International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 7 - Mar 2014
Flexible Sensing Window in Efficient Cognitive Radio
Systems
Siddhatapa Mohapatra#1, S.Pavithra*2
#
M.Tech, VLSI Design Sathyabama University
JEPPIAAR NAGAR, RAJIV GANDHI SALAI,
CHENNAI – 600119. TAMILNADU
Abstract— Cognitive Radio (CR) is a revolutionary invention in the
recent generation of wireless technology. This is a promising
technology for the appropriate spectrum utilization by flexible use
of the frequency spectrum. When a frequency band is not actively
used by the primary user, seems to be ideal. Taking the advantage
of the duration of the spectrum hole, the secondary user can use the
frequency band. This duration implies the interval for the
appearance of two primary users. Within that time interval, the
spectrum hole has to be sensed by the secondary user. Spectrum
utilization is directly affected by the duration of the spectrum hole.
More efficiently the spectrum hole can be sensed, more effectively
the it can be utilized. In this paper a variable numbers of samples
are used for sensing the presence of spectrum holes. It leads to the
improvement of the probabilities of spectrum hole detection and
also the spectrum utilization. This idea reduces the probability of
miss detection and probability of false alarm in the existing sensing
method. The simulations are done using Xilinx ISE design suit 9.1i
and ModelSim and synthesized using VHDL.
Keywords— Cognitive Radio, Energy Detection, Spectrum holes,
I. INTRODUCTION
A rapid advancement in the field of wireless communication
leads to the increasing demand of the available radio frequency.
According to FCC (Federal Communication Commission)
more than 70% of available spectrum is underutilized.
International Regulatory bodies, such as ITU, harmonize usage
of spectrum through spectrum allocation and dedicating bands to
specific applications. Regional or national regulatory bodies,
such as FCC, assign the bands to service providers. Each service
provider acquires a license for its assigned band. Within the
current spectrum framework, most of the spectrum bands are
exclusively allocated to specific licensed services. However, a
lot of licensed bands, such as those for TV broadcasting, are
underutilized, resulting in spectrum wastage. For prevention of
this wastage Federal Communications Commission (FCC) has
opened the licensed bands to unlicensed users through the use of
cognitive radio (CR) technology. Cognitive radio is
revolutionary invention to overcome spectrum scarcity.
Cognitive radio (CR) technology is a promising invention
which provides an ultimate solution for this issue by ensuring
the efficient utilization of the available spectrum. Cognitive
radio paves the way for the utilization of the available spectrum
ISSN: 2231-5381
by allowing the unlicensed users to sense the spectrum within
the duration of white space. It is called a smart radio system
which learns from the environment, adapts its parameters and
acts accordingly. This cognition depends on an efficient sensing
method.
Cognitive radio (CR) is a tremendous concept in wireless
communication system to make the dynamic spectrum access to
happen in the radio frequency spectrum. This results the
accessing of frequency spectrum by the cognitive user or the
secondary user. The spectrum holes are detected by the
secondary user when the frequency band is not used by the
primary user. As for accessing a frequency spectrum, first
priority is given to the primary user. So base on the request,
secondary user releases the spectrum to the primary one. The
spectrum hole appears for a limited span. In this duration,
secondary user has to sense the presence of spectrum hole and
then utilization will take place. The duration of spectrum hole
sensing is inversely proportional to the utilization of the
spectrum hole. To avoid the underutilization of the spectrum,
improvising the sensing method is a good option.
A proper energy detection method is needed for the sensing
purpose. An efficient sensing method gives a result about the
presence or absence of the spectrum holes. Some draw backs are
present in the existing spectrum sensing method. That can be
avoided by improving the sensing with an adaptive sensing. The
spectrum holes appear for a limited time frame. Within that time
interval the cognitive radio user should be able to sense it. Once
sensing is done, rest of the time will be used for utilizing the
spectrum hole. In the literature, no such adaptive sensing
techniques that consider the spectrum holes utilization is not
available [1]. The related references [3],[4] which have
mentioned various methods of adaptive sensing.
The rest of the paper is organized as follows: in the section
II, the problems in the existing system are discussed. In the
section III, the algorithm and an explanation about it is done.
