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Proceedings of IEEE International Conference on Smart Structures and Systems-ICSSS’20
2020
Department of ECE, Saveetha Engineering College, Chennai. 268
A Study and Simulation of Spectrum
Sensing Schemes for Cognitive Radio
Networks
G.T. Bharathy1 Dr.V.Rajendran2
Research Scholar
Department of ECE
Vels Institute of Science, Technology & Advanced Studies
(VISTAS)
Professor and Director Research
Department of ECE
Vels Institute of Science, Technology & Advanced Studies
(VISTAS)
Chennai, India Chennai, India
bharathy@jerusalemengg.ac.in
T.Tamilselvi3 Dr.M.Meena4
Associate Professor, Department of ECE Assistant Professor, Department of ECE
Vels Institute of Science, Technology & Advanced Studies
(VISTAS)
Chennai, India
Vels Institute of Science, Technology & Advanced Studies
(VISTAS)
Chennai, India
Abstract - The under - deployment dilemma of the
allocated radio spectrum has made the Cognitive
Radio (CR) communications to evolve as a
trustworthy and valuable solution. Spectrum sensing
is one of the schema to accomplish the essential
Quality of Service (QoS). Spectrum sensing offers the
critical data to facilitate the interweave
communications which does not authorize the
primary and secondary to utilize the medium
simultaneously. Cognitive Radio Networks (CRNs) in
addition include the suppleness to amend its own
transmission parameters in accordance with the
requirements of multimedia services or applications.
This paper concentrates on various schemes for
spectrum sensing such as Energy Based Detection
scheme, Autocorrelation Based Detection scheme,
Euclidean Distance Based Detection scheme, Wavelet
Based Detection scheme, Matched Filter Detection
scheme for the Cognitive Radio Networks.
Index Terms -
Cognitive Radio, Cooperative Spectrum
Sensing, Energy Detection, Autocorrelation Detection,
Euclidean Distance Detection, Wavelet Detection,
Matched Filter Detection.
I. OVERVIEW OF COGNITIVE NETWORKS
A. Dynamic Spectrum Access:
The Conventional Wireless Networks uses
the static spectrum access scheme and it is
interchanged by the Variable Spectrum Access
also called as Dynamic Spectrum Management
(DSM) [1], giving raise to the concept of cognitive
radio networks. The spectrum sensing operation is
made reliable and smooths [1] [2], by the
suppleness of the spectrum sharing which helps the
cognitive users to utilize the holes in the spectrum
also named as white spaces in the licensed
spectrum. Cooperation from licensed users is also
a key which evades the interference. The factors
involved in DSA are spectrum sensing,
interference improvement and spectrum usage
analysis. The working principle of CRNs is
completely dependent on the cognitive cycle.
Spectrum sensing is conventionally characterized
as a measure of the spectral contents, or measure
of the radio frequency energy in a given spectrum;
The cognitive radio is a further universal concept
that engages obtaining the spectrum utilization
attributes across compound aspects such as time,
space, frequency, and code.
Figure 1: Diagram of Dynamic Spectrum Management
Department of ECE, Saveetha Engineering College, Chennai. 269
B. Cognitive Cycle:
The complete spectrum sensing and
spectrum allocation methods are classified into four
main categories represented as cognitive cycle.
Identifying the spectrum holes (white spaces)
available in the spectrum by using different spectrum
sensing techniques is the initial move in the cognitive
cycle. The second step is the spectrum management,
during which the interference provided to primary
users is set aside at a bare minimum to sustain the
secondary user communications in the licensed band.
The third step is the sharing of the spectrum which is
initially sensed & managed. In the scheme of
spectrum sharing, the spectrum holes (white spaces)
are utilized by using the following methods of the
white space utilizations techniques such as underlay,
overlay, or interweave method [3],[4]. The cognitive
cycle i.e, the spectrum sensing, spectrum
management, spectrum sharing and the mobility helps
in the efficient usage of the spectrum.
Figure 2: Block Diagram of Cognitive Cycle
II. SPECTRUM SENSING:
The spectrum sensing is classically carried
out by means of two statistical hypotheses H1 and H0
which specify the existence or nonexistence of
primary user signal in the licensed band [5], [20].
