investigation of cognitive radio system using matlab

INVESTIGATION OF COGNITIVE RADIO SYSTEM

USING MATLAB

Md. Manjurul Hasan Khan∗

Dr. Paresh Chandra Barman∗∗

Abstract

By developing wireless communication we reached at a step, where we get the Software Defined Radio (SDR).The addition of cognition on SDR. Then we got a radio technology that is cognitive radio. The cognitive radio is most effective for the working capability of disaster environment. It is also acceptable for its frequency allocation, agility, conceivability, capability. Spectrum sensing and its functionality that can give us a massive performance of its nascent spectrum management. IEEE 802.22 networks are cognitive technology based networks which will enhance the performance of 4G Communication systems. The main functionality of cognitive radio recognizes spectrum from desirable radio signal.In this paper, we reviewed the concept of simulating a cognitive radio system to reprocess locally unused spectrum to increase the total system capacity. This work focuses on the practical analysis of a Cognitive radio system. To test the performance of Cognitive radio, simulation has been carried out using MATLAB.

Keywords: Cognitive Radio, WRAN, Spectrum Sensing, Energy Detection Technique, MATLAB Simulation.

1. Introduction: Cognitive radio is a new technology designed specially to solve underutilization of the

wireless spectrum. Previous experiments, reported in [1&2] show that the spectrum

utilization of any fixed policy wireless network is within 6 % over the day. The spectrum is a

valuable resource, which may be wasted if it is underutilized. Cognitive radio can improve

the utilization of any wireless network by allowing secondary users to access the spectrum

holes, or white spaces, left by primary users who are licensed and have the rights to access

the spectrum at anytime and anywhere within the coverage area. The secondary users are

guests; they can access the licensed spectrum only, when the primary users are absent.

∗

Assistant Maintenance Engineer, Information Technology Department (System), Janata Bank Ltd, Head

Office, Dhaka, Bangladesh; ∗∗ Professor, Department of Information & Communication Engineering, Islamic University, Kushtia,

Bangladesh.

World

Vision

ISSN: 2078-8460

Vol. 8 • No. 1 • Nov 2014

Investigation of cognitive radio system using Matlab 73

Moreover, secondary users have to vacate the channel, when the primary users start to access

it. The presence and absence of the primary users can be determined by applying spectrum

sensing techniques. There are a lot of spectrum sensing techniques, but the most popular

methods are; matched filter detector, cyclostationary detector, and energy detector which are

explained in [1].

Sources: Akyildiz et al. (2006).

Fig. 1: Spectrum Utilization.

Fig. 1, shows the signal strength distribution over a large portion of the radio spectrum,

reveals that while the 6 spectrum usage is concentrated on certain portions of the spectrum, a

significant amount of the spectrum remains unutilized in some bands. This necessitates the

need for a more flexible means of controlling radio spectrum usage and control.

In this paper, a cognitive radio system, using the energy detector, is proposed to distinguish

the primary users from the secondary users when the transmitted signal spectrum is presented

The ability of the energy detector to determine the empty slots within the spectrum, in order

to be utilized by secondary users is verified. The cognitive radio system is defined in Section

2. Subsequently, in Section 3, defined wireless regional area network, the spectrum sensing

process of the cognitive radio system is stated in section 4. Section 5 describes energy

detection technique, finally in Section 6 & Section 7, the simulation process & result are

discussed.

2. Cognitive Radio:

The formal definition for Cognitive Radio is given as “Cognitive Radio is a radio for

wireless communications in which either a network or a wireless node changes its

transmission or reception parameters based on the interaction with the environment to

communicate effectively without interfering with the licensed users.”[3]

74 World Vision Research Journal Vol. 8, No. 1, 2014

The two main important characteristics of Cognitive Radio

� Cognitive Capability:

Cognitive capability refers to the ability of the cognitive radio technology to capture or sense

the information from its radio environment.[5]

� Re-configurability:

Re configurability enables the cognitive radio to be programmed dynamically according to

the radio environment. [5].

