Spectrum Sensing using Cooperative Energy Detection Method for Cognitive Radio

Proceedings of IOE Graduate Conference, Vol. 1, Nov 2013 91
Spectrum Sensing using Cooperative Energy Detection
Method for Cognitive Radio
Saroj Dhakal, Sharad Kumar Ghimire
Department of Electronics and Computer Engineering, IOE, Central Campus, Pulchowk, Tribhuvan University, Nepal
saroj_dhakal@live.com
Abstract: In order to utilize the spectrum efficiently, the role of spectrum sensing is essential in cognitive
radio networks. The transmitter detection based techniques, energy detection, cyclostationary feature
detection, and matched filter detections are most commonly used for the spectrum sensing. However,
detection performance in practice is often compromised with multipath fading, shadowing and receiver
uncertainty issues. To mitigate the impact of these issues, cooperative spectrum sensing has been shown to
be an effective method to improve the detection performance by exploiting spatial diversity. The main idea
of cooperative sensing is to enhance the sensing performance by exploiting the spatial diversity in the
observations of spatially located CR users. By cooperation, CR users could share their sensing information
for making a combined decision more accurate than the individual decisions. Thus the Cooperative sensing
can formulate excellent use of network assets and make the network smooth.
Keywords: Cognitive radio, radio spectrum, spectrum sensing, cooperative sensing, detection probability.
1. INTRODUCTION
In CR network, each CR user in the primitive sense is to
detect licensed (primary) users if they are present and
also identify if they are absent. This is achieved by a
process called spectrum sensing. The objective of
spectrum sensing are twofold i.e., CR users should not
cause interference to PUs and CR users should efficiently
identify and exploit spectrum holes for required
throughputs and quality of services. Thus the detection
performance can be primarily determined on the basis of
two metrics i.e., probability of false alarm, which denotes
the probability of a CR user declaring that a PU is present
when the spectrum is actually free, and probability of
detection, which denotes the probability of a CR user
declaring that a PU is present when the spectrum is
indeed occupied by the PU. Since a miss in the detection
will cause the interference with the PU and a false alarm
will reduce the spectral efficiency, it is usually required
for optimal detection performance that the probability of
detection is maximized subject to the constraint of the
probability of false alarm. In practice, several factors
such as multipath fading, shadowing and, consequently,
the hidden terminal problem may affect the detector’s
performance. These factors could be, however, mitigated
if the CR users shared their sensing results with the other
CRs. This mechanism is called cooperative spectrum
sensing [1]. This scenario can be illustrated as below
figure.
Due to this multipath fading and shadowing the signal to
noise ratio (SNR) of the received primary signal can be
quite small and detection task may very difficult. Since
the receiver sensitivity indicates that the capability of
detecting weak signal.
Figure 1: Receiver uncertainty and multipath fading
2. SPECTRUM SENSING CHALLENGES
Before the detail discussion of the spectrum sensing
techniques, some of the challenges associated with
spectrum sensing are mentioned.
Hardware requirements
In cognitive radio networks [2] analogue to digital
converter with high speed processors, high resolution and
with dynamic range are required for spectrum sensing.
Therefore, terminals are essential for processing
transmission for any opportunity over a much wide band.
Hence in order to identify and spectrum opportunity the
CR should be in a position to capture and analysed a
larger band. Radio frequency (RF) components are
imposed on additional requirements by larger operating
bandwidth such as antennas and power amplifiers.
Hidden primary user problem
This hidden primary user problem is like the hidden node
dilemma in Carrier Sense Multiple Accessing (CSMA)
[3]. Many factors like shadowing or severe multipath
fading which is observed by secondary user during the

transmission scanning for the primary user, create this
hidden primary user problem.
Figure 2: Hidden primary user problem in CR System [3].
Figure above illustrates the hidden node problem while
the operating ranges for the primary user (PU) and for the
cognitive radio device are shown by dashed lines.
Detecting spread spectrum primary users
A DSSS device resembles the FHSS devices but they
utilize a single band in order to spread their energy.
Primary users (PUs) which use spread spectrum
signalling are hard to identify as the power of the PUs is
dispersed over a broad frequency range, while the real
information bandwidth is much narrower [4]. A partial
solution of this problem is that if I know the hopping
pattern and method of perfect synchronization, but it is
possible but not easy to develop such an algorithm
through which estimation in code dimension is possible.