Generally spectrum sensing scheme (or method) are
related with two probabilities value: detection
probability value (Pd = P[H1 H1]) that represents the
probability of accurately detecting the existence of
primary signal when the signal is truly available and
probability of false alarm (Pf = P[H1 H0]) that
represents the probability of wrongly declaring the
existence of primary signal if the signal is truly
absent.
Larger numerical values of Pd and smaller
numeric values of Pf are forever preferable for SUs,
since larger Pd value guarantees the least likelihood
of intrusion to PU transmission and smaller Pf
numeric value offers enhanced likelihood of
throughput to be obtained by SUs. Hence the sensing
routine characteristics of a SU is controlled by the
values of Pd and Pf which in turn is reliant on the
characteristics of the licensed spectrum band.
Figure 3: Diagram Spectrum Sensing
III. REPRESENTATION OF SPECTRUM SENSING :
The spectrum sensing model is represented as:
(1)
where n =1….N, N is the total samples, y(n)
indicates the cognitive signal, s(n) represents the
primary user, w(n) indicates the additive white
Gaussian noise (AWGN) with zero mean and
variance δ2W and h designates the complex channel
gain of the sensing channel. H0 and H1 symbolize
the non availability and the availability of the
primary user signal respectively. The detection of
primary user signal is carried out with the help of
one of the spectrum sensing methods to take a
decision among the two hypotheses H0 and H1. The
detector output, which is otherwise named as the
test statistics, is finally compared with the value of
threshold for taking a sensing decision regarding
the availability of the main user signal.
Department of ECE, Saveetha Engineering College, Chennai. 270
Figure 4: Model of Spectrum Sensing
The sensing decision is performed as:
(2)
in which T represents the test statistics involved in the
detector and represents the threshold value of
sensing. When the main user signal is not present,
cognitive user will have a capability of right to use
the PU channel. The literature has proposed a large
number of sensing methods which are categorised as
two major classifications namely: cooperative scheme
of sensing and non-cooperative method of sensing [6]
as given in Figure 5.
Figure 5: Categories of Spectrum Sensing
In the non-cooperative sensing method,
which is otherwise named as local technique for
sensing, every SU requests for its own access and
do not consider the decisions taken by various SUs
involved. Since there is no transfer of information
or collaboration that exists among the various
secondary users which senses the identical band of
frequency, decision on the sensing of the spectrum
is executed locally [8]. But the non cooperative
scheme in turn it is prone to errors because of the
following parameters such as shadowing,
interferences due to fading, and the uncertainty in
the noise etc. These techniques are primarily
utilized if and only if there is a single sensing
terminal existing.
In the cooperative spectrum sensing
schema, the SUs work in partnership. This
collaboration among the varied SUs is categorised
as the following two schemas: one is centralized
and the other is distributed schema [7]. In the
distributed schema, SUs share its local
interpretation along with the sensing details.
Considering the details from various other
secondary users sensing the identical frequency
band, its own decision is made by every SU. This
schema does not necessitate any general network
for making the ultimate decision in which the
detection is completely inhibited by the secondary
users. However in the centralized schema, each
and every SUs transmit its sensing details to a vital
central entity, named as fusion center, as given in
Figure 6.
Figure 6: Representation of Centralized Cooperative Spectrum
Sensing
A. System Requirement Specifications:
The competence of the entire spectrum
sensing algorithm is calculated depending on the
subsequent important evaluation statistics: Detection,
probability, false alarm probability, misdetection
probability and the signal-to noise ratio. The
evaluation of the detection is calculated via an
AWGN channel with the help of MATLAB
simulation tool.
IV. SIMULATION SPECIFICATIONS:
MATLAB (R2014a), a sophisticated
language tool, is utilized to design the algorithm for
sensing the spectrum. The Primary User produces an
input which is Quadrature Phase Shift Keying
(QPSK) modulated. The secondary signal is received
through the channel where the modulated signal gets
added with the noise of the channel (AWGN).
Department of ECE, Saveetha Engineering College, Chennai. 271
The energy performance metric is evaluated by
comparing the signal that is received to a
predestined value, at a characterized false alarm
probability.
