Hybrid Spectrum Sensing Method for Cognitive Radio

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2683~2695
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2683-2695  2683
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Hybrid Spectrum Sensing Method for Cognitive Radio
A. S. Khobragade1
, R. D. Raut2
1
Department of Electronics Engineering, Nagpur University, India
2
Shri Ramdeobaba College of Engineering and Management Nagpur University, India
Article Info ABSTRACT
Article history:
Received Mar 23, 2017
Revised May 15, 2017
Accepted Jul 11, 2017
With exponential rise in the internet applications and wireless
communications, higher and efficient data transfer rates are required. Hence
proper and effective spectrum is the need of the hour, As spectrum demand
increases there are limited number of bands available to send and receive the
data. Optimizing the use of these bands efficiently is one of the tedious tasks.
Various techniques are used to send the data at same time, but for that we
have to know which bands are free before sending the data. For this purpose
various spectrum sensing approaches came with variety of solutions. In this
paper the sensing problem is tackled with the use of hybrid spectrum sensing
method, This new networking paradox uses the Centralized concept of
spectrum sensing and creates one of the most trusted spectrums sensing
mechanism. This proposed technique is simulated using MATLAB
software.This paper also provides comparative study of various spectrum
sensing methodologies.
Keywords:
Cognitive radio
Energy detector
GLRT
Hybrid sensing
Match filter detector
Robust estimator
Temperature based detector
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Awani S. Khobragade,
Department of Electronics Engineering,
Nagpur University,
42 Shukhsawarup Apartment, Nelco society, Subhashnagar, Nagpur,
Maharashtra 440022, India.
Email: awanigaidhane@yahoo.co.in
1. INTRODUCTION
Due to increase in wireless devices and applications, the available electromagnetic radio spectrum is
getting crowded day by day. It has been also noticed that, because of the static allocation policy of the
spectrum, the allocated spectrum is under-utilized. The unutilized part of the spectrum results in ‘Spectrum
holes’ or ‘White Spaces’.The limited available spectrum and the inefficiency in the spectrum usage
necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically[1]-[4].
To deploy new services or to enhance existing ones ie. Dynamic spectrum access is proposed to solve these
current spectrum inefficiency problems as shown in Figure 1.
Cognitive Radio (CR) using spectrum sensing technique can be used to solve the issues of spectrum
underutilization. In order to supply extremely reliable communication for all secondary users of the network.
CR is defined as "An intelligent wireless communication system that provides more efficient communication
by allowing secondary users to utilize the unused spectrum segments". From the definition, the main
functionalities required for the cognitive radio systems can be summarised as follows:
1. Spectrum Sensing: Sensing and monitoring the available spectrum bands reliably to detect the
unused portion of the primary user spectrum.
2. Spectrum Decision: The cognitive radio can allocate a channel based on the regularly policies and
spectrum sensing results.

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3. Spectrum Sharing: Coordination among multiple cognitive radio users is needed to prevent the
colliding in the available portion of the spectrum.
4. Spectrum Mobility: The cognitive radio user is regarded as visitor to the primary user spectrum,
and a reliable communication cannot be sustained for a long time if the primary user uses the
licensed spectrum frequently. Therefore, the cognitive radio system should support mobility to
continue the communication in other vacant bands.
Figure 1. Illustration of spectrum white space
Figure 2.shows cognitive radio cycle consists of all the above functions, the most crucial task is to
establish cognitive radio network using spectrum sensing method. The various spectrum sensing techniques
broadly classified as:
a. Transmitter based detection
b. Cooperative based detection
c. Interference based detection.In this paper we discussed about hybrid spectrum sensing method
which blend five different detecting methods of spectrum sensing.
To view its performance in hybrid manner. This paper is organized as follows: Section 2 Describes
the hypothesis test on analytical model and system parameters Section 3 Explains Proposed system, Section 4
Implementation of Hybrid Spectrum Sensing, Section 5 Simulation Result and Discussion, Section 6 Final
Conclusion, Section 7 references.
Figure 2. Cognitive Radio cycle
2. LITERATURE REVIEW
Spectrum sensing is the capability of the CR to allocate the best available ideal licensed spectrum to
the secondary users (SUs) keeping in mind their Quality of services (QoS) but without disturbing the primary