Sensing duration and frequency
As the CR operates in the bands of primary users, these
bands can be claimed by primary users at any time so in
order to avoid interference to and for PU, the CR should
be so sensible that it could identify the presence of the
PU and leave the band immediately. Hence within certain
duration, the CR should identify the presence of the PU.
Although these conditions put some complexity and
challenge for the design of CR, the sensing frequency is a
key parameter which should be chosen carefully. Sensing
frequency requirements can be relaxed if the status of the
PU is going to change slowly. For example in the case of
TV channel detection, in a geographical area presence of
a TV channel does not change frequency unless an
existing channel goes off or a new channel starts
broadcasting. Sensing period for IEEE 802.22 draft
standard is 30 seconds. Except sensing frequency, other
timing related parameters like channel move time and
channel detection time etc, are also defined in the
standard [5].
Decision fusion in cooperative sensing
For the case of cooperative sensing all results due to
various measurements and sharing information among
CR was a difficult task. There are two types of decisions
i.e.; soft and hard decisions, based on shared information
made by each cognitive device [6]. The results existing in
[6], illustrates that soft information made by each
outperforms hard information combining techniques in
term of the possibility of missed opportunity. While on
the other hand when cooperative users are high, hard
decisions perform as good as soft decisions. A variety of
simpler schemes for combining results are exploited in
[7].
Security
The cognitive radio air interface can be modified by a
malicious user to mimic a primary user. Hence primary
users can be misleading during the spectrum sensing
process. Such a behaviour or attack is called primary user
emulation (PUE) attack. The transmitter position is used
to identify an attacker in [8]. A challenging problem is to
develop valuable countermeasure when an attack is
identified. In order to prevent secondary users masked as
primary users, public key encryption based primary user
recognition is proposed in [9]. An encrypted value which
is generated using a private key is required to transmit
with the transmission of legitimate primary users.
3. ELEMENTS OF COOPERATIVE SPECTRUM
SENSING
The conventional cooperative sensing is generally
considered as a three-step process i.e., local sensing,
reporting, and data fusion. The overall elements used for
cooperative sensing as follows.
Fig3: Element of cooperative sensing [1]
Cooperation models
I considered the most popular parallel fusion network
models and recently developed game theoretical models.
For this paper preferred primarily fusion model only.
Sensing techniques
It used to sense the RF environment, taking observation
samples, and employing signal processing techniques for
detecting the PU signal or the available spectrum. The

choice of the sensing technique has the effect on how CR
users cooperate with each other.
Hypothesis testing
It is a statistical test to determine the presence or absence
of a PU. This test can be performed individually by each
cooperating user for local decisions or performed by the
fusion centre for cooperative decision.
Control channel and reporting
It concerns about how the sensing results obtained by
cooperating CR users can be efficiently and reliably
reported to the fusion centre.
Data fusion
It is the process of combining the reported or shared
sensing results for making the cooperative decision.
User selection
It deals with how to optimally select the cooperating CR
users and determine the proper cooperation
footprint/range to maximize the cooperative gain and
minimize the cooperation overhead.
Knowledge base
It stores the information and facilitates the cooperative
sensing process to improve the detection performance.
4. CLASSIFICATION OF SPECTRUM SENSING
Figure 4: Classification of spectrum sensing
Figure above shows the detailed classification of
spectrum sensing techniques. They are broadly classified
into three main types, transmitter detection or non
cooperative sensing, cooperative sensing and interference
based sensing. Transmitter detection is further classified
into energy detection, matched filter detection and
cyclostationary feature detection.
Spectrum Sensing using Energy Detection
It is not coherent detection method that detects the
primary signal base on sensed energy. Due to the
simplicity in the circuit and needlessness of prior
knowledge of primary user signal .Energy detection (ED)
is the most popular sensing technique in cooperative
sensing [11].
Figure 5 : Energy detection block diagram.
The block diagram for the energy detection technique as
shown in the above figure 3.4.1.In this method signal is
passed through the band pass filter of a band with ‘W’
and is integrate over a time interval. The output from the
integrator is then compared to an already predefined
threshold. This comparison is used to discover the
existence or absence of primary user. The threshold value
can set to be fixed or variable based on channel
condition. The ED is said to be a blind signal detector
because it is unaware of the structure of the signal. It
estimates the presence of the signal by comparing the
energy received with a known threshold derived from the
statistics of the noise. Analytically signal detection can be
reduced to be a simple identification problem and
formalizer as a hypothesis test.