The ModelSim simulation outputs are explained in the section
IV. Conclusion is in the section V.
II. EXISTING SYSTEM AND PROBLEMS
Consider a frequency spectrum, which is in used by the
primary users. In the Fig. 1, the usage of frequency spectrum by
http://www.ijettjournal.org
Page 321
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 7 - Mar 2014
the primary user is shown. Continuously the spectrum is not
being used by the primary users. These ideal periods are known
as white space or spectrum holes, represented as Ai. For some
interval of time, the frequency spectrum is being ideal. This
duration is mentioned by Di. This is the time when the
secondary user has to realize about the presence of the spectrum
holes. So that an under utilization of frequency can be avoided
.
determined. Based on the outcomes of the quantized signal,
energy detection is going to happen.
The sensing is done by comparing the computed energy with a
threshold value. An energy detector measures the received
energy on a primary band during an observation interval. If the
measured energy is less than the threshold value, the detection
process will declares a white space. The performance of the
detector depends on the correctness of the threshold value. The
correct threshold value is difficult to determine. As already
mentioned about the presence of additive Gaussian noise in the
environment, it leads to a fixed threshold value [5].
For calculation of the threshold value Quantum Theory from
physics has been taken. Energy of an electromagnetic wave
depends on the frequency of the energy particles. The equation
followed for the calculation is
E=hf………(1)
Fig. 1. Sensing the white space in a limited duration in discrete time.
One assumption is made about the signal received by the
cognitive radio user. This signal is received along with the
AWGN (Additive White Gaussian Noise). Different methods
are used for energy detection. By the conventional energy
detection method, fixed numbers of samples are used for energy
detection.
In Fig. 2. Block diagram of a common method of spectrum
sensing is drawn. It’s a method of low computations and less
complexities. By the method of sampling and quantizing, a
Fig. 2. Block diagram for energy detection based sensing.
continuous time and continuous amplitude received signal is
converted to the discrete time and discrete amplitude signal. For
a particular instant of time, the energy level of the signal can be
ISSN: 2231-5381
Where, h is the Planks constant (6.626 * 10-34) and f is the
frequency of the channel needed to be sensed. These energy
particles are nothing but the photon having some frequency.
Each and every particle in this world is made up of a huge
numbers of atoms. That is the reason to multiply the frequency
with a constant value. For the computation of the energy value
at a particular time period, fixed numbers of samples are
considered. This method of calculation leads to the increase in
the probability of detection and false alarm represented as Pd
and Pf. Both of these factors depends on the number of samples
are used for sensing. Let these samples used for energy
detection is called window.
 If this widow size is very short then it may increase the
probability of miss detection.
 Similarly if the window size is too long then it may
lead to the maximization of the probability of false
alarm.
 If for sensing most of the samples from the given frame
is used, then for proper utilization of the spectrum hole
enough time will not be left for the secondary user.
Before the utilization, the primary user may request for
transmission.
The performance in the proposed system depends on the hole
utilization. The hole utilization is represented by Uh.
Referring the Fig. 1, Ss is the time for sensing for the detection
of spectrum holes. It may happen that, Ss may include some
samples where, primary users exist. It causes to the miss
detection of primary user by the secondary user. Let Ts is the
time period for the identification of the spectrum hole. Sensing
Ss is a part of Ts. It may not correct all the time for sensing the
presence of primary user.
Vs=Ts-Ss..............(2)
http://www.ijettjournal.org
Page 322
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 7 - Mar 2014
Vs is the time period left after sensing for the utilization of
spectrum hole. But there is no 100% guarantee about the
absence of primary user during Vs. For accuracy another factor
Es is considered.
So Es is the overlapping period of Vs and Ts. If Es is greater
than zero, then it indicates about the utilization of the spectrum
hole by the secondary user. If not it leads towards false detection
or sensing overhead. If the total numbers of spectrum holes
detected by the secondary user and the cardinality of spectrum
holes in a duration T is known, then there ratio will give the
value of hole utilization Uh. In the entire sensing process the
sensing time is fixed independent of the situation. In the
proposed system, a method to adapt different numbers of
samples for sensing purpose and this flexibility in sensing will
give a better result about spectrum hole detection and also
reduces the numbers of computation.
III. PROPOSED SYSTEM
In the proposed spectrum sensing technique, energy detection is
taking place through adaptive sensing window. The block
diagram is shown in Fig. 3.
Fig. 3. Block diagram for energy detection based on adaptive sensing.
