Apr14

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014

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IJITCE PUBLICATION

International Journal of Innovative Technology & Creative Engineering

Vol.4 No.4

April 2014

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From Editor's Desk

Dear Researcher, Greetings! Research article in this issue discusses about Peristaltic MHD Flow of a Jeffrey. Let us review research around the world this month; Clothes with hidden sensors act as an always-on doctor. Everyday clothes with invisible sensors woven in can monitor your vital signs. Future designs could tell you – or your doctor – when something is amiss. HEY smarty pants. Your underwear could soon tell if you are falling ill before you know it yourself, notify others if you've fallen over or help doctors diagnose and treat diseases. Clip-on sensors or wristbands can already monitor a wearer's vital signs, such as activity levels and sleep patterns. But the rigid form of these devices limits what signals they can pick up and they won't work if you forget to wear them. Wire up hives to keep bees happy and healthy. With the help of Open Source Beehives, a do-it-yourself apiary kit, you can build a hive that encourages healthy bees. The hive comes with a sensor system that collects data so that you can keep an eye on the bees in real time.Apart from keeping hives happy, they hope to collect enough data to shed light on colony collapse disorder, which has devastated beehives since 2006, but whose cause remains mysterious. An electric-powered light aircraft took to the skies over the vineyards of Bordeaux, France. It was only a small, two-seater plane but the technologies that made the flight possible could lead to a new class of hybrid airliners. One day, the plane you board to go on holiday might be flying using cleaner, greener electric power. Called the E-Fan, the quiet, sleek carbon-fibre plane is the work of Airbus, the French plane-maker. With two 65-kilogram lithium battery packs hidden in its wings, each driving a 30-kilowatt electric motor, the E-Fan cruises at 185 kilometres per hour and flies for an hour. While it's not going to win any speed or endurance prizes, it's the first step in a development programme that could lead to much bigger electric planes – with next generation, high-power lithium-air batteries and superconducting motors. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


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Contents

Implementation of Cyclostationary Feature Detection & Energy Detection Methods for Spectrum Sensing in Cognitive Radio by Abhijeet A. Chincholkar, Chaitali H. Thakare ……………………………………………….[199]


199 www.ijitce.co.uk

Implementation of Cyclostationary Feature Detection & Energy Detection Methods for

Spectrum Sensing in Cognitive Radio Abhijeet A. Chincholkar1, Chaitali H. Thakare2

Asst. Professor, Electronics & Telecommunication Engineering Department, JCOET, Yavatmal, Mh., India 1.

UG Student, Electronics & Telecommunication Engineering Department, JCOET, Yavatmal, Mh., India2.

Abstract— This paper aims to research and focus

on spectrum sensing in Cognitive Radio which is a

recently introduced technology. It helps to increase

the spectrum efficiency in cognitive radio.

Increasing efficiency of the spectrum usage is a

need of an intrinsic result of rapidly increasing

wireless users and also the conversion of voice

oriented applications to multimedia applications.

Static allocation of the frequency spectrum does

not needs to current wireless technology where as

a dynamic spectrum usage is required for wireless

networks. Cognitive radio is considered as a

promising candidate to be employed in such

systems as they are aware of their operating

environments and having ability to adjust their

parameters. Cognitive radio can sense the

available spectrum and detect the idle frequency

bands. The secondary users can be allocates

those bands which are not used by primary users.

In order to avoid this interference in between

primary user by secondary user spectrum sensing

is to be needed. There are several spectrum

sensing techniques proposed in literature for

cognitive radio based systems. This work

approaches for energy detection and

Cyclostationary feature detection based spectrum

sensing systems for cognitive radios in wireless

communication channels.

Key words — Cognitive Radio, Feature Detection,

Energy Detection, Reconfiguration.

I. INTRODUCTION

Wireless communication systems have been widely and

successfully deployed all over the world. Day by day,

upper layer protocols demand high speed wireless

access with very low delay requirements for

applications in data, voice, video and other high

bandwidth usage multimedia applications. However,

radio spectrum band available to serve wide variety of

all these emerging applications is strictly limited.

Regulatory bodies licensed radio spectrum,

implementing strict limitations on operators and

manufacturers protecting radio resource and licensed

users. This command and control nature of regulations

limits access of radio resource which is more

important problem than physical scarcity of spectrum.

Further it is discovered that, some frequency bands are

largely underutilized most of the time or partially

occupied, even in revenue rich urban areas. Cognitive

radio was proposed a mechanism for efficient use such

a free bands by exploiting its availability by cognitive

users.