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or licensed users.One of the most challenging tasks in CR systems is spectrum sensing as it requires high
accuracy and low complexity for dynamic spectrum access. In figure3 we observe that the spectrum sensing
field, the spectrum sensing performance metric is measured between selectivity and sensitivity which are
expressed in terms of levels of detection and false alarm probability. Higher detection probability ensures
better primary users (PUs) protection and lower false alarm probability ensures more chances of channel
utilization by secondary users(SUs). A false alarm probability of 10% and a detection probability of 90%
have been regarded as the target requirements for all the sensing algorithms.
Figure 3. Hybrid Spectrum sensing structure in a cognitive radio network
2.1. Analytical Model (Two hypothesis)
Spectrum sensing is one of the most important task in the cognitive radio network. It is the first step
for communication to take place that needs to be performed. Spectrum sensing is popularly known as the
hypothesis test because it is thought as an identification processes. The Spectrum sensing is a test of the
following two hypotheses:
H0:x(t)=n(t)
H1:x(t)=s(t)+n(t)
s (t)-The signal that is transmitted by the PUs.
x (t)-The signal which is received by the SUs.
n(t)-Known as the AWGN (Additive White Gaussian noise).
H0 hypothesis is used to tell that no primary signals are present in the spectrum and only noise is present.
And hence it is allotted to the secondary users. H1 hypothesis is used to tell that primary signals are present
in the spectrum along with the noise. And hence it is not allotted to the secondary users else there will be
harmful interference to the primary users.
2.2. System Parameters
Our fundamental objective of spectrum sensing is to use the system for decision making process to
check the availability of the spectrum holes, record the data and then carry out simulation to analyze the
stored data:
2.2.1. Probability of false alarm (Pfa):
Probability of false alarm occurs when no primary signals are present in the spectrum but we get the
idea that they are present and hence do not allocate bands to the SUs. It occurs when only noise is present in
the channel and energy of noise level exceed the predefined threshold value and hence the presence of
primary user is detected by the decision making device. This is false representation and should be minimized.
(1)

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2.2.2. Probability of missed detection (Pm):
Probability of missed detection occurs when primary signals are present in the spectrum but we get
the idea that they are not present and hence spectrum is allocated to other SUs. This causes interference to the
primary users. It happens when a signal is present in the channel and energy of signal present do not exceed
the predefined threshold value and hence the presence of primary users is not detected by the decision
making device. This is miss condition and hence should be minimized. Figure 4 shows the trade-off
between Pm and Pfa with respect to the threshold value:
PM= 1 – PD (2)
(3)
Figure 4. PDFs for Hypothesis Test Model
In the above Figure 4, we observe that threshold value decrease with the decrease probability of missed
detection would increase the probability of false alarm and increasing the threshold value to decrease the
probability of false alarm would increase the probability of missed detection. Since both are unwanted and
both can’t be decreased simultaneously, hence trade-off between these two parameters is done and the
threshold is set accordingly.
3. PROPOSED SYSTEM
To efficiently sense the spectrum, authors uses five spectrum sensing techniques and club them
together to create a hybrid spectrum sensing method. This hybrid method is summarized as follows: Hybrid
spectrum sensing method is based upon Centralized Coordination concept in which, an infrastructure
deployment is thought for the CR users. Once CR detects the presence of a primary transmitter, it informs CR
controller which can be a wired immobile device. The CR controller informs all the CR users in its range
using broadcast control message.
3.1. Centralized schemes can be further classified according to their level of cooperation as:
a. Partially Cooperative Scheme where network nodes cooperate only in sensing the channel. CR users
independently detect the channel and inform the CR controller about it.
b. Totally Cooperative Schemes where nodes cooperate in relaying each other’s information in
addition to cooperative sensing channel.
The proposed system consists of five spectrum sensing methods :
a. Match Filter detector,
b. Energy detector,
c. GLRT,
d. Robust Estimator Correlator and
e. Temperature based detector.

