Int. J. Communications, Network and System Sciences, 2012, 5, 684-690
http://dx.doi.org/10.4236/ijcns.2012.510071 Published Online October 2012 (http://www.SciRP.org/journal/ijcns)
Detection the Spectrum Holes in the Primary Bandwidth of
the Cognitive Radio Systems in Presence Noise and
Attenuation
Ahmed S. Kadhim, Haider M. AlSabbagh
Department of Electrical Engineering, College of Engineering, Basra University, Basra, Iraq
Received July 23, 2012; revised August 20, 2012; accepted September 3, 2012
ABSTRACT
Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) represent two complementary developments that will re-
fashion the world of wireless communication. In order to investigate the roles of knowledge representation and reason-
ing technologies in this domain, we have developed an experimental cognitive radio simulation environment. That is, a
conventional radio when operating in a particular communications mode always follows the same procedure and either
succeeds or fails at a given task. A cognitive radio, by contrast, can use knowledge of radio technology and policy, rep-
resentations of goals, and other contextual parameters to reason about a failed attempt to satisfy a goal and attempt al-
ternate courses of action depending upon the circumstances.
Keywords: Cognitive Radio (CR); Power Spectral Density (PSD); Primary User (PU); Secondary User (SU)
1. Introduction
Most of today’s radio systems are not aware of their ra-
dio spectrum environment and operate in a specific fre-
quency band using a specific spectrum access system [1].
Investigations of spectrum utilization indicate that not all
the spectrum is used in space (geographic location) or
time. A radio, therefore, that can sense and understand its
local radio spectrum environment, to identify temporarily
vacant spectrum and use it, has the potential to provide
higher bandwidth services; increasing spectrum effi-
ciency lead to minimizing the need for centralized spec-
trum management. This could be achieved by a radio that
can make autonomous (and rapid) decisions about how it
accesses spectrum. Cognitive radios have the potential to
do this. Cognitive radios have the potential to jump in
and out of un-used spectrum gaps to increase spectrum
efficiency and provide wideband services. In some loca-
tions and/or at some times of the day, 70 percent of the
allocated spectrum may be sitting idle [2]. The FCC has
recently recommended that significantly greater spectral
efficiency could be realized by deploying wireless de-
vices that can coexist with the licensed users [3].
Figure 1 shows the unusing of cognitive radio for the
spectral holes [4].
In this paper we simulated the basics of cognitive radio
enabling dynamic spectrum access at run time. The spec-
trum is utilized efficiently by cognitive users and take the
priority to the primary user if they return to use the spec-
trum also take the effect of noise and attenuation on the
spectrum.
This paper is organized as follows: in Section 2 a com-
plete description for PSD is given. Section 3 explains
Spectrum Concentration. Section 4 illustrates the system
performance with the block diagram. The simulation re-
sults are presented in Section 5. Section 6 concludes the
achievements from this study.
2. Power Spectral Density Detection
The power spectral density (PSD) is intended for con-
tinuous spectra [4-8]. An important attribute of random
noise is its power spectral density (PSD). The estimation
of the power spectral density (PSD) by the function call-
ed periodogram function.


The periodogram for a sequence 1n
x
x

is given
by:
2
1
1
ee
2π
N
j
jn
n
n
Sx
N
 



(1)
The periodogram is
2
2π
1
1es
f
j
n
NF
n
n
s
Sf x
FN







(2)
where ω is in radians/sample. Frequency in Hz and the
Fs are the sampling frequency. Periodogram is the PSD
estimate of the signal defined by sequence

1n
x
x.
C
opyright © 2012 SciRes. IJCNS

A. S. KADHIM, H. M. ALSABBAGH 685
Figure 1. Spectrum measurement across 900 kHz - 1 GHz band (Lawrence, USA) [4].
3. Spectrum Concentration
Figure 2 shows relatively low utilization of the licensed
spectrum which is largely due to inefficient fixed fre-
quency allocations rather than any physical shortage of
spectrum. This observation has forced the regulatory
bodies to search a method where secondary (unlicensed)
systems are allowed to opportunistically utilize the un-
used primary (licensed) bands commonly referred to as
white spaces.
It is clear from the plan that spectrum is not used fully.
This is turn incentive thinking for utilizing the cognitive
radio technology to make best from the available [10,11].
The current fixed frequency band allocation scheme
cannot accommodate these requirements of increasing
number of high data rate devices. The spectrum utiliza-
tion in the frequency bands between 30 MHz to 3 GHz
averaged over six locations was studied by the Shared
Spectrum Company [10,12]. The report shows that the
maximum utilization is approximately 25% in TV chan-
nel and the average usage is only about 5.2%. This find-
ing suggests that spectrum scarcity as perceived today is
mostly due to the inefficient fixed frequency allocation
rather than physical shortage of radio spectrum.
4. System Model
Consider a 5 carrier frequencies; Fc1 = 1000, Fc2 = 2000,
Fc3 = 3000, Fc4=4000 & Fc5 = 5000. Keeping the user
message/data signal frequency as 1000.