In order to complete these cognitive tasks in cognitive

radio network, CU must perform additional tasks than

normal wireless user. Detection of spectrum holes is

called spectrum sensing. Spectrum sensing aims to

determine spectrum availability and presence of

licensed users. Such major task to perform by CUs,

Recent literature proposes three techniques to detect

presence of spectrum holes that are Energy Detection

and Cyclostationary feature detection. Energy detection

compares the signal energy received in a certain

frequency band to properly set decision threshold. If

signal energy lies above threshold, band is declared to

be busy. Otherwise band is supposed to be idle and

could be accessed by CR users. Another detection

method used in literature is Cyclostationary feature

detection which depends on fact that modulated signals

are generally coupled with sine wave carriers, pulse

trains, repeating spreading, hopping sequences or cyclic



prefixes which result in periodicity and their

statistics, mean and autocorrelation, exhibit periodicity

in wide sense. This periodicity trend is used for

analyzing various signal processing tasks such as

detection, recognition and estimation of received

signals.

II. COGNITIVE RADIO

Cognitive Radio is a system model for wireless

communication. It is built on software defined radio

which an emerging technology is providing a platform for

flexible radio systems, multiservice, multi-standard,

multiband, reconfigurable and reprogrammable by

software for Personal Communication Services. It uses

methodology of sensing and learning from environment

and adapting to statistical variations in real time.

Network or wireless node changes its transmission or

reception parameters to communicate efficiently

anywhere and anytime avoiding interference with

licensed or unlicensed users for efficient utilization of

radio spectrum. Cognitive modules in transmitter and

receiver must work in a harmonious manner which is

achieved feedback channel connecting them. Receiver

is enabled to convey information on performance of

forward link to transmitter. Thus CR by necessity is an

example of feedback communication system. Cognitive

Radio System Classified according to operational area.

Cognitive Radio classified in multiband system which is

supporting more than one Frequency band used by a

wireless standard (e.g., GSM 900, GSM 1800, GSM

1900), a multi-standard system that is supporting more

than one Standard which works within one standard

family (e.g. UTRA FDD, UTRA-TDD for UMTS) or

across different networks (e.g., DECT, GSM, UMTS,

WLAN), multi-service system which provides different

services (e.g. telephony, data, video streaming) and

multi-channel system that supports two or more

independent transmission and reception channels at

same time.

Fig.1 General Cognitive Radio Cycle

III. Problem and Proposed Solution

In future rapid growth of wireless communications, many

exciting technologies exist that will require more

spectrum. In recent days large portion of radio spectrum

is not used for significant periods of time. Thus, lot of

spectrum holes in frequency bands are not utilized these

frequencies in all times by license owner. Figure shows

very low utilization of spectrum from 3-6 GHz.

Most of unlicensed spectrums are heavily accessed by

users and have high spectrum utilization according to

possibility of open access. Observations lead to key idea

where spectrum utilization can be drastically increased

by allowing secondary users to access spectrum holes

that are unutilized by primary user at certain time and

space. Cognitive radio has been proposed to achieve

such dynamics. Cognitive radio senses spectral

environment over wide frequency band and exploits this

information to opportunistically provide wireless links

that can best meet demand of user, but also of its radio

environments.

Fig.2:-Utilization of Spectrum from 3-6 GHz

Cognitive-radio has two important functionalities such as

spectrum sensing and adaptation. Secondary terminal

first senses spectrum environment in order to learn

frequency spectrum unoccupied by primary users. Once

such spectrum hole is found, secondary terminal adapts

its transmission power, frequency band, modulation, so

that it minimizes interference to primary users. Even

after starting transmission, secondary terminal should

detect or predict appearance of primary user so that it

makes spectrum available for primary user. Basically,

primary users should not change their communication

infrastructure due to these operations. Thus, these

sensing and adaptation of secondary users must be

done independently of primary users. Thus, cognitive

radio allows users to utilize frequency band more

densely in time and space, thereby leading to a drastic



increase of the total spectrum efficiency. So Cognitive

radio requires innovative and unprecedented techniques

in order to sense and adapt to the spectrum.

IV. SPECTRUM SENSING

Spectrum sensing for cognitive radio is still ongoing

development and technique for primary signal detection.

Most distinguished features of cognitive radio networks

is having ability to switch between radio accesses

technologies, transmitting in different parts of radio

spectrum as idle frequency band slots arise. Cognitive

radio network are assumed to be secondary users will

also need to coexist with primary users, which have right

to use spectrum and thus must have not guarantee to

interfere by secondary users. Fundamentally, spectrum

sensing device gives general idea on medium over

entire radio spectrum. This allows cognitive radio

network to analyze all degrees of freedom (time,

frequency and space) to predict spectrum usage.