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These methods are explained in brief below the hybrid sensing algorithm. All these methods have
their special functions to detect the spectrum whether it is free or occupied. To perform the hybrid spectrum
sensing method the system will take the input as the received signal and will test this received signal on all
the five detector methods , then compared the outputs with the threshold value and then finally output is
declared whether the band is free or occupied.
The Algorithm is as follows:
Step 1: Take input signal.
Step 2: Give input to the Match Filter method.
Step 3: record the output.
Step 4: Repeat step 2 and step 3 for all the other 4 methods.
Step 5: if number of true results recorded divide by 5 is greater than 0.66.
Then return true
Else return False.
3.2. The implemented sub-methods are explained as follows:
3.2.1. Match Filter
The MFB (Matched Filter Bound) is a versatile method to analysis the theoretically optimal
performance of a wireless transmission system in a time-frequency variant fading channel. The effectiveness
of a digital communication systems in the presence of interference is measured by the rate at which errors in
the reception of the information bits are made. This is referred to as the bit error rate (BER). If the
interference is assumed to be Gaussian noise, then BER is a minimum in any system in which the signal-to-
noise ratio of the individual bits is a maximum. Theoretical analysis has shown that if the signal-to-noise
ratio is optimized for white Gaussian noise interference, then the receiver is implemented as a “matched
filter” [7]. The characteristics of matched filters can be designated by either a frequency response function or
a time response function, both are related to each other by a Fourier transform operation.
In the frequency domain, the matched-filter transfer function H(f) is the complex conjugate
function of the spectrum of the signal Td be processed in an optimum fashion. Thus, in general terms:
H(f)=2K/NoS*(f)e-jwTb
(4)
where S*(f) is the spectrum of the input signal s(t), and Td is a delay constant required to matched filter
physically realizable. The normalizing factor, ‘K’ and the delay constant are generally ignored in formulating
the underlying significant relationship, usually expressed as :
H(f) = S*(f). (5)
The corresponding time domain relationship between the signal to be operated upon and the matched filter is
obtained from the inverse Fourier transform of H(f). This leads to the result that the impulse response of the
matched filter is a replica of the time inverse of the known signal function.
h(t) =KS(Tb-T) (6)
Figure 5. Matched Filter Detector
A general representation for a matched filter is illustrated in Figure 5 The filter input x(t) consists of
a pulse signal s(t) corrupted by additive channel noise w(t), as shown in Figure .where Tb is an arbitrary
observation interval. The pulse signal s(t) may represent a binary symbol 1 or 0 in a digital communication
system. The w(t) is the sample function of a white noise of zero mean and power spectral density No/2. The