= … … … (1)
= h *s + … … … (2)
Where is the sample to be analysed at each instant k
and is the noise of variance 2
. Let be a
sequence of received samples k= {1, 2... N} at the signal
detector then a decision rule can be sated as
…..if ɛ >
…..if ɛ <
Where ɛ=E | the estimated energy of the received
signal and is chosen to be the noise variance 2
.
However ED has the following disadvantages as follows
i. The sensing time taken to achieve a given
probability of detection may be high.
ii. Detection performance is subjected to the
uncertainty of noise power.
iii. ED cannot be used to distinguish primary
signals from the CR user signals. Thus, CR users
need to be tightly synchronized and refrained

from the transmissions during an interval called
quite period in cooperative sensing.
iv. ED cannot be used to detect spread spectrum
signals.
Match filter method
Figure 6: Block diagram of match filter method
A match filter (MF) is the linear filter design to maximize
the output signal to noise ratio for a given input signal.
When secondary user knows about the primary user
signal, a method called match filter detection, which is
equivalent to correlation, in which the unknown signal is
convolved with the filter whose impulse response is the
mirror and time shifted version of a reference signal. The
operation of match filter detection is expressed as,
Y[n] (3)
Where X is the unknown signal and is convolved with ‘h’
the impulse response of matched filter, which is matched
to the reference signal for maximizing the SNR.
Detection using matched filter is useful only in the cases
where the information from the primary users is already
known to the cognitive users [12].
Advantages: Matched filter detection needs less detection
time because it requires only (1/SNR) samples to meet a
given probability of detection constraint. When the
information of the primary user signal is known to the
cognitive user, matched filter detection is optimal
detection in stationary Gaussian noise.
Disadvantages: Matched filter detection requires a prior
knowledge of every primary signal. If the information is
not accurate, MF performs poorly. Also, the major
disadvantage of MF is that a CR would need a dedicated
receiver for every type of primary user.
Cyclostationary feature detection
Figure 7: Cyclostationary feature detection method.
It exploits the periodicity in the received primary signal
to identify the presence of primary users (PU). The
periodicity is commonly embedded in sinusoidal carriers,
pulse trains, spreading code, hoping sequences or cyclic
prefixes of the primary signals. Due to the periodicity,
these cyclostationary signals exhibit the features of
periodic statistics and spectral correlation, which is not
found in stationary noise and interference. Thus
cyclostationary feature detection is robust to noise
uncertainties and performs better then energy detection in
low SNR levels. Although it requires a prior knowledge
of the signal characteristics, cyclostationary feature
detection is capable of distinguishing the CR
transmissions from various types of PU signals. This
eliminates the synchronization requirements of energy
detection is cooperative sensing. Moreover, CR users
may not be required to keep silent during cooperative
sensing and thus improving the overall CR throughput.
This method is not encouraged to apply as it has its own
drawbacks owing to its high computational complexity
and long sensing time. Considering these issues, this
detection method is less common compared to energy
detection in cooperative sensing.
Interference based Detection
In this section I present interference based detection so
that the CR users would operate in spectrum underlay
(UWB like) approach.
Primary Receiver Detection
In general primary receiver emits the local oscillator (LO)
leakage power from its RF front end while receiving the
data from primary transmitter. This method is useful to
detect primary user by mounting a low cost sensor node
close to a primary user’s receiver in order to detect the
local oscillator (LO) leakage power emitted by the RF
front end of the primary user’s receiver which are within
the range of communication from CR system users. After
that the local sensor reports the sensed information to the
CR users so that they can identify the spectrum
occupancy status. This method can also be used to
identify the spectrum opportunities to operate CR users in
spectrum overlay.
Interference Temperature Management
Unlike the primary receiver detection, the basic idea
behind the interference temperature management is to
setup 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
based on where they are located with respect to the
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 transmitting
power that is restricted by the interference temperature
level so as not to cause harmful interference to primary
users.

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
cannot 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. This is noted that
the CR users in this method should know the location and
corresponding upper level of allowed transmitted power
levels. Otherwise they will interfere with the primary user
transmissions.
Figure 8: Interference temperature model [10].
5. CLASSIFICATION OF COOPERATIVE SENSING
There are three different cooperative sensing categories
based on how CRs share data in the network i.e.,
centralized, distributed and relay-assisted. In the
centralized category, an entity called fusion centre (FC)
controls all the cooperative sensing process.
