and correct detection versus rounds
(a) (b)
Figure 3 PREVIOUS TECHNIQUE (a) False detection (b) Correct detection
(a) (b)
Figure 4 HYBRID TECHNIQUE (a) False detection (b) Correct detection

(a) (b)
Figure 5 COMPARISON GRAPH (a) Previous (b) Proposed
7.3. Simulated Graphs for Accuracy, Error, Energy Left and Throughput
Figure 6 Accuracy Figure 7 Error
Figure 8 Energy Left Figure 9 Throughput

Table 6 Comparison of Result Analysis for previous and proposed techniques
8. CONCLUSION
In this Research, low complexity detector known as the hybrid model is implemented that is based on
combination of two well-known spectrum sensing methods-energy and cyclostationary detection. This
model is proposed to overcome flaws of energy detection and the cyclostationary detection. The results are
obtained by plotting the graphs and simulation is done by using Matlab. With the proposed work
improvement in the parameters like Throughput, accuracy, error and energy consumed has been done.
Simulation results show that by using proposed model, the throughput is greatly improved with tolerable
interference. This will help in increasing the efficiency of spectrum utilization without wasting
unnecessary time.
REFERENCES
[1] Ramandeep Kaur, Sakshi Sharma, “A Review on Hybrid Spectrum Sensing based on Energy and
Cyclostationary Techniques”,International Journal of Innovations in Engineering and Technology , Vol.
7, Issue 3, October 2016, pg.307-310.
[2] F. F. Digham, M. S Alouini and M.K Simon, “On the energy detection of unknown signals over fading
channels”, in Proc. IEEE International Conference on Communication (ICC003), May 2003, pp. 3575-
3579.
[3] Yonghong Zeng, Ying Chang Liang, Anh Tuan Hoang, and Rui Zhang, A Review on Spectrum Sensing
for Cognitive Radio: Challenges and Solutions, EURASIP Journal on Advances in Signal Processing
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[4] Z. Gao, H. Zhu, S. Li, and S. Du. Security and Privacy of Collaborative Spectrum Sensing In Cognitive
Radio Networks, IEEE journal on Wireless Communications, Vol. 19, 2012, pp. 106-112,
[5] S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE J. Sel. Areas Commun.,
vol. 23, 2005 ,pp. 201–220.
[6] Verma, Pradeep Kumar, and Rajeshwar Lal Dua. Performance Analysis Of Energy Detection, Matched
Filter Detection & Cyclostationary Feature Detection Spectrum Sensing Techniques. Indian Streams
Research Journal 2.9 ,2012.
[7] Shahzad A, Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing
Techniques in Cognitive Radio Systems, Australian Journal of Basic and Applied Sciences,4(9) , INSInet
Publication 2015 pp: 4522- 531
[8] M. Gandetto and C. Regazzoni, Spectrum sensing: A distributed approach for cognitive terminals, IEEE
J. Sel. Areas Commun., vol.no. 3, pp. 546–557, Apr. 2007.

[9] Bansal A.,Mahajan R., Building Cognitive Radio System Using Matlab , International Journal of
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[10] Wenjing Y., Baoyu Z., A Two-Stage Spectrum Sensing Technique in Cognitive Radio Systems Based on
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[11] Ramandeep Kaur, Sakshi Sharma, “Analysis of Hybrid model based on Energy and Cyclostationary
Spectrum Sensing Techniques”, IJSER Research publication, Vol. 8, Issue 2, february 2017.
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A RESEARCH ON NON COOPERATIVE HYBRID SPECTRUM SENSING TECHNIQUE

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