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source of uncertainty lies in the noise w(t). The function of the receiver is to detect the pulse signal s(t) in an
optimum manner, gives the received signal x(t).To satisfy this requirement, we have to optimize the design of
the filter so as to minimize the effects of noise at the filter output in some statistical sense, and thereby
enhance the detection of the pulse signal s(t).Since the filter is linear, the resulting output y(t) may be
expressed as:
y(t)=s(t)+w(t) (7)
where s(t) and w(t) are produced by the signal and noise components of the input x(t), respectively.
A simple way of describing the requirement that the output signal component s(t) be considerably
greater than the output noise component w(t) is to have the filter make the instantaneous power in the output
signal s(t), measured at time t = Tb, as large as possible compared with the average power of the output noise
n(t). This is equivalent to maximizing the peak pulse signal-to-noise ratio, defined as
η=|s(t)|2
/E|w(t)| (8)
Finally, the structure of the receiver used to perform decision-making process is shown in Figure 5.
It consists of a match filter followed by a sampler, and then finally a decision device. The filter is matched to
a Triangular pulse of amplitude A and duration Tb, exploiting the bit-timing information available to the
receiver. The resulting matched filter output is sampled at the end of each signaling interval. The presence of
channel noise w(t) adds randomness to the matched filter output. The sample value y is compared to a preset
threshold A in the decision device. If the threshold is exceeded, the receiver makes a decision in favor of
symbol 1; if not, a decision is made in favor of symbol 0.
3.2.2. Energy Detector
Conventional Energy detector is a simple detector. The detector computes the energy of the received
signal and compares it to certain threshold value to decide whether the desired signal is present or not. When
the primary user signal is unknown or the receiver cannot gather sufficient information about the primary
user signal, the energy detection method is used. This method is optimal for detecting any unknown zero-
mean constellation signals and can be applied to CR (Cognitive Radio) is the new intelligent wireless
communication technology to solve the inefficiency of a fixed spectrum assignment policy [6].
Generally, the performance of spectrum sensing depends majorly on the settings of the detection
threshold. Adopting a fixed threshold to distinguish the primary user from noise is one of the most
conventional spectrum sensing methods [9]. But with the fixed threshold setting method, it is difficult to
guarantee the detection probability and false alarm probability, especially with the Fluctuating noise power.
Unlike the conventional method of fixed threshold sensing algorithm, recent work considers an adaptive
threshold. According to the SNR, sensing time or transmit power the adaptive threshold dynamically adjust
the SU. Study shows the design of sensing duration to maximize the energy efficiency for SUs with
cooperative sensing in cognitive radio networks.
Figure 6.Frequency domain representation of Energy Detector
The energy detection method calculates the energy of the input signal and compares it with a
Threshold energy value. The signal is said to be present at a particular frequency if the energy of the signal
exceeds the Energy level of the threshold. The threshold value is chosen so as to control the parameters such
as probability of false alarm and probability of detection. The threshold voltage can be calculated from:

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(9)
3.2.3. GLRT (Generalized Likelihood Ratio Test):
The model of GLRT based spectrum sensing shows reduction in probability of false alarm versus
the probability of detection for different input energy levels and hence the energy shall also be a parameter in
deciding the effectiveness of cognitive systems besides their optimization techniques for energy efficiency.
For multipath fading the GLRT is considered as the best method in demodulator domain for channel
estimation and data detection. It is regarded as blind because the channel fading coefficients are unknown to
both the receiver and transmitter. The GLRT that is the generalized likelihood ratio test is considered the best
decision maker for this work. The GLRT Criterion is as follows: Firstly we give two assumptions. Both
transmitter and receiver knows the memory order of the channel ν. Secondly none of them has the knowledge
about channel fading λ.
Figure 7. Principle of the GLRT Detector
The received signal can be expressed in the form
y(n) =θ Acos (2π fc n +φ )+ w(n);
n = 0,1,..., N −1, θ = 0 or 1
where A is the amplitude,
φ is the phase, and
fc is the carrier frequency.
θ = 0 or1, represents the primary signal is absent or present respectively, the test statistic as follows
T(y)=P(y(n);A,φ,H1)/P(y(n);H0)=λ (10)
where p(y(n);H1) and p(y(n);H0) are the PDF of the received signal under each hypothesis.
While A and φ represent the maximum likelihood estimation (MLE) of the amplitude and the phase
respectively.The likelihood function under H0 in the condition of the unknown parameter A and φ is
P(y(n);H0)=1/(2Πσ2
w)N/2
exp[-1/2σ2
wΣN-1
n=0y2
(n)] (11)
If the signal is present, the likelihood function under H1 can be expressed as
P(y(n);A,φ,H1)=1//(2Πσ2
w)N/2
exp(-1//(2Πσ2
w)ΣN-1
=0(y(n)-Acos(2Πfcn+φ))2
) (12)
Putting above two Equations in test statistics ,we get
TG(y)=1/N[[ΣN-1
n=0y(n)cos2Πfcn]2
+[ΣN-1
n=0y(n)sin2Πfcn]2
]˃˂H1H0λG (13)
3.2.4. Robust Estimator Correlator:
The more we know a priori how a system behaves, the better we can optimize the performance.
Exploiting prior knowledge of a system has proven to be a source of performance improvement in many