2.1 Cognitive Cycle: There are four main steps in Cognitive cycle [4]:

i. Spectrum Sensing: It refers to detect the unused spectrum and sharing it without harmful

interference with other users. It is an important requirement of the Cognitive Radio network

to sense spectrum holes, detecting primary users is the most efficient way to detect spectrum

holes.

ii. Spectrum Management: It is the task of capturing the best available spectrum to meet

user communication requirements.

iii. Spectrum Mobility: It is defined as the process where the cognitive user exchanges its

frequency of operation

iv. Spectrum Sharing: This refers to providing a fair spectrum scheduling method among

the users. Sharing is the major challenge in the open spectrum usage.

Fig.2. Cognitive cycle [6]


3. Wireless Regional Area Network (WRAN):

Wireless Regional Area Network (IEEE 802.22) defines air interface for use by license exempt devices on a non- interfering basis in VHF and UHF (54-862 MHz) bands which are also referred to as the TV White Spaces. This 802.22 standard utilizes cognitive radio technology to ensure that no undue interference is caused to television services using the television bands. In this way 802.22 is the first standard to fully incorporate the concept of cognitive radio. This new standard, which will operate in the TV bands, makes use of techniques such as spectrum sensing, incumbent detection and avoidance, and spectrum management to achieve effective coexistence and radio resource sharing with existing licensed services [9]. WRAN can have an edge over other types of networks inregards to the coverage area as it targets to provide a coverage ofabout 100km as against 15km maximum coverage offered bylargest WAN network [7]. WRAN proposes to have muchlarger coverage areas than today’s wireless networks. This mayprimarily be due to higher power and favorable propagation characteristics of TV bands. If power is not an issue, BS coverage can go up to 100 km. Some important parameters of this standard are summarized in Table 1.

Table 1: SUMMARISED SERVICE PARAMETERVALUES FOR IEEE 802.22[10]

Sr. No. Parameter Value

1 Spectral Efficiency 0.5 – 5 bit/sec/Hz

2 Average Spectral Efficiency 3 bits/sec/Hz

3 Throughput a. Downstream - 1.5Mbps per CPE

b. Upstream - 384Kbps

4 Coverage 100 Km

5 Operational Frequency Range 41- 910 MHz

6 Channel Bandwidths 6,7 and 8 MHz

7 Threshold for vacating channels

-116dBm over a 6 MHz channel (Digital TV)

-94dBm at the peak of NTSC

8 Wireless Microphone -107dBm in 200KHz bandwidth

9 Services Voice Data, Audio and Video.

4. Spectrum Sensing:

Spectrum sensing is the process that the cognitive user can determine the spectrum holes or white spaces left by primary users [1, and 2]. Spectrum holes are the frequency bands, or the time slots provided for a primary user operation, but they may be vacant at certain location and time. The success of secondary users to utilize these bands depends on their detection capability. Spectrum sensing can be considered as the most important stage in any cognitive radio system. If it is not carried out properly, the cognitive radio system will not be successful. There are a lot of spectrumsensing methods; such as matched filter detector, cyclostationary feature detector, and energy detector. Fig. 3 Shows Classification of Spectrum Sensing Techniques and Advantages and Disadvantages of Spectrum Sensing Techniques are given Table 2.


The ability of matched filter to increase the signal to noise ratio at the sampling instant is the

key for being used as a sensor [12, 13 and14]. Unfortunately, it cannot be used without the

existence of priori information about the primary user signal. Pilot signals are used to

overcome this problem.

The cyclostationary detector depends on estimating the spectral correlation of the received

signal and then decides the presence or absence of a primary user [12, 13 and14].The signal

is cyclostationary, if it’s mean and autocorrelation function are periodic. The modulated

signals are cyclostationary with a spectral correlation, but the noise is a random signal with

no correlation.

Fig.3. Classification of Spectrum Sensing Techniques [15]

Table 2: Advantages and Disadvantages of Spectrum Sensing Techniques [8]

In this paper we used energy detection technique.