Fig 9: Centralized cooperative sensing [1]
Figure illustrated these functions as CR0 is the FC and
CR1–CR5 are cooperating CR users performing local
sensing and reporting the results back to CR0. For local
sensing, all CR users are tuned to the selected licensed
channel or frequency band where a physical point-to-
point link between the PU transmitter and each
cooperating CR user for observing the primary signal is
called a sensing channel. For data reporting, all CR users
are tuned to a control channel where a physical point-to-
point link between each cooperating CR user and the FC
for sending the sensing results is called a reporting
channel. Note that centralized cooperative sensing can
occur in either centralized or distributed CR networks. In
centralized CR networks, a CR base station (BS) is
naturally the FC. Alternatively, in CR ad hoc networks
(CRAHNs) where a CR BS is not present, any CR user
can act as a FC to coordinate cooperative sensing and
combine the sensing information from the cooperating
neighbours [4].
In distributed cooperative sensing does not rely on a FC
for making the cooperative decision. In this case, CR
users communicate among themselves and converge to a
unified decision on the presence or absence of PUs by
iterations. Figure below illustrates the cooperation in the
distributed manner.
Figure 10: Distributed cooperative sensing [1].
After local sensing, CR1–CR5 shares the local sensing
results with other users within their transmission range.
Based on a distributed algorithm, each CR user sends its
own sensing data to other users, combines its data with
the received sensing data, and decides whether or not the
PU is present by using a local criterion. If the criterion is
not satisfied, CR users send their combined results to
other users again and repeat this process until the
algorithm is converged and a decision is reached. In this
manner, this distributed scheme may take several
iterations to reach the unanimous cooperative decision
[4].
The third scheme is relay-assisted cooperative sensing. In
this scheme both sensing channel and report channel are
not perfect, a CR user observing a weak sensing channel
and a strong report channel and a CR user with a strong
sensing channel and a weak report channel, for example,
can complement and cooperate with each other to
improve the performance of cooperative sensing. Figure
illustrates the functioning of relay assisted cooperative
sensing.
Figure 11: Relay Assisted cooperative sensing [1].

From figure, CR1, CR4, and CR5, who observe strong
PU signals, may suffer from a weak report channel. CR2
and CR3, who have a strong report channel, can serve as
relays to assist in forwarding the sensing results from
CR1, CR4, and CR5 to the FC. In this case, the report
channels from CR2 and CR3 to the FC can also be called
relay channels.
6. BENEFITS OF COOPERATION
Cognitive users who have a major role in a big deal to
sense the channels that have large benefits among which
the plummeting sensitivity requirements: channel
impairments like multipath fading, shadowing and
building penetration losses, impose high sensitivity
requirements inherently limited by cost and power
requirements. Employing cooperation between nodes can
drastically reduce the sensitivity requirements up to
-25dBm, and thus, reduction in sensitivity threshold can
be obtained by using this scheme agility improvement: all
topologies of cooperative network reduce detection time
compared to uncoordinated networks.
7. DISADVANTAGES OF COOPERATION
Sensing should be done from time to time at periodic
intervals by CR users as the sensed information is passed
at fast rate due to factors like mobility, channel
impairments etc., which increases the chances of data
overhead; large sensory data, since the spectrum, which
results to large amounts of data to be processed, being
inefficient in terms of cooperatively sensing data poses
lot of challenges, it could be carried out without incurring
much overhead because only approximate sensing
information is required eliminating the need for complex
signal processing schemes at the receiver side and
reducing the data load. Also even though a wide channel
has to be scanned, only a portion of it changes at a time
requiring update only the changed information and not all
the details of the entire scanned spectrum.
8. ED WITH COOPERATIVE METHOD
Step 1: Numbers of signal are received from two or more
users. Each received signal is sampled with certain
sampling frequency.
= h *s + where “i” is the number of users,
i=0, 1, β, γ….
Step 2: Estimated energy of each received signal is
calculated with noise variance .
ɛi = E |
Step 3: Integrated output signal of each user is compared
with already defined threshold value.
Step 4: Each user sends estimated energy to fusion centre
and compared with threshold value
…..if ɛi >
…..if ɛi <
Step5: Final decision at FC related to given band is based
on data fusion rule.
ɛi, ɛ {0, 1},
Where “0” (“1”) indicates the absence (presence) of
primary user,
ɛ i = decision of i-th CR user upon a given sub-band.