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scenarios. However, if this knowledge is relied upon without taking into consideration the possibility of
errors or deviations the result can be degraded. In this method , we provide robust designs for the signals to
be emitted from a multi-antenna array by taking into account uncertainties in the system parameters. By
characterizing the possible parameter uncertainties or errors, the design can combat against severe
performance degradation. In general design problems, there exists an objective function f(Dv, Sp) where
design variables Dv should be chosen such that f(Dv, Sp), for given system parameters Sp, is optimized;
Moreover a set of constraints over Dv, described by the set Dv, is also satisfied. Mathematically, 1min Dv
f(Dv, Sp) s.t. Dv ∈ Dv. (1.1) The performance degradation occurs when a physical phenomenon is described
by an erroneous model with (possibly) ) uncertain system parameters. Time variations are also important
causes of errors. Even if one estimates system characteristics perfectly, these characteristics will change over
time which make the estimates unreliable. As a result, signal processing or control schemes are devised under
incorporating such uncertainties. In practice, some levels of loss are inevitable, where the performance of a
system is evaluated under real-world assumptions, that is using real parameters which essentially fall away
from prior knowledge based on which the whole processes and designs have taken place [10]-[12]. Let the
MIMO base-band system model between the primary user base-station and the secondary user, corresponding
to the kth symbol transmission, be represented as,
y(k)=Hx(k)+η(k) (14)
where y(k)ƐCNr*1
, x(k) Ɛ CNr*1
are the received and transmitted temporally independent and identically
distributed (IID) zero-mean Gaussian signal vectors respectively, with the Gaussian signal covariance matrix
defined as:
RSƐCNr*Nr
(15)
Rs=E{s(k)sH
(k)}=H.E{x(k)xH
(k)}HH
(16)
3.3. Robust Test Static Detector
The test statistic corresponding to RTSD for spectrum sensing in MIMO cognitive radio scenarios
can be equivalently given as,
TRTSD=Σk=1yH
(k)R-1
ηy(k)–fRTSD* (17)
Where denote the optimal value of the objective function for the optimization framework.
3.4. Robust Estimator Correlator Detector:
The optimization framework in, the test statistic in can be equivalently formulated as,
TRECD=ΣK
k=1yH
(k)R-1
ηy(k)-f*RECD (18)
Where f*RECD denote the optimal value of the objective Function The above test statistic yields a robust
decision rule for primary user detection in MIMO cognitive radio networks.
3.5. Interference Based Detection:
Unlike the primary receiver detection, the basic idea behind the interference temperature
management is to set up an upper interference limit for given frequency band in specific geographic location
such that the CR users are not allowed to cause harmful interference while using the specific band in specific
area. Typically, CR user transmitters control their interference by regulating their transmission power (their
out of band emissions) based on their locations with respect to primary users. This method basically
concentrates on measuring interference at the receiver .The operating principle of this method is like an
UWB technology where the CR users are allowed to coexist and transmit simultaneously with primary users
using low transmit power that is restricted by the interference temperature level so as not to cause harmful
interference to primary users.