For a proper explanation of this sensing technique, following
few term are going to be used.
 Wsen is the number of samples used for sensing.
Wmin is the minimum numbers of the samples are

needed for sensing. Wmin is the lower limit for the
numbers of samples used for sensing purpose.
Wmax is the maximum numbers of samples used for

sensing. This is the maximum limit of the size of
sensing window, beyond which samples are not
allowed.
 So the limit of the sensing window can be represented
as Wmin < = Wsen < Wmax.
ISSN: 2231-5381
The size of the sensing window depends on the various
situations. In this technique the status of current state and the
previous state is considered. The states are defined depending on
the presence or absence of the primary user and the secondary
user. Due to the two input variables, four conditions can arise.
Condition 1- Going with the first condition, both the current and
previous states, primary users are present. In the existing energy
detection method, for each outcome, one computation has to
take place. For this situation of current and previous states,
groupings of maximum number of samples are done and that is
considered as sensing window. It leads to the reduction in the
number of computations. After that sensing will take place, if
there is a detection of primary user then, size of sensing window
will be reduced by the minimum numbers of samples. For
energy detection if result is the presence of primary user, then
the above process is repeated till it reaches at the minimum
numbers of the samples. In other case if energy detection results
about the presence of secondary user, then maximum numbers
of samples are taken as sensing window.
Condition 2—In the next condition, both the current and
previous states, secondary users are present. In the existing
energy detection method, for each outcome, one computation
has to take place. For this situation of current and previous
states, groupings of maximum number of samples are done and
that is considered as sensing window. It leads to the reduction in
the number of computations. After that sensing will take place.
Condition 3--- Going with the third condition, when the current
state is the presence of primary user and the previous state is the
secondary user then sensing window will adapt the maximum
number of samples for sensing. In the existing energy detection
method, each outcome is the result of one computation. For this
situation of current and previous states, groupings of maximum
number of samples are done. As maximum priority is given to
the presence of primary user and current state is a presence of
primary user, as a result of which, maximum numbers of
samples are considered as sensing window. It leads to the
reduction in the number of computations. After that sensing will
take place.
Condition 4--- Going with the fourth condition, when the
current state is the presence of secondary user and the previous
state is the primary user then sensing window will adapt the
minimum number of samples for sensing. For a fine detection
minimum numbers of samples are used for sensing. So that
probability of miss detection can be reduced.
The above four conditions are better described by a flow
diagram as shown in Fig. 4.
http://www.ijettjournal.org
Page 323
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 7 - Mar 2014
IV. SIMULATIONS AND RESULTS
In this section results are presented in order to demonstrate
the functionality of an adaptive sensing window. Based on the
presence or absence of the primary user in the present and
previous states, some assumptions are made during the
simulation. The simulations are done using ModelSim by
developing coding in VHDL in Xilinx 9.1i design suit. Based on
the Table 1, simulation outputs for various conditions are
mentioned.
TABLE I
VALUE OF SENSING WINDOW
Pre PU(0/1)
1
0
1
0
PU
1
0
0
1
Wsen
Wsen- Wmin
Wmax
Wmin
Wmax
For condition (1,1), both the current and previous states
result the detection of primary user. For example, Wmax=4
and Wmin=2, in that case the sensing window will be Wmax. The
simulation output is shown in Fig. 6.
Fig. 4. Flow chart for proposed adaptive sensing technique.
Here after following different conditions the sensing window is
represented as W’sen. This proposed algorithm is also can be
modeled by a state transition diagram as shown in Fig. 5
Fig. 6. Sensing window for (1,1) condition.
If the sensing results the presence of primary user, again
Wsen will be reduced by Wmin. This uniform reduction will
happen till Wsen value is same as Wmax.
For condition (0,0), both the current and previous states
result the detection of secondary user. For example, Wmax=4
and Wmin=2, in that case the sensing window will be Wmax. The
simulation output is shown in Fig. 7.
Fig. 5. States Diagram of the Flow chart.
Here the two states are represented as State 1 and State 2.
Depending up on the presence or absence of primary user, state
transition will take place. State 1 shows the absence of primary
user and the state 2 shows the presence of the primary user. So
the absence state corresponds to the case when spectrum hole is
present. So for sensing, minimum numbers samples are used.