PU Signal
QPSK
Noise added in the channel
AWGN
Samples count/Fast Fourier
Transform Size
128, 256, 512,
1024, 2048
SNR
-25 dB to 0 dB
False Alarm Probability
0 to 1
Required Detection
Probability
0.9
Required False Alarm
Probability
0.1
Iteration Performed
10000
Table 1: Simulation Specifications
V. ANALYSIS OF SPECTRUM SENSING TECHNIQUES:
A CRN is defined as a network containing
the combination of many CR enhanced nodes
(secondary users) along with the licensed nodes
(primary users), in which the CR enhanced nodes
utilizes the communication of the licensed
spectrum bands by exploiting the spectrum holes
(White Spaces) available. The Sensing of spectrum
holes available in the licensed bands by the
cognitive users also called as secondary users
(SUs) is important for the accomplishment of CRN
and this terminology is named as Spectrum
Sensing. In CRN, SUs are enriched with CR
facilities such as frequency agility, adaptive
modulation which enables the CRN to dynamically
to deal with the performance of spectrum sensing.
Figure 7: Types of Spectrum Sensing Methods
A. Energy Detection Scheme:
The easiest sensing method, which do
not involve any data regarding the main signal
for its operation is the Energy detection scheme.
The detection is done by means of comparison
of the arrived signal energy to a predefined
value. The value of threshold is fixed depending
merely on the value of the power of the noise.
The decision metric of an energy detector is
evaluated using the squared magnitude value of
a Fast Fourier Transform which is averaged over
N as given in Figure 8. The output of the
detector is represented by
(3)
in which n=1…. N, N represents the total samples,
and y(n) represents the SU, and TED represents the
test metric. Therefore, the decision-based energy
detection is represented as:
(4)
in which represents the threshold value for
sensing and TED represents the received signal
energy from the SU. A Gaussian random signal
is used for the representation of the signal
received. Consequently, the evaluation metric,
TED, is represented in terms of Gaussian and
expressed as
(5)
in which δ2S represents the primary user signal
variance, δ2W represents the noise variance, with Ɲ
representing the distribution which is normal. In
the estimation metrics, the detection probability and
the false alarm probability with an additive white
Gaussian noise (AWGN) channel is represented as:
(6)
where Q(.) symbolizes the Q-function with λ
representing the value of threshold for sensing
[7]. The formula for both the metrics is written
as a function of signal to noise ratio (SNR) as
follows:
Department of ECE, Saveetha Engineering College, Chennai. 272
(7)
in which γ represents the SNR and λ represents
the average value of the threshold,
Consequently, the threshold value of sensing
which is based on the power of the noise is
represented for a target Pfd as [8]:
(8)
Figure 8: Block Diagram of Energy Detection Model
Every value of threshold denotes a pair of (Pd,
Pfd), representation said as the receiver operating
curve (ROC). ROC characterizes the plot
corresponding to accurate rate of detection which
is expressed as a function of the wrong rate of
detection with various values of threshold. Energy
detection is simple to employ and do not
necessitate any previous data regarding the PU
signal, that enables it to be used as a general
method. Nevertheless, this is extremely responsive
in the presence of noise in addition it do not
discriminate among the signal & noise if the power
of the signal is little. Additionally, the value of
threshold for sensing of energy detector is also a
significant parameter. If the detector does not
amend the threshold value appropriately, then its
capability of the spectrum sensing will be reduced.
The below given Matlab simulation
result shows that the results of energy detection for
different numbers of CRs over Additive White
Gaussian Noise channel for an SNR n = -10 dB. It
is observed that the false alarm probability is
increased to a great extent while the probability of
detection is increased.
Figure 9: Simulation result of Complementary ROC of
Cooperative Sensing Under AWGN Channel
B. Autocorrelation Based Detection Method:
Autocorrelation based sensing schema
depends on the autocorrelation coefficient value of
the received signal. It utilizes the available
autocorrelation characteristics in the transmitted
signal which will be available in the noise signal
[9].
The autocorrelation function for any
signal, s(t), is expressed as:
(9)
Department of ECE, Saveetha Engineering College, Chennai. 273
in which τ represents the time lag, t represents
time, and s* indicates the conjugate of the signal.