cos 2π1000
every user'sbase band data signal
t
x  .
Once user 1’s data arrive, it is modulated at the first
carrier Fc1, similarly as the 2nd user’s data arrives, it is
modulated at the 2nd carrier Fc2, and so on until fifth
user is assigned the Fc5 band. If any user’s data is not
present his frequency band remains empty which is
called a Spectral Hole [13-17]. Figure 3 shows the block
diagram representation for calculation of PSD.
Let us explain it through this simple example:
in_p = input('\nDo you want to enter first primary user
Y/N: ','s');
if(in_p == 'Y' | in_p == 'y')
y1 = ammod(x,Fc1,Fs);
end
:
:
:
in_p = input('Do you want to enter fifth primary user
Y/N: ','s');
if(in_p == 'Y' | in_p == 'y')
y5 = ammod(x,Fc5,Fs);
end
Firstly the 5 Carrier Frequency Bands (Fc) are initial-
ized for all users, Message Frequency (as taken 1000
here) and the Sampling Frequency (Fs). When any user’s
data arrives it is modulated at its carrier frequency, if any
user’s data is not present then his frequency band re-
mains empty. Then all the modulated signals are added to
create a carrier signal. The Power Spectral Density is
estimated by using periodogram method. All the PU is
assigned with spectrum according to their data require-
ments. When a new User (SU) arrives he is assigned the
first spectral hole. If all the slots are reserved ask user to
empty a particular slot. The slot that is to be fired is
asked and made empty accordingly to user. Whether to

A. S. KADHIM, H. M. ALSABBAGH 686
Figure 2. Inefficient use of spectrum [9].
Figure 3. Block diagram for PSD calculation.

A. S. KADHIM, H. M. ALSABBAGH 687
add or not the Noise and in how much amount is asked to
user. The output is plotted. The attenuation and % age of
attenuation is asked to be added and plotted accordingly
[4,10,12,18].
5. Results
We design our system to have 5 different frequency
channels and each user is assigned a particular frequency
band. Once the program is run it l asks to add a user and
assign it a particular band in the ascending order.
In Figure 4 the users 2, 3 and 5 are not entered, thus
their respective bands are still un-allocated. The power
spectral density behavior of the carrier signal is shown in
Figure 5.
Now, with another user is adding as shown in Figure 6,
the system will search the first available gap in the spec-
trum and automatically assign it to the new user. As the
first available gap was after User 1 as User 2 was not
sending any data so the band reserved for User 2 at start
is now assigned to this new User.
From Figure 7 it can be seen that the first spectral gap
has been filled by assigning it the new incoming user’s
data. The first spectral gap belonged was that of User 2.
Figure 4. Command widow showing entry of u sers.
Figure 5. PSD graph.
Figure 6. Command window.
Figure 7. PSD graph.
With adding another user the list look is as shown in
Figure 8.
As user 3’s data was not present the spectral gap of
User 3 has been filled by the next incoming user as
shown in Figure 9.
Now we have just one empty slot left which will get
filled by addition of another Primary User as depicted in
Figure 10.
Figure 11 shows the power spectral density of the
signal and all of the frequency bands are efficiently in
use after the addition of the last incoming user.
Once all the slots are being assigned our system will
entertain no other Users will be able to free up the slots
one by one as shown in Figure 12.
If it is required to empty a slot it will remove the data
in the first slot and make it ready for the next assignment.
Similarly, noise and attenuation parameter can be
added to analyze the channel characteristics, as illustra-
ted in Figure 13.
Therefore, noise is added to the signal. The resulting
noisy carrier’s power spectral graph is given depicted in
Figure 14.
Then, attenuating the carrier the system will ask for the
percentage of attenuation required, as shown in Figure 15.

A. S. KADHIM, H. M. ALSABBAGH 688
Figure 8. Command window.
Figure 9. PSD graph.
Firgure 10. Command windows.
Here it is seen that the effect of adding attenuation to
the signal in Figure 16. As the level of the signal de-
pends upon the % age of attenuation is added.
6. Conclusion
This paper takes the problem of in-efficient spectrum
Figure 11. PSD graph shows All of the bands are in use.
Figure 12. command window.
Figure 13. Command window.
utilization i.e. shown by FCC that the spectrum is not
scarce but it is not used efficiently with maximizing the
utilizations. A PSD is introduced to test the portion of the
spectrum which is not used by PU at a time to be allo-
cated for SUs. The priority to PUs and accordingly the
left sots are allocated to SUs is presented. Then, addi-

