14.IJAEST Vol No 7 Issue No 2 Spectrum Utilization by Using Cognitive Radio Technology 258 263

8/6/2019 14.IJAEST Vol No 7 Issue No 2 Spectrum Utilization by Using Cognitive Radio Technology 258 263

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Spectrum Utilization by Using Cognitive Radio

Technology

Er. Vishakha Sood

ECE departmentStudent RIEIT Railmajra

Punjab, [email protected]

Er. Manwinder Singh

ECE departmentFaculty RIEIT Railmajra

Punjab, [email protected]

Abstract: Cognitive radio networks can be designed to managethe radio spectrum more efficiently by utilizing the spectrum

holes in primary users licensed frequency bands. The

currently available unlicensed spectrum is reaching its limit

and various demands for applications and data rates in

wireless communications requires additional spectrum which

imposes limits on spectrum access. These requirements

demand for efficient and intelligent use of spectrum. The

system model and the problem of the optimum spectrum

allocation in cognitive radios are introduced and formulated.

There are very few experimental simulation techniques present

regarding cognitive radios, thus we intend to come out with a

simpler and efficient simulating technique. Our approach was

to take the decisions on the basis of power spectral density of

the channel which can be used cognitively to find out the

available gaps those can be assigned to new incoming usersthus improving the overall channel throughput.

Keywords: - cognitive radio (CR), Power spectral density

(PSD), Primary user (PU), secondary user (SU).

I. INTRODUCTIONMost of todays radio systems are not aware of

their radio spectrum environment and operate in a specific

frequency band using a specific spectrum access system.

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, increase spectrum efficiency and

minimize the need for centralized spectrum management.

This could be achieved by a radio that can makeautonomous (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 locations and/or at

some times of the day, 70 percent of the allocated spectrum

may be sitting idle. The FCC has recently recommended

that significantly greater spectral efficiency could be

realized by deploying wireless devices that can coexist with

the licensed users [1].

Figure-1.Measurement of 0~6 GHz spectrum Utilization at Berkeley

Wireless Research Center [1]

As we now that spectrum is not scarce but it is

not used properly or efficiently. It is shown in figure-1

that the total available spectrum is 0-6 GHZ but only up to

2 GHZ is used properly.

Figure-2.Spectrum measurement across 900 kHz-1 GHz band

(Lawrence, USA) [2]

The figure -2 shows the use of cognitive radio for filling

the spectral holes [2].

This paper is organized as follows: in section II we

will give complete description of PSD. In section III we

will explain the current frequency allocation plan in India

.In section IV we will explain the system performance with

the block diagram .In section V we will explain the

simulation results with graphs. Section VI will conclude the

theory.

Er. Vishakha Sood* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES

Vol No. 7, Issue No. 2, 258 - 263

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 90
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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II. POWER SPECTRAL DENSITY DETECTIONThe power spectral density (PSD) is intended for

continuous spectra [2]-[6]. The integral of the PSD over a

given frequency band computes the average power in the

signal over that frequency band. In contrast to the mean-

squared spectrum, the peaks in these spectra do not reflect

the power at a given frequency.

Syntax

Hpsd=dspdata.psd(Data)Hpsd=dspdata.psd(Data,Frequencies

Hpsd=dspdata.psd(...,'Fs',Fs)Hpsd=dspdata.psd(...,'SpectrumType',SpectrumTyp)Hpsd=dspdata.psd(...,'CenterDC',flag)

Hpsd = dspdata.psd (Data, Frequencies) uses the powerspectral density estimation data contained in Data andFrequencies vectors.

Hpsd = dspdata.psd(...,'Fs',Fs) uses the sampling frequency

Fs. Specifying Fs uses a default set of linear frequencies (inHz) based on Fs and sets Normalized Frequency to false.

Hpsd = dspdata.psd (...,'SpectrumType', Spectr mType) uses

the SpectrumType string to specify the interval over whichthe power spectral density was calculated. For data thatranges from [0 pi) or [0 pi], set the SpectrumType to one-

sided; for data that ranges from [0 2pi), set the

SpectrumType to two-sided.

Hpsd = dspdata.psd (...,'CenterDC', flag) uses the value offlag to indicate whether the zero-frequency (DC) component

is centered. If flag is true, it indicates that the DCcomponent is in the center of the two-sided spectrum. Set

the flag to false if the DC component is on the left edge ofthe spectrum.

The periodogram for a sequence[x1.xN] is given by the

formula

()

| | (1)

The periodogram will be

S (f) =

| | (2)

Where is in radians/sample. Frequency is in Hz, Fs arethe sampling frequency. Periodogram is the PSD estimate of

the signal defined by sequence [x1 .xN].

III. CURRENT SPECTRUM ALLOCATION ININDIA

The word spectrum refers to a collection of various

types of electromagnetic radiations of different

wavelengths. Spectrum or airwaves are the radiofrequencies on which all communication signals travel. In

India the radio frequencies are being used for different types

of services like space communication, mobile

communication, broadcasting, radio navigation, mobile

satellite service, aeronautical satellite services, defense

communication etc.