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Figure 8. Interference temperature model
Here CR users do not perform spectrum sensing for spectrum opportunities and can transmit right
way with specified preset power mask. However, the CR users can not transmit their data with higher power
even if the licensed system is completely idle since they are not allowed to transmit with higher than the
preset power to limit the interference at primary users. [13]-[14] It is noted that the CR users in this method
are required to know the location and corresponding upper level of allowed transmit power levels. Otherwise
they will interfere with the primary user transmissions .The next section will show the implementation of the
proposed system.
4. IMPLEMENTATION
To enhance the spectrum sensing efficiency a hybrid spectrum sensing technique is
implemented [15]-[16]. In this proposed technique all five detectors are arranged in a cooperative manner.
a. Match Filter Detector.
b. Energy Detector
c. GLRT.
d. Robust Estimator Correlator.
e. Temperature Based Detector.
To detect whether band is vacant or occupied single frequency band is sensed by all the above
methods and the result of each method is then sent to the function block where the results are compare with
the fixed threshold value and a final output is determine. The flow of the system is as follows:
Figure 9. Flow of Proposed System
In the above Figure9, the system is provided with a spectrum band as the input, to check whether it
is free or occupied [17]-[18]. The input module sends the information of the band to the spectrum sensing
module. In this module the system will test the individual bands of the spectrum. The same input is provided

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to all the five methods of the spectrum sensing block. That is if the channel 2 is to be checked all the sub-
block will receive the channel 2 credentials only. Once all the sub-blocks process the channel status and
provide the output, then this output is then taken to the functional block. In the functional block the output is
checked. If the output of majority of sub-blocks takes either of the side that the channel is free or not then the
final output will be displayed [19][24].
For example: Let us assume that the outputs from the sub blocks are as follows: M=1, E=1, G=1,
R=0, T=1. From the output we observed that 4 detectors say that the band is free out of the 5 detectors
taken. This result is then sent to the Hybrid function block. In the function module the system will check for
the majority, if the majority of the output is true and is greater than the threshold of 66%, in our case the
channel is free with a chance of 80% since 4 methods say channel is free out of five methods. Therefore the
final output is say’s that band is vacant. In this way multiple bands can be tested. Further in the next section
results of the proposed technique are shown.
5. SIMULATION RESULTS
In order to compare the performance of different spectrum sensing methods two basic tasks are
considered a. To determine which bands are unoccupied and b. Best method can be determine for assigning
secondary users to the unoccupied spectrum without interfering with the primary users [21]-[22]. Using
MATLAB simulation of above detectors we have experimentally check the unoccupied band. The purpose of
this paper is to aggregate different spectrum sensing methods and compares these methods based on a variety
of performance metrics. Such performance metrics include detection accuracy, complexity, robustness,
flexibility of design choices, RF spectrum used and execution time of each system. In the below table
spectrum sensing algorithms such as energy detector, matched filtering, GLRT, robust Estimator correlator
and a hybrid technique are analyzed and compared.
As with experimental practices, the best spectrum decision method depends on the application.
Some variables to be considered are:
a. Expected SNR values.
b. Computational and implementation complexity.
c. Required reliability (express in terms of probability of detection, probability of false alarm).
d. Amount of information that the receiver knows about the primary user’s transmitted signal.
e. Execution time.
f. Flexibility in primary user’s transmitted signal.
Table 1. Performance comparison summary of various spectrum sensing techniques. The choice of a
particular method purely depends on the application and environment in which it will be used. From the
Figure 10(a), we observed that the output from the sub blocks are as follows: M=1, E=1, G=1, R=1, T=1.
Hence, the analysis concludes that all 5 detectors say that the band is free. This result is then sent to the
function block. In the function module the system will check for the majority, if the majority of the output is
true and is greater than the threshold of 66% then, the channel is free with a chance of 100%.Since 5 methods
say’s that band is vacant. Therefore the final output is displayed as Band is free. In this way multiple
channels can be tested. After certain time interval if we again check the same channel we observe that the
result of the proposed technique is different which depends on the status of primary user.
Figure 10(a).Output of Hybrid System in terms of slot status.

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Table 1. Summarizes all of the results presented throughout research
Figure 10(b).Output of Hybrid System in terms of slot status.