In the perspective of sensing of spectrum, the
superiority of sensing is altered by presence of the
noise level and the interpretation of the Gaussian
noise affected signals become difficult. In reality,
the autocorrelation of the uncorrelated white noise
contains a sharp spike at zero lag. Figure 10.
Figure 10: Diagram of White Noise Autocorrelation
Function
The transmitted signal which is a
concurrent signal; the zero lag along with the first
lag are extremely near each other as given in
Figure 11.
Figure 11: Diagram of Autocorrelation Function of the
Signal
Consequently, the signal's autocorrelation
is correlated at the same time as the autocorrelation
of the noise is uncorrelated as given in the Figure
10 & Figure 11. The strength of the signal become
larger as the measure of correlation is larger. As a
result, the spectrum sensing is executed by
utilizing the autocorrelation function so as to
identify the main user availability during the
existence of noisy signal as given in the Figure 12.
Figure 12: Block Diagram of Sensing Model Based on Autocorrelation
The decision on sensing is taken depending on
the awareness of the numerical allocation of the
autocorrelation function. The initial lag of the
value of autocorrelation is extremely tiny even
negative for a random noise, at the same time
during the existence of signal the
autocorrelation value at the first lag denotes a
considerable value [10]. The decision on sensing
is represented as:
(10)
C. Euclidean Distance Based Detection Method:
A novel scheme for sensing was
developed in [11] which is Euclidean distance
based detection. It is principally dependent on the
value of autocorrelation function of the signal from
the SU. The detector achieves by evaluating the
Euclidean distance among the autocorrelation
value of the signal with the line of reference [3].
The received signal's autocorrelation value can be
presented as
(11)
where RS,S(τ) represents the autocorrelation at
lag τ, s indicates the signal received, and N is
the total samples. The line of reference is
indicated by :
(12)
in which M synbolizes the lag counts of
autocorrelation which ranges between 0 ≤ t ≤ 𝑀
2 ,
R symbolizes the line of reference. [12]
The Euclidean distance D, is represented as:
(13)
It is defined as the difference calculated
among the line of reference long with the signal
autocorrelation [12]. The comparison of the
above metric is done with a predefined threshold
value for performing the sensing as given in the
Department of ECE, Saveetha Engineering College, Chennai. 274
Figure 13.
Figure 13: Block Diagram of Model of Sensing based on the
Euclidean Distance
The decision on sensing is represented as:
(14)
in which represents threshold value of the
sensing. The sensing based on Euclidean
distance is very much competent compared to
the autocorrelation based sensing with respect to
the detection success rate [13].
D. Wavelet Based Sensing Method:
The sensing based on Wavelet, which
is otherwise named as edge detection, depends
on the value of continuous wavelet transform,
that enables the calculation of the decomposed
signal coefficients using the basis function[3],
[13], [14]. It is represented by
(15)
where x(t) denotes the continuous wavelet
function[15].
Figure 14: Block Diagram of Wavelet Based Sensing Model
in which s denotes the parameter of translation, u
represents the parameter of scaling which is large,
and U,S(t) corresponds to the basis. The
investigation could be executed at frequency
corresponding to the factor s, at the time interval
corresponding to factor u [3]. The sensing
dependent on wavelet is carried out by evaluating
the wavelet transform of the continuous signal to
achieve the required PSD. The comparison of the
highest value of the PSD which is represented by
the edge, with the threshold value is done to make
decision regarding the spectrum possession as
given in the Figure 14.
The decision on sensing is represented as:
(16)
in which e indicates the wavelet edge and
represents the value of threshold for sensing.
The wavelet edge is utilized for taking
judgments on sensing since the density of the
power of any signal is represented by a single
spike at the desired frequency despite the fact
that it is represented by multiple spikes if noise
is included as given in the Figure 15 and 16.
Figure 15: Diagram of Power Spectral Density of Noiseless
Signal
Figure 16: Diagram of Power Spectral Density of Noisy
Signal
Department of ECE, Saveetha Engineering College, Chennai. 275
Figure 17: Simulation Result of Wavelet Based Detection
The above Matlab simulated result
shows the plot of wavelet based detection in
which frequencies with values zeros indicate the
spectrum holes and the frequencies with values
indicate the spectrum occupancy.