Indias National Frequency Allocation plan:-

The National Frequency Allocation Plan (NFAP)

forms the basis for development and manufacturing ofwireless equipment and spectrum utilization in the country.

Frequency bands allocated to various types of radio services

in India are as follows.

1) 0-87.5 MHz is used for marine and aeronauticalnavigation, short and medium wave radio, amateur

(ham) radio and cordless phones.

2) 87.5-108 is used for FM radio broadcasts3) 109-173 MHZ Used for Satellite communication,

aeronautical navigation and outdoor broadcast vans

4) 174-230 MHz not allocated.5) 230-450 MHZ Used for Satellite communication,

aeronautical navigation and outdoor broadcast vans

6) 450- 585 is Not allocated.7) 585-698 is used for TV broadcast8) 698-806 not allocated.9) 806-960 is used by GSM and CDMA mobile

services.

10) 960-1710 is used for Aeronautical and spacecommunication.

11) 1710- 1930 is used for GSM mobile services.12) 1930-2010 is used by defense forces.13) 2010-2025is not allocated.14) 2025-2110is used for Satellite and space

communications.

15) 2110-2170 is not allocated.16) 2170-2300 is used for Satellite and space

communications.

17) 2300-2400 is not allocated.18) 2400- 2483.5 Used for Wi-Fi and Bluetooth short

range services.

19) 2483.5-3300 Space communications.20) 3300-3600 not allocated.21) 3600-10000 Space research, radio navigation.22) 10000 are used for satellite downlink for broadcast

and DTH services.

As is clear from the above plan that spectrum is not

used fully thats why we are making use of cognitive radio

technology to make best from available[7],[12]. The current

fixed frequency band allocation scheme cannot

accommodate these requirements of increasing number of

high data rate devices. The challenges for managing the

radio spectrums in India are mentioned in [11].The

spectrum utilization in the frequency bands between 30

MHz to 3GHz averaged over six locations was studied by

the Shared Spectrum Company [12],[15].The report shows

that the maximum utilization is approximately 25% in TV


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channel and the average usage is only about 5.2% .This

finding suggests that spectrum scarcity as perceived today is

mostly due to the inefficient fixed frequency allocation

rather than physical shortage of radio spectrum.

IV. SYSTEM PERFORMANCEWeve taken 5 carrier frequencies Fc1 = 1000, Fc2

= 2000, Fc3 = 3000, Fc4=4000 & Fc5 = 5000. Keeping the

user message/data signal frequency as 1000.

x = cos (2*pi*1000*t)//every users base band data signal.

Once user 1s data arrive, it is modulated at the first carrier

Fc1, similarly as the 2nd

users data arrives, it is modulated

at the 2nd

carrier Fc2, so on till fifth user is assigned the Fc5

band. If any users data is not present his frequency band

remains empty which is called a Spectral Hole [16]-[20].

Figure-3 shows the block diagram representation for

calculation of PSD.

Figure 3: Block diagram for PSD calculation

Let us explain it by taking 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

Firstly we will initialize the 5 Carrier Frequency Bands (Fc)

for all Users, Message Frequency (as taken 1000 here) and

the Sampling Frequency (Fs). When any users data arrives

it is modulated at its carrier frequency, if any users data is

not present then his frequency band remains 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 requirements. 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 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 [2], [7]-[9].

V. SIMULATION RESULTSWeve designed our system to have 5 different

frequency channels and each User is assigned a particular

frequency band. Once we run our program itll ask to add a

User and assign it a particular band in ascending order.

Figure-4 command window showing entry of users

Here we havent entered User 2, & 4, thus their respective

bands are still un- allocated. We can see them below in the

power spectral density graph of our carrier signal.

Figure-5.PSD graph

This figure shows the PSD graph of the values entered

above. As is clear from the figure that we have allocated

only users 1, 3 and 5; their respective bands can be seen

here.

Figure-6 command window

Here the secondary users entry is asked and secondary user

is entered at the free space which is not occupied by PU.


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Figure-7 PSD graph

Here is the PSD of figure-6.We can see from fig-5 that slot

2 was not assigned to PU so it is allocated here to the SU.

Figure-8 command window showing noise addition

Here the noise adds operation is performed. The SNR is

asked to be added. As here 50 dB is added.

Figure-9 PSD graph

Here the added noise figure is plotted. We can easily

distinguish between the original signal and noise added

signal.

Figure-10 command window showing attenuation

Here we can see the attenuation operation. The %age by

which the signal is to be attenuated is added here.

Figure-11 PSD plot

Here we can see the effect of adding attenuation to the

signal. As the level of the signal depends upon the %age of

attenuation added.

Figure-12 command window

Here we can see that until all the slots are not filled the

program will re -run and ask for adding the secondary users.


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Figure-13 PSD graph

As we can see from fig-6 that slot 4 was un-occupied so it is

allocated here to SU.