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From the Figure 10(b), we observed that the output from the sub blocks are as follows: M=1, E=0, G=0, R=0,
T=0. Hence, we observed that only 1 detector say that the band is free out of the 5 detectors. This result is
then sent to the function block. In the function module the system will check for the majority, if the majority
of the output is true and is greater than the threshold of 66%, then the band is free with a chance of 20 %
since only 1 method say that channel is free out of five methods. Therefore the final output is that the slot is
occupied by the primary user. In this way multiple channels can be tested. Further the final conclusion is
drawn in the next section.
6. CONCLUSION
Spectrum utilization can be improved significantly by allowing secondary users to utilize a licensed
bands when the primary users (PU) are absent. Cognitive radio (CR), as an agile radio technology, has been
proposed to promote the efficient use of the spectrum. Considering the challenges raised by cognitive radios,
the use of spectrum sensing method appears as a crucial need to achieve satisfactory results in terms of
efficient use of available spectrum and limited interference with the licensed primary users. To overcome
these challenges we have implemented hybrid spectrum sensing method.
As we have already described in this paper, the implementation of the hybrid spectrum sensing
algorithm for cognitive radio network (CRN). It requires the involvement and interaction of many advanced
techniques, which includes cooperative spectrum sensing, interference management, cognitive radio
reconfiguration management, and distributed spectrum sensing communications. Further for efficient
utilization of limited radio frequency spectrum, the method used should identify the interference, So that the
primary user will not suffer from CR system to utilize their licensed spectrum. Agility improvement of
cooperative networks reduces detection time compared to uncoordinated networks. It also provides fast,
reliable operation with proper identification of the system interferences. The throughput of secondary nodes
can be improved by increasing the spatial diversity and spectrum diversity. To demonstrate the feasibility and
performance of hybrid method, simulation is done using MATLAB Software. It is a totally new research
direction for CRNs and interesting future research topics for discussed.
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BIOGRAPHIES OF AUTHORS
Awani S. Khobragade has completed her bachelor‘s degree in Electronics Engineering, from
Shri Ramdeobaba Kamla Nehru Engineering College, Maharashtra, India. She Completed her
Masters in Electronics Engineering from G.H.Raisoni College of Engineering Maharashtra,
India in 2009. Presently Pursuing Ph.D in Electronics. Having an experience of 12 years, she is
currently working as a Assistant Professor in Electronics Department of Priyadarshini college
of Engineering Maharashtra, India. Her research interests include wireless communication,
mobile communication and cognitive radio.
Dr. Rajeshree D. Raut, a young and dynamic Academician is Ph. D in Electronics and her
research has made significant contribution in the field of Error Control Coding in Cognitive
Environment. Having an experience of 16 years, she currently is actively associated with
Research & Development Cell at Shri Ramdeobaba College of Engineering & Management and
has worked as the Associate Dean R & D. Dr Raut has been conferred the Prestigious National
Award for Best Engineering Teacher, by ISTE. She was awarded the IDEAL TEACHER of
RTM, Nagpur University, in the year 2011. Her work as a resource person includes Book on
‘Error Control Coding; For Performance Improvement in Cognitive Radio’, published by
Verlag Academic Publishing, Chaired the Session for National & International Conferences in
India & abroad, Reviewer, Advisory & Editorial Board Member for many International Journals
including IEEE Communication Letters, Elesvier, InderScience & Hindawi Publications. She has
on her accord more than 50 International Journal and conference papers, organized by countries
like USA, Italy, Mexico, UK and Malaysia. She has delivered more than 30 expert talks in the
field, including one at Mexico. At this young age she has worked as LEC committee expert and
an RTMNU appointed expert in Selection Committees constituted for the appointment of Ass.
Prof/ Lecturer. She is a registered guide at Nagpur , Amravati & Vignan University , Guntur,
with a total of 8 research scholars working under her guidance.

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