Performance Evaluation of Hybrid Channel Sensing
Techniques based on Beam-Steering
Chinky Sharma
Research Scholar
Dept of Computer Science and Engineering,
College, Fatehgarh Sahib
chinkycse.cgc@gmail.com
ABSTRACT
The increasing demand of number of wireless devices has
put a lot of restrictions on the use of available radio spectrum
which is very limited and precious resource and this has
made it essential to address the spectrum scarcity problem. It
has been observed that some frequency bands in the spectrum
are largely unoccupied most of the time, some others
frequency bands are partially occupied and the remaining
frequency bands are heavily used. This leads to a
underutilization of radio spectrum. For the efficient utilization
of spectrum some innovative techniques are required . So to
provide a perfect solution for this ,Cognitive radio arises to be
a best solution for the inefficient utilization of spectrum
problem by introducing opportunistic usage of the frequency
bands that are not much occupied by the primary users. 2.
Cognitive radio is a form of wireless communication where
radio transceiver intelligently detects which spectrums are
free and which are not. After this it occupies the vacant one
while avoiding the busy one spectrum. Cognitive radios
promote open spectrum allocation which is a clear departure
from traditional command and control allocation schemes for
radio spectrum usage.
Keywords
Cognitive Radio, Spectrum Sensing, Primary User, Secondary
User, Hybrid Scheme
INTRODUCTION
As the need of wireless communication applications are
increasing, the available electromagnetic spectrum band is
getting crowded day by day. According to many researches it
has been found that the allocated spectrum (licensed
spectrum) is not utilized properly because of the static
allocation of spectrum. It has become more difficult to
either set up a new service or to enhance the existing one. In
order to overcome these problems we are going for “Dynamic
Spectrum Management” which improves the utilization of
spectrum. Cognitive Radio works on this dynamic Spectrum
access principle which provides the solution for the
ineffective utilization of spectrum in wireless communication.
This radio provides a highly reliable communication. In this
the unlicensed systems (secondary users) are allowed to use
the unused spectrum of the licensed users (primary users).
Cognitive radio will change its transmission parameters like
wave form, protocol, operating frequency, networking etc
Jatinder Saini
Assistant Professor
Dept of Computer Science and Engineering,
College, Fatehgarh Sahib
sainijatinder@gmail.com
which is totally dependent on the communication with the
environment in which it works [1]. Cognitive radio has mainly
four functionalities. They are popularly known as Spectrum
management, Spectrum Sharing, Spectrum Sensing and
Spectrum Mobility. Sensing is to identify the presence of
licensed users and unused frequency bands i.e., white spaces
in those licensed bands. Spectrum Management is to identify
how long the secondary users can use those white spaces.
Spectrum Sharing is to share the white spaces (spectrum hole)
fairly among the secondary users. Spectrum Mobility is to
maintain unbroken communication during the transition to
better spectrum. When compared to all other techniques,
Spectrum Sensing is the most crucial task for the
establishment of cognitive radio based communication
mechanism.
SPECTRUM SENSING
The major challenge for the cognitive radio is that the
secondary user needs to detect the presence of primary user
and accordingly primary user needs to quit the frequency band
at that time only to order to avoid the interference. This
phenomena is known as Spectrum Sensing.
The technique of Spectrum Sensing is the crucial process to
construct Cognitive Radio system [2]. Spectrum Sensing
technique can be categorized into two types. These are: Direct
and Indirect Techniques. Direct Technique is also called as
frequency domain in which the estimation is carried out
directly from signal approach. whereas in Indirect Technique
(also called as time domain approach), in this approach
autocorrelation of the signal is used for the estimation.
Spectrum Sensing Techniques for Spectrum Opportunities
1.)The Primary Transmitter Detection : The major issue in
cognitive radio is the detection of spectrum holes with the help
of spectrum sensing. The secondary users (non-licensed users)
do not have the knowledge of the whole spectrum, so we need
to detect primary users (licensed users) i.e. their local presence
in a particular spectrum. Detection of primary users can be
done using several techniques as shown in figure 1.1 [3].
1. Energy Detection
2. Cyclostationary Detection
2.)Cooperative and Collaborative detection: For spectrum
opportunities primary signals are identified completely by
communicating with other users, and this process can be
accomplished in either centralized access to spectrum in
which there is a central spectrum server which co-ordinates all
the activities or in a distributed manner followed by the
spectrum load smoothing algorithm.