Similarly in the presence state corresponds to the presence of
primary user. So here maximum numbers of samples are taken
as the sensing window.
ISSN: 2231-5381
Fig. 7. Sensing window for (0,0) condition.
http://www.ijettjournal.org
Page 324
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 7 - Mar 2014
For condition (0,1), the current state is detected as the
presence of primary user and previous states result the detection
of secondary user. For example, Wmax=4 and Wmin=2, in that
case the sensing window will be Wmax on priority based. The
simulation output is shown in Fig. 8.
method both Pd and Pfa can be improved. So that Pd will
increase and Pfa will be reduced. For example, let the value of
detected PU is 12 and detected holes are 14. The actual numbers
of PU and holes are 20, and then Pd and Pfa will be 0.6 and 0.3.
By this example, it is cleared that the performance of energy
detection can be improved by using adaptive sensing window.
V. CONCLUSION
Fig. 8. Sensing window for (0,1) condition.
For condition (1,0), the current state is detected as the
presence of secondary user and previous states result the
detection of primary user. For example, Wmax=4 and Wmin=2,
in that case the sensing window will be Wmin for a fine
detection. The simulation output is shown in Fig.9.
The objective is to increase the probability of detection
(Pd) and reduce the probability of false alarm (Pfa). Pd is
defined as the ratio of numbers of the primary users detected to
the total
In this paper, a new method for spectrum sensing has been
proposed. The spectrum utilization is improved compare to the
fixed size of sensing window by using the adaptive quality of
sensing window. The functionality of the sensing window in
different scenarios is shown in the simulation output. The
literature of the affect window size on the performance of
energy detection and spectrum utilization has been well
explained. By an appropriate approach of spectrum accessing
can make the spectrum utilization more effective. So an
improved method of spectrum sharing, which consist of
spectrum sensing and spectrum accessing can initiate a better
spectrum utilization in cognitive radio system.
REFERENCES
1.
2.
3.
4.
Fig. 9. Sensing window for (1,0) condition.
numbers of the primary users actually existing. Pfa is defined by
subtracting the ratio of numbers of the holes detected to the total
numbers of the holes actually existing from one.
P d=
P fa= 1 -
…………..(3)
5.
…………………..(4)
For example, when fixed numbers of samples are used for
energy detection, let the value of detected PU is 10 and detected
holes are 12. The actual numbers of PU and holes are 20, and
then Pd and Pfa will be 0.5 and 0.4. But by adaptive sensing
ISSN: 2231-5381
6.
7.
8.
9.
[1] Dusadee Treeumnuk and Dimitrie C. Popescu, “Adaptive Sensing for
Increased Spectrum Utilization in Dynamic Cognitive Radio
Systems”, Department of Electrical and Computer Engineering, Old
Dominion University, USA,2012.pp.319-322.
[2] Danijela Cabric, Artem Tkachenko, Robert W.Brodersen,
“Experimental Study of Spectrum Sensing based on Energy
Detection and Network cooperation”, Berkley Wireless Research
center 2108 Allston Way #200 Berkeley.
[3] T. S. Shehata and M. El-Tanany, “A Novel Adaptive Structure of The
Energy Detector Applied to Cognitive Radio Networks,” in Proc.11th
Canadian Workshop on Information Technology, May 2009,pp. 95–
98.
[4] J. Y. Xu and F. Alam, “Adaptive Energy Detection for Cognitive
Radio: An Experimental Study,” in Proc. 12th International
Conference on Computer and Information Technology, Dec. 2009,
pp. 547–551.
[5]http://www.en.zte.com.cn/endata/magazine/ztecommunicationz2009ye
ar/no2/articles/200907/t20090701-173473.html
[6] Dusadee Treeumnuk and Dimitrie C. Popescu,Energy Detector with
Adaptive Sensing Widow for Improved Spectrum Utilization in
Dynamic Cognitive Radio System, Department of Electrical and
Computer Engineering, Old Dominion University, USA,2012.
pp.1528-1532.
[7] Energy Detector Prototype for Cognitive Radio System
Sabita MAHARJAN, Kimtho PO, and Jun-ichi TAKADA
Graduate School of Engineering, Tokyo Institute of Technology
2-12-1 O-okayama, Meguro-ku, Tokyo, 152-8550 Japan
http://www.ijettjournal.org
Page 325