A. S. KADHIM, H. M. ALSABBAGH 689
Figure 14. Noisy channel’s power spe c tral density graph.
Figure 15. Command window.
Figure 16. Noisy and attenuated Carrier’s power spectral
density graph.
tional noise and attenuation are considered to evaluate
their effects on the availability of the signal. The ob-
tained results show that such simulation is capable to
illustrate a wide range of results and different case of pa-
rameters.
REFERENCES
[1] D. Cabric, S. M. Mishra and R. W. Brodersen, “Imple-
mentation Issues in Spectrum Sensing for Cognitive Ra-
dios,” Proceedings of the 38th Asilomar IEEE Confer-
ence on Signals, Systems and Computers, Vol. 1, 2004,
pp. 772-776.
[2] “Spectrum Occupancy Measurements, Loring Commerce
Centre, Limestone, Maine,” 18-20 September 2007.
[3] S. J. Kim, E. C. Kim, S. Park and J. Y. Kim, “Dynamic
Spectrum Allocation with Variable Bandwidth for Cogni-
tive Radio Systems,” Proceedings of the 9th IEEE Con-
ference on International Symposium on Communication
and Information Technology, Seoul, 28-30 September
2009, pp. 106-109.
[4] A. M. Wyglinski, “Cognitive Radio: A Flexible Wireless
Platform for Transceiver Optimization,” 2007.
http:// www.ittc.ku.edu/~ alexw
[5] J. Mitola, “An Integrated Agent Architecture for Software
Defined Radio,” Ph.D. Thesis, Royal Institute of Tech-
nology (KTH), Stockholm, 2000.
[6] M. Haddad, A. M. Hayar and M. Debbah, “Spectral Effi-
ciency of Cognitive Radio Syetems,” Mobile Communi-
cations Group Institut Eurecom, France, 17 March 2007.
[7] A. Ginsberg, J. D. Poston and W. D. Horne, “Experi-
ments in Cognitive Radio and Dynamic Spectrum Access
Using an Ontology-Rule Hybrid Architecture,” The MI-
TRE Corporation, McLean, 2008.
[8] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless
Communications,” IEEE Journal on Selected Areas Com-
munication, Vol. 23, No. 2, 2005, pp. 201-220.
doi:10.1109/JSAC.2004.839380
[9] A. Shahzad, et al., “Comparative Analysis of Primary
Transmitter Detection Based Spectrum Sensing Tech-
niques in Cognitive Radio Systems,” Australian Journal
of Basic and Applied Sciences, Vol. 4, No. 9, 2010, pp.
4522-4531.
[10] Q. Zhao and A. Swami, “A Decision-Theoretic Frame-
work for Opportunistic Spectrum Access,” IEEE Wireless
Communication Magazine Special Issue on Cognitive
Wireless Networks, Vol. 14, No. 4, 2007, pp. 14-20.
[11] M. A. McHenry, “NSF Spectrum Occupancy Measure-
ments Project Summary,” Shared Spectrum Company
Report, National Radio Astronomy Observatory (NRAO)
Green Bank, West Virginia, 2005.
[12] R. Etkin, A. Parekh and D. Tse, “Spectrum Sharing for
Unlicensed Bands,” IEEE Journal of Selected Areas Com-
munication, Vol. 25, No. 3, 2007, pp. 517-528.
doi:10.1109/JSAC.2007.070402
[13] E. Hossain, “Cognitive Wireless Communication Net-
works,” Springer, New York, 2007.
[14] A. A. El-Saleh, M. Ismail, O. B. A. Ghafoor and A. H.
Ibrahim, “Comparison between Overlay Cognitive Radio
and Underlay Cognitive Ultra Wideband Radio for Wire-
less Communications,” Procedings of the 5th IASTED
(AsiaCSN 2008), Langkawi, 2-4 April 2008, pp. 41-45.
[15] IEEE 802.11 Wireless RAN, “Functional Requirements
for the WRAN Standard, IEEE 802.11 05/0007r46,”
2005.
[16] Z. Chair and P. K. Varshney, “Optimal Data Fusion in
Multiple Sensor Detection Systems,” IEEE Transactions

A. S. KADHIM, H. M. ALSABBAGH 690
on Aerospace and Electronic Systems, Vol. 22, 1986, pp.
98-101.
[17] P. K. Varshney, “Distributed Detection and Data Fusion,”
Springer, New York, 1997.
doi:10.1007/978-1-4612-1904-0
[18] A. Ghasemi and E. S. Sousa, “Fundamental Limits of
Spectrum Sharing in Fading Environments,” IEEE Tran-
sactions of Wireless Communication, Vol. 6, No. 2, 2007,
pp. 649-658. doi:10.1109/TWC.2007.05447