Spectrum Sensing Techniques for Interference Detection
1.)Interference temperature detection: In this method of
spectrum sensing, primary users & secondary users co-exist
with certain limitations and they are only permitted to
transfer with less power and are also restricted by the
temperature level of interference so as not to disturb the
primary users.
2.)Primary receiver detection: Spectrum opportunities in
this method are totally based on the primary receiver's local
oscillator leakage power [4].
TYPES OF SPECTRUM SENSING TECHNIQUES
Spectrum
Sensing
Non-Cooperative
Sensing
Cooperative
Sensing
In ED technique, inputted signal goes through the BPF (band pass
filter) followed by the integration operation. The result obtained from
the integrator block is then compared to the threshold value which is
already predefined. This method of comparison is used to check
whether the primary user is present or not . The predefined value can
either be fixed or variable based on the conditions of the channel. It
is also known as the Blind signal detector because it does not know
about the structure of the signal. It checks the existence of the
energy signal by matching the energy calculated with a known
threshold λ .Signal detection can be reduced to a simple identification
problem, formalized as a hypothesis test analytically as below:
y(k)={ n(k)
H0 (White Space)
(1)
y(k)={ h* s(k) + n(k) } H1(Occupied)
where y (k) is the sample to be analyzed at each instant k and
n (k) is the noise of variance σ2. Let y (k) be a sequence of
received samples k Є {1, 2….N} at the signal detector, then a
decision rule can be stated as,
H0 ..........if ε < v
H1……...if ε > v
where ε=|E y(k)|2 is the estimated energy of the received
signal and v is chosen to be the noise variance σ2 [11].
Interference
Sensing
(2)
When the secondary user receiver does not have
information about the primary user
Apply Energy Detection Technique
Energy Detection
Cyclostationary Detection
Feed the signal to Energy Detector Technique
Figure 1: Spectrum Sensing Techniques Types [5]
The above figure shows the types of Spectrum Sensing
techniques. They are mainly categorized into three types, non
cooperative sensing or transmitter detection, cooperative
detection and interference based detection. Non cooperative
detection technique is further classified into Energy detection
and Cyclostationary feature detection which is the main topic
of this research paper [6].
Compare the final output with the threshold level
Y
Calculate Pf, Pd
Is multifading
and
shadowing
considered??
Calculate Pf,,Pd
accordingly
Primary Transmitter Detection:
In this we are going to discuss about two primary transmitter
detection techniques.
1) Energy Detection : It is a non cooperative detection
method which sense the primary signal based on the energy
of the signal [7]. This method is very easy to implement and
in this no prior information of primary user signal is required,
and for these two reasons Energy detection (ED) is very
popular among non-cooperative spectrum sensing techniques
[8]-[10].
Input
Band Pass
Filter
Square Law
Device
Integrator
N
Take measures to lower the value of Pd
Lower the threshold level so as to minimize Pd
Can the technique
differentiating
between noise
&signal
Threshold
N
End
Output
Figure 2: Block diagram for Energy Detection Technique
Figure 3: Flow Sequence of Energy Detection Technique
2.) Cyclostationary Detection Technique Man made signals
are normally not stationary but some of them are
cyclostationary, showing periodicity in their statistics. This
periodicity can be utilized for the detection of a random signal
which has a particular modulation type in a background of
noise. Such detection is called cyclostationary detection. The
signal of the PU can be detected at very low SNR values if it
exhibits strong cyclostationary properties. If the
autocorrelation of a signal is a periodic function of time t with
some period then such a signal is called cyclostationary and
this cyclostationary detection is performed as follows,
Rx(τ)=E[x(t+τ)x*(t-τ)e-j2t]
BPF
N Point
FFT
(3)
Correlate
Average
over T
Feature
Detection
Figure 4: Block Diagram for Cyclostationary Detection
Here α is cyclic frequency and E[.] is the statistical
expectation operation. The spectral correlation function (SCF)
denoted by S(f,α), also called the cyclic spectrum is obtained
by computing the discrete Fourier transformation of the cyclic
auto correlation function(CAF). Detection is completed
finally by searching for the unique cyclic frequency
corresponding to the peak in the SCF plane. This approach is
vigorous to interference and random noise from other
modulated signals, because different modulated signals have
different unique cyclic frequencies while noise has only a
peak of SCF at zero cyclic frequency.