5. Energy Detection Technique:

Energy detector is the best spectrum sensing method when no priori information about the primary user signal is available. The energy detector block diagram is presented in Fig. 7 [11]. In the beginning, a secondary user scans a certain spectrum band which is licensed for a primary user, and then the received signal is converted to the digital domain by means of an A/D converter. Fast Fourier Transform (FFT) is an important stage, which is used to obtain the spectral components of the received digital signal. The signal energy can be obtained if the summation of the squared spectral components of the signal is averaged over certain time interval. The calculated energy is compared to a threshold value to decide if the primary user is present or absent. The primary user is present, if the received energy is more than the threshold value.

Fig. 7.The block diagram of the digital energy detector.

6. Simulation process:

Simulation process are followed following parameter

�Initialization- Initialize the 7 Carrier Frequency Bands for Users and also initialize Message Frequency and the Sampling Frequency.

� Modulation- Modulates user data over the respective frequency band using amplitude modulation

� Adder- Addition of all the modulated signals to create a carrier signal

� Period gram- For estimation of the power spectral density.

� Allocation of unused slot- When a new User arrives he is allotted to the first spectral hole.

� Emptying a slot-If all the slots are engaged ask user to empty a specific slot.

� Addition of noise- Amount of Noise to be added.

� Attenuation- Percentage of Attenuation Required


7. Simulation Result & Discussion:

In the results shown below we used MATLAB (R2010a) using Digital implementation of

energy Detector as FFT.In this allocation system we assumed seven primary users. Where we

provided first two and fifth primary users that’s present, while 3rd,4th, 6th, 7th are not present.

After assuming first primary users the next step of the signal of energy on the slot looks

decreasing. That was determined by the energy detection of FFT and also detected in the hole

of following spectrum. At the first step of run the program if first primary user will present,

definitely the secondary user space will be vacant.

Here this all seven primary users have the carrier signal frequency that we are using. They

are 2MHz, 4MHz, 6MHz, 8MHz, 10MHz, 12MHz, 14MHz. The sampling frequency must be

used. Our sampling frequency is 30MHz and the using rate of frequency spectrum on the

slot carries about 85% of sampling value.

The power spectral density of signal is calculated by its predefined value to determined

presence of primary users.Fig. (5.1) Here in the first step to run the program .Where we

assumed first two and fifth primary user, that’s present. And 3rd, 4th, 6th, 7th is not present.

The users are also called as input of the signal.

By assigning these three users in the slot

Allocated Spectrum Spectrum

Band Holes

Fig.5.1 (Used 1st, 2nd and 5th), unused bands

(3rd, 4th, 6th and 7th)

Here spectrum hole of slot can automatically

assign it to secondary user.

Now the Spectral gaps added the secondary

users it to the Spectral Holes in (fig.5.2, fig.5

3and fig.5.4)

2nd spectrum gap is filled Left over

By secondary User 2spectrum gap

Fig5.3 2nd unused gap assigned to

secondary user2


Spectrum gap filled by Left over

Secondary user 1 Spectrum gaps

Fig.5.2 1st unused band added to Secondary

user 1

Fig.5.8 Attenuation=22%

3rd Spectral Gap filled by Last Spectral

Secondary User 3 Hole Left

Fig.5.4 3rd unused gap added it to

Secondary user 3

Now there also the spectrum hole

assigned a secondary user.

Fig.5.5 finally all Spectrum bands are in

use And finally the vacant area (Spectrum hole) added it to the secondary user 4(Fig.5.5). Now all of seven users are present in the slot. In the Fig.5.1 the signal of the cognitive radio spectrum is displayed. Slot peak


Fig.5.6 SNR=11dB

Fig.5.7 SNR=17dB

area is allocated for these users, who is present and the decreasing area is located by spectrum hole. The next figure after assigning secondary user 2 there also peaks the signal of spectrum. Continuously (fig.5.3, fig.5.4, and fig.5.5) where the secondary users 3, 4, 5 are added by the spectrum hole, there also peaks the spectrum on the slot. Now the allotment is fulfilled by the Cognitive Radio Network of this experiment. After allotting the secondary users we used the Signal to Noise Ratio as SNR 11dB and 17 dB.Fig.5.6 and Fig.5.7the figure are shown below to display a signal with signal to Noise Ratio.