Figure-14 command window showing all slots filled

Here again the secondary users entry is asked. If all slots

are reserved then the program will not re run.

Figure-15 PSD plot

Here is the PSD of above entered command. As all slots are

occupied so no user cant be entered now. It is required to

first vacate any slot for further entry.

Figure-16 command window showing slot fired operation

Here the slot fire operation is explained. The slot which is

required to fire by user is entered and made vacant

accordingly.

Figure-17 PSD Plot

As we can see from above command that slot 1 is fired so it

is free for next data or for next user to be used.

VI. CONCLUSION

In this paper we have taken the problem of in-efficientspectrum utilization i.e. shown by FCC that the spectrum is

not scarce but it is not used efficiently and we have tried to

maximize the utilization. We have made use of PSD and

tried to vary some parameters so that the portion of the

spectrum which is not used by PU at a time can be allocated

to SUs. Firstly we have given priority to PUs and

accordingly the left sots are allocated to SUs. Then we have

fired the slots. Then we have added noise and attenuation to

see their effects on the availability of the signal. As we have

obtained best results but the results can vary with the

previous researches due to variations in parameters.


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REFERENCES

[1] Seung Jong Kim, Dynamic Spectrum Allocation with VariableBandwidth for Cognitive Radio Systems, IEEE 2009.

[2] Cognitive Radio: A Flexible Wireless Platform for TransceiverOptimization by Alexander M. Wyglinski the University of Kansas.

[3] Joseph Mitola III,cognitive radio :An Integrated Agent ArchitectureFor Software Defined Radio, PhD dissertation, Royal Institute of

Technology (KTH) Stockholm, Sweden, 8 May, 2000.

[4] Spectral Efficiency of Cognitive Radio System; Majed Haddad,Aawatif Menouni Hayar and Merouane Debbah. Mobile

Communications Group Institut Eurecom, France March 17, 2007.

[5] Experiments in Cognitive Radio and Dynamic Spectrum Accessusing An Ontology-Rule Hybrid Architecture; Allen Ginsberg,

Jeffrey D. Poston, and William D. Horne The MITRE Corporation

McLean, VA.

[6] S. Haykin, Cognitive radio: Brain-empowered wirelesscommunications, IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp.

201220, Feb. 2005.

[7] Q. Zhao and A. Swami, A decision-theoretic framework foropportunistic spectrum access, IEEE Wireless Commun. Mag.

Special Issue onCognitive Wireless Networks, vol. 14, no. 4, pp. 14

22, Aug. 2007.

[8] A. Ghasemi and E. S. Sousa, Fundamental limits of spectrum-sharing in fading environments, IEEE Trans. Wireless Commun.,

vol. 6, no. 2, pp. 649658, Feb. 2007.[9] R. Etkin, A. Parekh, and D. Tse, Spectrum sharing for unlicensed

bands, IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 517528,

Apr. 2007.

[10] Ekram Hossain, Cognitive Wireless Communication Networks.[11] K. Sridhara, Ashok Chandra, P. S. M. Tripathi, Spectrum

Challenges and Solutions by Cognitive Radio: An Overview

Springer Journal Wireless Personal communication vol.45, No.3,

May 2008, pp.281-291.

[12] M. A. McHenry, NSF Spectrum Occupancy Measurements ProjectSummary, shared spectrum co. report, Aug. 2005.

[13] Q. Zhao and A. Swami, A decision-theoretic framework foropportunistic spectrum access, IEEE Wireless Commun. Mag.

Special Issue onCognitive Wireless Networks, vol. 14, no. 4, pp. 14

22, Aug. 2007.

[14] A. Ghasemi and E. S. Sousa, Fundamental limits of spectrum-sharing in fading environments, IEEE Trans. Wireless Commun.,vol. 6, no. 2, pp. 649658, Feb. 2007.

[15] R. Etkin, A. Parekh, and D. Tse, Spectrum sharing for unlicensedbands, IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 517528,

Apr. 2007.

[16] Ekram Hossain, Cognitive Wireless Communication Networks.[17] Ayman A. El-Saleh, Mahamod Ismail, Omar B. A. Ghafoor, and

Anwar H. Ibrahim, Comparison between Overlay Cognitive Radio

and Underlay Cognitive Ultra Wideband Radio for Wireless

Communications, Proc. of the Fifth IASTED (AsiaCSN 2008), pp.

41- 45, April 2-4, 2008, Langkawi, Malaysia.

[18] IEEE 802.11 wireless RAN, Functional requirements for the WRANstandard, IEEE 802.11 05/0007r46 Oct. 2005.

[19] Z. Chair and P.K. Varshney, Optimal data fusion in multiple sensordetection systems, IEEE Trans. on Aerospace and Elect. Syst.,

vol.22 pp.98-101, January 1986.

[20] P. K. Varshney, Distributed Detection and Data Fusion. Springer,1997.


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14.IJAEST Vol No 7 Issue No 2 Spectrum Utilization by Using Cognitive Radio Technology 258 263

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