Cyclostationary Detection Technique
Original signal x(t) is modulated using sine wave,
repeating spectrum, hoping sequence, cyclic prefix to
make the signal periodic
Apply Spectral Correlation Function
Y
It is a Energy
signal s(t)
Is there a
correlation
between signals?
N
It is a noise
n(t)
Do the further Processing
Figure 5. Flow sequence of Cyclostationary technique
RELATED WORK
Spectrum sensing is very crucial for the successful
development of Cognitive Radios. The main motive of present
spectrum sensing schemes for Cognitive Radio is classified
into two main categories:the first is to improve the
performance of local sensing, and the second is to improve
the performance by having a proper communication between
Secondary users.
In [12] author presented various sensing methods, their
performance, applicability and effectiveness under different
transmission conditions and advantages and disadvantages
incorporated with each sensing method. Authors have
evaluated the performance of cognitive radio with energy
detection based spectrum sensing (ED-SS) in AWGN, and
Cyclo-stationary feature is used for Spectrum sensing .In this
paper the results of different windowing techniques are
implemented along with their contour plots and a comparative
study based on the simulation results is also proposed
successfully. This technique based on window functions will
be helpful to detect the primary user under low SNR
conditions.
A possible two-stage spectrum sensing approach was
sudggested by authors in [13]. A very fast spectrum sensing
algorithm based on the energy detection is introduced
focusing on the coarse detection. A complementary fine
spectrum sensing algorithm adopts one order Cyclostationary
properties of primary user’s signals in time domain. This
feature detection technique in time domain realizes simple and
low computational complexity compared to spectral feature
detection methods. Also, it drastically reduces hardware
burdens and power consumption as opposed to two-order
feature detection. The sensing performance of the proposed
method is studied and the analytical performance results are
given. The results indicate that better performance can be
achieved in proposed two-stage sensing detection compared to
the conventional energy detector.
A new & improved local spectrum sensing scheme, namely
I3S (Intelligent Spectrum Sensing Scheme) was suggested in
[14] to improve the utilization efficiency of the current radio
spectrum by decreasing sensing time and by increasing the
detection reliability. The suggested scheme chooses either the
hybrid detector, or the matched filter detection . The proposed
Intelligent Spectrum Sensing is compared with the existing
non-cooperative detection techniques. It is concluded that
suggested scheme has better results with less mean detection
time, if prior information of Primary user waveform is known.
And it is observed that this scheme is having results which is
approximately
equivalent
to results obtained from
cyclostationary detection technique .
Authors have developed a low complexity detector based on a
combination of two well-known and complementary spectrum
sensing methods: energy and Cyclostationary detection in [15]
The Cyclostationary detector is used to estimate the noise
level N0 , which is then used to fix the threshold of the energy
detector. Simulation results show promising performances of
the proposed detector in low Signal to Noise Ratio (SNR).but
this paper demands the study of power consumption and the
convergence time of the E-HSD architecture.
A hybrid model terminology was presented in [16] which is a
proper channelization of the detection techniques in a non
cooperative environment utilizing the various techniques of
estimation of different parameters. The three techniques
together sense the opportunistic spaces in the focused
spectrum band efficiently and help in the utilization of
spectrum with the increase in overall efficiency. The
presented approach helps in detecting the idle spectrum bands
(spectrum holes that is the underutilized sub bands of the
radio spectrum) opportunistically with better utilization of the
spectrum under non cooperative sensing with increase in the
overall spectrum efficiency.
signal undergoes through the proposed hybrid model and in
this value of noise n(t) &value of signal s(t) is calculated
using the cyclostationary feature of hybrid model by applying
the spectral correlation function which differentiates between
the energy signal and noise signal. Along with this, the second
function hypothesis model for x(t) is performed. The result of
which ensures whether a primary user is present in that
location or not. If primary user is present, then a condition is
checked does the receiver of the secondary user have the
desired information regarding the primary user transmitter? If
the receiver does not have any information regarding the
various parameters of the transmitter of the primary user then
we will implement the hybrid methodology. In this we will
make use of Beam steering technique and obtain the RF signal
following by calculating the relative phase .and accordingly
we will change the direction of antenna according to the phase
obtained.
Signal is transmitted by the primary user,s(t)
Experimental Setup
Sno
1.)
2.)
3.)
4.)
5.)
6.)
7.)