E. Matched Filter Detection Method:
It is one of the most favourable filters
that necessitate the former information of the
Primary User signals. This sensing scheme is the
finest preference under the situation where
certain knowledge regarding the Primary User
signal are already existing at the Secondary User
receiver. With an assumption that the Primary
User transmitter transmits a pilot data sequence
concurrently along with the data, the secondary
user gets the signal beside with the pilot data
sequence. The detection in Matched filter
scheme is executed by extrapolating the pilot
data xp, as given in the Figure 17 [16][17].
The test statistic is represented by:
(17)
in which xp indicates the main user signal, y
indicates the cognitive user signal, and TMFD
indicates the test metrics of the detector
depending on the matched filter. The ultimate
decision regarding the accessibility of the
spectrum is taken by comparing the test metrics
with the predetermined value.
Figure 18: Matched Filter Detection
(18)
Depending on the criterion of Neyman-Pearson,
the correlation among the probability of detection
and the false alarm is expressed as.
(19)
in which E represents the signal energy of PU,
is the threshold value of sensing, Q(.) [18]
denotes the Q- function, and δ2W represents the
variance of the noise. The sensing predetermined
value is written as function of the signal energy
of the PU and the variance of the noise as [19].
(20)
Figure 19: Simulation Result of Matched Filter
Detection
The above Matlab simulated plot
shows that for the proposed algorithm increase
in the value of probability of false alarm results
if the probability of detection increases in
comparison with the theoretical value.
Department of ECE, Saveetha Engineering College, Chennai. 276
Figure 20: Simulation of Signal-to-Noise Ratio versus
Probability of Detection with varying Sample
The above Matlab simulated result
shows that the increase in probability of
detection results as the SNR of the signal
increases. The simulation also shows that the
increase in the probability of detection is
achieved by increasing the number of samples.
Figure 21: Simulation of ROC for various SNR's
The above Matlab simulated plot shows
that as the SNR value reduces from -11 dB to -22
dB, the area under the curve is reduced.
Consequently, larger the SNR better will be the
probability of detection.
VI. SIMULATION RESULT ANALYSIS AND COMPARISON:
Metric
Energy
Detector
Auto Correlation
Based Detector
Euclidean
Distance Based
Detector
Wavelet Based
Detector
Matched Filter
Detector
Design Choices
Difficult to
choose
threshold for
decision
Finding Auto
correlation
Function
Evaluating
Euclidean
Distance
Computing the
Wavelet
Transform of the
signal
Transmission
characteristics can
be chosen to
improve accuracy
Complexity
Low
computational
and
implementation
complexity
Moderately
Complex
Less Complex
Moderately
Complex
Highly Complex
(Necessitates a
devoted Rx for
every primary
signal)
Toughness
* Do not need
the prior data of
Tx signal
* Needs
awareness of
noise power
Do not need any
Tx data at the Rx
Do not need any
Tx information at
Rx
Do not need any
Tx information at
the Rx
Need near perfect
Tx information at
Rx
Accuracy of
Detection
* Good results
at large SNRs
* meagre
results at small
SNRs
Good results at all
SNRs
Good results at all
SNRs
Good results at all
SNRs
* Excellent
performance at all
SNRs
* Poor
performance in
the absence of
prior knowledge
about the Tx
Table 2: Comparison of various Spectrum Sensing Schemes
Department of ECE, Saveetha Engineering College, Chennai. 277
Energy Detector
Matched Filter Detector
Probability
of False
Alarm
Probability
of Detection
Probability
of False
Alarm
Probability
of Missed
Detection
0.1
0.87
0.1
10-1
0.2
0.92
0.2
10-1.5
0.3
0.93
0.3
10-2
0.4
0.95
0.4
10-2.5
0.5
0.96
0.5
10-3
0.6
0.97
0.6
10-3.5
0.7
0.98
0.7
10-4
0.8
0.985
0.8
10-4.5
0.9
0.99
0.9
10-4.8
1
1
1
10-5
Table 3: Result Comparison for SNR = -10 dB
VII. CONCLUSION AND FUTURE WORK:
This paper presents a detailed analysis of
the working principle of different spectrum sensing
techniques such as Energy Detection,
Autocorrelation Detection, Euclidean Distance
Detection, Wavelet Detection, Matched Filter
Detection. It is concluded from the results that
Energy Detection is simple and the Matched Filter
Detection gives accurate result compared to other
spectrum sensing schemes.