Fig.5.9 Attenuation 26%

We also have to attenuate the received

signal. Our attenuate percentage values

are 22% and 26%. Fig.5.8& Fig.5. 9.

8. Conclusion:

In this experiment the allocation system of cognitive radio spectrum, we used MATLAB (R

2010 a) for our simulation and also used energy detector FFT for sensing the spectrum. Our

simulation results showed how cognitive radio works in a changing frequency. Here we used

seven primary users and all the secondary users are added by the spectral gaps that are

registered. We used signal to noise ratio SNR as 11 db and 17 db also add the attenuation

percentage as 22% and 26% for showing the signal. Our main focus as that the cognitive

radio can be demonstrated successfully in the changing frequency.


References:

1. Ian F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey, Computer Networks, Vol. 50, pp. 2127- 2159, May 2006.

2. B. Wang, and K. J. Ray Liu, Advances in Cognitive Radio Networks : A Survey, IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No.1, pp. 5-23, February 2011.

3. FCC, "Spectrum Policy Task Force Report," vol.ET Docket No. 02-155, November 2002.

4. Rehan Ahmed &YasirArfatGhous, “Detection of vacant frequency bands in Cognitive Radio,” Blekinge Instituteof Technology May 2010.

5. F.I. Akyildiz, W.Y. Lee, M.C. Vuran and S. Mohanty, “A Survey on Spectrum Management in Cognitive Radio Networks,” in IEEE Communications Magazine, 2008, pp. 40- 48.

6. D.Manish “Spectrum Sensing in Cognitive Radio: Use of Cyclo-Stationary Detector,” National Institute of Technology Rourkela, Orissa-769008, India May 2012 page no.7.

7. C. Cordeiro, K. Challapali, D. Birru, and S. Shankar, “IEEE 802.22: An Introduction to the first worldwide wireless standard based on cognitive radios,” Journal of Communications.Vol 1, no. 1, pp.38-47. April 2006.

8. A. Sahai, N. Hoven, and R. Tandra, “Some fundamental limits on cognitive radio,” in Proc. Allerton Conf. on Communications, Control, and Computing (Monticello), Oct.2004.

9. S. Sengupta, S.Brahma, M.Chatterjee, S.Shanker,“Enhancements to cognitive radio based IEEE802.22 air interface”, Proceedings of International conference of Computing, pp- 35-43, January 2003.

10. K.Prabhjot, U.Moin, K.Arun “Cognitive Radios: Need, Capabilities, Standards, Applications and Research Challenges,” International Journal of Computer Applications (0975 – 8887) Volume 30– No.1, September 2011.

11. A. Bansal, and M. R. Mahajan, Building Cognitive Radio System using MATLAB, International Journal of Electronics and Computer Science Engineering, Vol. 1, No. 3, pp. 1555-1560, 2012.

12. V. Sonmezer, M. Tummala, and D. Jenn, Cooperative Wideband Spectrum Sensing and Localization using Radio Frequency Sensor Networks, thesis, Naval Post Graduate School, Monterey, California. pp. 1-91, September 2009.

13. H. Hu, Cyclostationary Approach to Signal Detection and Classification in Cognitive Radio Systems, Beijing University of Posts and Telecommunication P. R. China, pp. 51-76, 2009.

14.C. Xiao, and A. Kataria, Cognitive Radios-Spectrum Sensing Issues, Thesis, Faculity of The Graduate School at The University of Missouri-Columbia, pp. 1-45, December 2007.

15. J. Gyanendra Prasad, N.SeungYeob and K. Sung Won, Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends Yeungnam University, 214-1 Dae-dong, Gyeongsan-si, Kyongsan 712-749, Gyeongsangbuk-do, Korea.

----------

investigation of cognitive radio system using matlab

Documents