Table 1. Network Scenario for Simulation
Parameter
Value
Number of Mobile Nodes
20
Simulation Time
50 s
Routing Protocol
WCETT
Traffic Type
CBR TCP-UDP
Channel
Wireless
Mac type
802_11
Area
1000*1000
Signal is sensed by the receiver of secondary user,x(t)
Hybrid model for primary user signal s(t)
Calculate the value of noise n(t) &value of Energy s(t)
using Cyclostationary Detection Technique
Proposed Work
Proposed Methodology: Hybrid model for transmitter
based spectrum sensing The hybrid model for transmitter
based detection is the combination of two techniques (Energy
detection and Cyclostationary detection). This hybrid
technique will help in detecting the spectrum holes in a more
efficient way.
Flow sequence clearly shows that the first task is to determine
whether a primary user exists in spectrum or not . For this
issue, we use the general model for transmitter detection (1).
The received signal x(t) has two outcomes H0, H1. If x(t) = H0,
then it is a null hypothesis (means no license user is present)
hence further proceedings cannot be done hence the cognitive
user moves to another area. But, if x(t) = H1, then it is an
alternative hypothesis (means primary user is present and
further proceedings can be implemented).The next job is the
combined application of the two detection techniques. The
disadvantage of Energy Detection is the inability to
differentiate between the energy signal and the noise. This
problem is solved by the second detection technique which is
cyclostationary detection technique. This technique applies
spectral correlation function and identifies whether the signal
is energy signal or noise. From flowchart we can see that
initially there will be some communication between the
primary transmitter and receiver. Let it be s(t) and noise in the
environment be n(t). The signal which is sensed by the
secondary user receiver and is received be x(t). The received
No Lisence
User Present
Y
Is x(t)=n(t)?
N
Lisensed
User
Present
Y
Is
x(t)=hs(t)+n(t)??
N
Apply Beam Stearing Technique
Obtain the RF signal
Calculate Relative Phase
c
Change direction of Antenna according to phase
Use the results for further processing
Figure 6. Flow sequence of Proposed Hybrid Technique
RESULTS AND ANALYSIS
An extensive set of simulations have been conducted using
the system model as described in the previous section. The
emphasis is to analyze the comparative performance of three
spectrum
sensing
techniques
(Energy
Detection,
Cyclostationary Detection and Proposed Hybrid Technique of
Detection). The performance metrics used for comparison
include the “probability of detection”, “probability of false
detection” and “probability of missed detection”. The number
of nodes considered in this analysis is twenty.
PD %
74
72
70
68
66
PD %
1.) Probability of Primary Detection
Figure-7 depicts the “probability of detection” as a function of
pause time for the three cases: (i) Energy Detection,
(ii)Cyclostationary Detection and (iii) Proposed Hybrid
Detection technique. The probability of detection may be
defined as a probability when a secondary user declares the
presence of a primary user when the spectrum is occupied by
the primary user. The average table of probability of detection
shows that hybrid technique is better than Cyclostationary
and Energy Detection Technique by2% and 4% respectively .
Figure 7: Comparison Graph for Probability of Detection
Table 2 . Average Table for Probability of Detection
Technique
Probability Of Detection
(Pd%)
Cyclostationary
70.28
2
Energy
68.28
3
Hybrid
72.41
Sno
1
Figure 8 : Average Graph For Probability of Detection
2.) Probability of False Detection Figure 10 illustrates the
“probability of false detection” for three transmitter detection
based spectrum sensing techniques versus pause time. It is
defined as a probability that a secondary user declares the
presence of the PU when the spectrum is idle is called the
probability of false alarm. It is observed that “probability of
false detection” of hybrid detection is smaller as compared to
other two techniques.
Figure 9: Comparison Graph for Probability of False
Detection
Table 3. Average Table for Probability of False
Detection
Probability Of False
Sno
Technique
Detection (Pf%)
1
Cyclostationary
42.27
2
Energy
46.45
3
Hybrid
37.46
PF %
PM %
50
40
30
20
10
0
PF %
Figure 10: Average Graph For Probability of False
Detection
3.)Probability of Missed Detection The probability of miss
detection (Pm) indicates the probability that an SU declares
the absence of a PU when the spectrum is occupied. The
probability of miss detection is simply, Pm = 1 – Pd. In view
of the fact that false alarms reduce spectral efficiency and
miss detection causes interference with the PU, generally it is
vital for optimal detection performance so that the maximum
probability of detection is achieved subject to the minimum
probability of false alarm.