ɛ = final decision made at FC for the sub-band.
9. SIMULATION RESULTS
Cooperation communication has obtained much attention
because of its capability to obtain high diversity gain,
decreased transmitted power, increased system
throughput and combat fading. Diversity gain is achieved
by allowing the users to cooperate in cooperative
networks and even better performance can be achieved by
combing the cooperation with other techniques.
Figure 12: Energy detection simulation result
For simulation purpose, the graph is plotted in terms of
probability of false alarm ( ) and probability of
detection ( ). The detection performance can be mainly
determined on the basis of these two things, i.e.; the
probability of false alarm which denotes the probability
of CR users declaring that a PU is present when the
spectrum is actually free. And another one is probability
of detection, which denotes the probability of CR users
declaring that a primary user is present when the
spectrum is indeed occupied by primary user. Since a
miss in the detection will cause the interference with the
primary user and the false alarm will reduce the spectral
efficiency. Thus it is usually required for optimal

detection performance that the probability of detection is
maximized subject to the constraint of the probability of
false alarm.
In above figure 12, versus simulation result at -
10dB SNR level was shown. From this simulation,
different value of probability of false alarm ( ) having
with different value of probability of detection ( are
shown. However ED is always accompanied by various
disadvantages like noise uncertainty problem, sensing
time take to achieve a given probability of detection may
be high, ED method cannot be used to distinguish
primary and secondary signal. Therefore ED method is
not useful in low SNR level applications.
Practically, Energy Detection method is best among,
different transmitter based detection method. Thus to
mitigate the issues arises in non cooperative techniques
like multipath fading, shadowing and hidden terminal
problem, cooperative ED is used.
Figure 13: Receicer Operating Characteristics for ED with
cooperative method
The ROC curve (figure 13) shows the simulation result in
terms of probability of false alarm versus probability of
detection. The simulation was done at -10db SNR level
and considering Gaussian channel. The simulation uses
the different number of users showing with different
Receiver Operating Characteristic (ROC) in above figure
13. The number of user (sensors) was considered 5, 8 and
10. If the number of users were higher the chance of
detection is maximized. The different numbers of
cognitive users are cooperates to each other and make a
centralized decision from fusion centre. This decision
may increase the chance of detection; from this
simulation result if the number of user is 10 the
probability of detection is maximum at a constant
probability of false alarm. Similarly in another side in
figure 14, if the numbers of users are minimums the
chance of misdetection is high. From another perspective
if the numbers of users are higher the chance of
probability of misdetection is also minimized using
cooperation. Hence the higher numbers of users the
chance of misdetection is minimized using cooperation,
which optimizes the spectrum utilization.
Figure 14: Complementry Receicer Operating Characteristics for ED
with cooperative method
To overcome the shortcomings of energy detection, the
other methods based on the eigenvalue of the covariance
matrix of the received signal is useful. But this method
may give the ratio of the maximum eigenvalue to the
minimum eigenvalue can be used to detect the presence
of the signal. Based on some latest random matrix
theories (RMT) [13], here quantify the distributions of
these ratios and find the detection thresholds for the
detection algorithms. The probability of false alarm and
probability of detection are also derived by using the
RMT. The methods overcome the noise uncertainty
problem and can even perform better than energy
detection when the signals to be detected are highly
correlated. The methods can be used for various signal
detection applications without knowledge of the signal,
the channel and noise power. Furthermore, different from
matched filtering, the methods do not require accurate
synchronization.
10. CONCLUSION
Cognitive radio is the promising technique for utilizing
the available spectrum optimally. The important aspect of
cognitive radio is spectrum sensing and from that
identifying the opportunistic spectrum for secondary user
communication. In this paper, different existing spectrum
sensing techniques were studied. Among them, the
performance of energy detection was simulated in non
cooperative and cooperative environment. The
performance of the ED method is presented in term of
Receiver Operating Characteristic (curves). Hence the
probability of presence or absent of the primary user is
decided using the ROC curves. The probability of false
alarm versus probability of detection or misdetection is
plotted. The ED method having uncertainty noise
variance at low SNR level is the major demerit. Besides

this, it increases the probability of detection and
minimized the probability of miss detection by using
cooperation. Thus the higher number of cooperative users
gives the higher probability of detection even low SNR
level.
Hence the cooperative spectrum sensing technique is a
best technique for sensing spectrum which optimizes the
use of spectrum dynamically by using cooperation among
number of available cognitive users.
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