The above mentioned work can be
extended with many other modulation schemes
with OFDM, CDMA techniques which may give
more efficient and accurate results for
implementing in 5G applications.
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BIOGRAPHY
Ms.G.T.Bharathy was born
in India in the year 1979. She
completed her B.E degree in
ECE in the year 2000 from
Madras University,
Easwari Engineering College,
Chennai, India and M.E
degree in the year 2005
Communication Systems in Anna University from Shri
Venkateshwara College of Engineering, Chennai, India. She
is currently a Research Scholar (part-time Ph.D.) at
Department of ECE, Vels Institute of Science, Technology &
Advanced Studies (VISTAS), Chennai and also working in
Department of ECE, Saveetha Engineering College, Chennai. 278
Jerusalem College of Engineering, Department of ECE,
Chennai as an Associate Professor. She is a member of IEEE
and also a life time member in ISTE. She has worked in the
Anand Institute of Higher Technology and Prince Shri
Venkateshwara Padmavathi College of Engineering, Chennai.
She is interested in research areas such as RF and Microwave
Circuits, Communication Systems and Wireless
Communication & Networks. She has published five papers
in Scopus Indexed Journal, one paper in Springer Scopus
Indexed Journal and one paper in IEEE Xplore Digital
Library and more than 20 papers in various other reputed
National and International Journals.
Corresponding author:
Email: bharathy@jerusalemengg.ac.in.
Dr. V. Rajendran received his MTech
in Physical Engineering from IISc,
Bangalore, India and received his PhD
degree on Electrical and Electronics
Engineering from Chiba University,
Japan in 1981 and 1993, respectively.
He is currently working as a professor
and Director Research,
Department of ECE in Vels Institute of Science and
Technology, Pallavaram, Chennai, India. He was awarded
MONBUSHO Fellowship, Japanese Govt. Fellowship
(1988–1989) through the Ministry of Human Resource and
Development, Govt. of India. He was elected twice as Vice
Chairman – Asia of Execution Board of Data Buoy Co-
operation Panel (DBCP) of Inter- Governmental
Oceanographic Commission (IOC)/World Meteorological
Organization (WMO) of UNSCO, in October 2008 and
September 2009, respectively. He was a Life fellow of
Ultrasonic Society of India, India (USI)in January 2001. He
was a Life fellow of Institution of Electronics and
Telecommunication Engineering (IETE), India, in January
2012. His area of interest includes cognitive radio and
software-defined radio communication, antennas and
propagation and wireless communication, under water
acoustic signal processing and under water wireless
networks. He has published 52 papers in web of science and
Scopus-indexed journal.
Email: director.ece@velsuniv.ac.in.
Ms.T.Tamilselvi was born
in the year 1978 in India.
She completed B.E
degree in ECE,
from Madras University
in the year 2000 from
Adhiparasakthi
Engineering College, India
and M.E. degree from College of Engineering, Guindy
(CEG Main Campus), Anna University, India in
Embedded System Technologies in the year 2006. She is
working as Associate Professor in the department of ECE,
Jerusalem College of Engineering, Chennai. She is a life
member in ISTE. she is interested in the research areas
such as VLSI, Embedded design and Wireless
Communication & Networks. She has published five
papers in Scopus Indexed Journal, one paper in Springer
Scopus Indexed Journal and two papers in IEEE Xplore
Digital Library and more than 20 papers in other National
and International Journals.
Email: tamilselvi@jerusalemengg.ac.in.
Dr. M. Meena received Ph.D degree in
ECE from Vels University. She
completed her M.E. degree in Applied
Electronics from Anna University. She
is currently working as an Assistant
Professor in Department of ECE at Vels
University. She is interested in research
areas such as Computer Networks,
Wireless Networking & Software
Defined Radio. She has published 11
papers in Scopus Indexed Journal.
Email: meena.se@velsuniv.ac.in.