32
30
28
26
24
PM %
Figure 12: Average Graph For Probability of Missed
Detection
CONCLUSION &FUTURE SCOPE
In this paper we have covered the cognitive radio in order to
explain the present wireless system. There are number of
aspects that are involved in cognitive radio that affects the
proper functionality of wireless system. Among various
aspects , spectrum sensing plays a vital role in cognitive radio.
We have explained different methods of spectrum sensing
such as Energy Detection, Cyclostationary Detection .We
have presented here the concept for combining these two
techniques with Beam Steering which in return gives better
results as compared with either Energy Detection or
Cyclostationary Detection technique. Further in future we can
combine more methods with fuzzy logic to get even more
better results.
REFERENCES
[1]B.A. Fette (2006),” Cognitive Radio Technology”, Elsevier
, Hardcover, 656 Pages, ISBN 978-0-7506-7952-7.
[2]D.B.Rawat, G. Yan, C. Bajracharya (2010), “Signal
Processing Techniques for Spectrum Sensing in Cognitive
Radio Networks’’, International Journal of Ultra Wideband
Communications and Systems, Vol. x, No. x/x, pp:1-10.
Figure 11 : Comparison Graph for Probability of Missed
Detection
Table4.Average Table For Probability of False Detection
Sno
Technique
Probability Of Missed
Detection (Pm%)
1
Cyclostationary
29.71
2
Energy
31.71
3
Hybrid
27.25
[3]Wenzhong WANG(2008) “Decision Fusion of Cooperative
Spectrum Sensing for Cognitive Radio under Bandwidth
Constraints,” IEEE International Conference on Convergence
and Hybrid Information Technology .
[4]Takeshi Ikuma and Mort Naraghi-Pour (2008), “A
Comparison of Three Classes of Spectrum Sensing
Techniques”, IEEE GLOBECOM proceedings.
[5]Ekram Hossain, Dusit Niyato, Zhu Han (2009), “Dynamic
Spectrum Access and Management in Cognitive Radio
Networks”, Cambridge University Press.
[6]Takeshi Ikuma and Mort Naraghi-Pour (2008), “A
Comparison of Three Classes of Spectrum Sensing
Techniques”, IEEE GLOBECOM proceedings.
[7]Shahzad A. et. al. (2010), “Comparative Analysis of
Primary Transmitter Detection Based Spectrum Sensing
Techniques in Cognitive Radio Systems,’’ Australian Journal
of Basic and Applied Sciences, 4(9), pp: 4522-4531, INSInet
Publication.
[8]Ekram Hossain, Vijay Bhargava (2007), “Cognitive
Wireless Communication Networks”, Springer.
[9]Linda Doyle (2009), Essentials of Cognitive Radio,
Cambridge University Press.
[10]D. Cabric, A. Tkachenko, and R. Brodersen, (2006)
“Spectrum sensing measurements of pilot, energy and
collaborative detection,” in Proc. IEEE Military Commun.
Conf., Washington,D.C., USA, pp: 1-7.
[11]Ian F. Akyildiz, Brandon F. Lo, Ravikumar (2011),
“Cooperative spectrum sensing in cognitive radio networks: A
survey, Physical Communication”, pp: 40-62.
[12]Mohapatra S.G., Mohapatra A.G., and Lenka S.K.(2013),
“Performance Evaluation of Cyclostationary based spectrum
sensing in cognitive radio network”, Automation, Computing,
Communication,
Control
and
Compressed
Sensing
(iMac4s),pp 90 – 97.
[13]Rao A.M., Karthikeyan B. R., Mazumdar D., and
Kadambi G. R. “ Energy Detection Technique For Spectrum
Sensing in Cognitive Radio”, SAS_TECH journals , volume
9, Issue 1 ,April 2010.
[14]Waleed E., Najam U.L., Seok L., and Hyung S.K. “I3S:
Intelligent spectrum sensing scheme for cognitive radio
networks” EURASIP Journal on Wireless Communications
and Networking , Springer 2013.
[15] Ziad K., Nafkha A.,and Jacques P.” Enhanced Hybrid
Spectrum
Sensing
Architecture
for
Cognitive
Radio
Equipment”, General Assembly and Scientific Symposium,
pp. 1-4,2011.
[16]Kapoor S., Singh G. “Non-Cooperative Spectrum
Sensing:
A
Hybrid
Model
Approach”,
Communications (ICDeCom), pp.1-5,2011.
Devices
and