Detection the Spectrum Holes in the Primary Bandwidth of the Cognitive Radio Systems in Presence Noise and Attenuation

doi:10.4236/ijcns.2012.510071

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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

Email: [email protected], [email protected]

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



is given

by:

2π











(1)

The periodogram is

2π

1es

Sf x















(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



x.

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

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.

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