Spectrum sensing is based on a well-known technique

called signal detection. Signal detection described as

method for identifying presence of signal in noisy

environment. Spectrum sensing is major task which is

performed by cognitive radio as shown in figure it gives

awareness about present spectrum usage by monitoring

primary users in particular geographical location and

frequency bands. This enables detection of present

spectrum holes in available frequency range.

4.1 Spectrum Sensing Analysis:

Through spectrum sensing analysis, CR can detect

spectrum white space as illustrated in Figure 2 i.e., a

portion of frequency band that is not being used by

primary users, and utilizes spectrum. When primary

users start using licensed spectrum again, CR can

detect their activity through sensing, to prevent from

harmful interference by secondary user.

Fig.3 Spectrum Holes

4.2 Spectrum management and handoff:

After recognizing spectrum white space by sensing,

spectrum management and handoff function of CR

enables secondary users to choose best frequency band

and hop among multiple bands according to time varying

channel characteristics to meet various Quality of

Service (Qi’s) requirements. For instance, when primary

user reclaims his/her frequency band, secondary user

that is using licensed band can direct his/her

transmission to other available frequencies, according

to channel capacity determined by noise and

interference levels, path loss, channel error rate, holding

time, and etc.

4.3 Spectrum allocation and sharing:

In dynamic spectrum access, secondary user may share

spectrum resources with primary users, other secondary

users, or both. Hence, good spectrum allocation and

sharing mechanism is critical to achieve high spectrum

efficiency. Since primary users own spectrum rights,

when secondary users co-exist in a licensed band with

primary users, interference level due to secondary

spectrum usage should be limited by a certain threshold.

When multiple secondary users share a frequency band,

their access should be coordinated to alleviate collisions

and interference.

V. CYCLOSTATIONARY SENSING

Most challenging task in designing and implementation

of cognitive radio is spectrum sensing. By using

spectrum sensing, cognitive radios can adapt

themselves to eternal wireless spectrum environment.

An effective method used for signal detection is

Cyclostationary sensing. A modulated radio signal is

considered as Cyclostationary process and statistical

properties of a Cyclostationary process vary periodically

over time. Autocorrelation function is cyclic processes

with a periodicity T. If we consider a signal from primary

user as:

X (t) = s (t) + n (t)…………Equation 1

Where n(t) represents additive white Gaussian noise,

while s(t) is transmitted signal, then s(t) has some visible

and distinct properties which can be exploited by

sensing Cyclostationary properties, for example, by

differentiating it from noise. These properties are: carrier

frequency, modulation type, symbol duration and so on.

Auto- correlation and mean function of received signal



x(t), is periodic signal with period T, where T is

expressed as reciprocal of carrier frequency. Spectral

correlation and auto correlation functions can be used to

extract weak signals from noise. They can be used to

find out presence or absence of random signal in

presence of other signals having different modulation

schemes. Spectral correlation function is special

characteristics exhibited by modulated signal. This can

be used for various signal processing tasks like

detection, classification and synchronization. Especially

for signals under during and interference, whereas

autocorrelation function is cross correlation of signal with

itself. Commonly, analysis of stationary random signal is

done using autocorrelation function and power spectral

density as mathematical tool. However, Cyclostationary

signals reflect correlation between distinct spectral

components because of periodicity, so analogy of

autocorrelation function is implemented to define

spectral correlation function. In Cyclostationary

spectrums different data length can be taken. It will be

easier to detect signal of primary users which use longer

transmission length rather than using shorter

transmission lengths. Main purpose of spectral

correlation function is to separate noise energy from

modulated signal.

5.1 Cyclostationary Feature Detection

It uses inbuilt features in primary user’s

waveform for detection. Hence, it is computationally

complex detector. Flow chart for implementation of

Cyclostationary Feature Detector is shown in below

Figure. Let r (t) is received signal which we have to pass

from Cyclostationary feature detector. Procedure of

Cyclostationary Feature Detection is as:

Step 1: First take Fourier of received signal by using ‘fft’

function. R=fft(r)

Step 2: Multiple r with complex exponential. As

multiplication with complex exponential in time domain is

equivalent to frequency shift in frequency domain.

XT=r.*exp (j*2*pi *shfT);

Step 3: Correlate XT with R XY=xcorr (XT, R);

Average over time T: Pt= fft (XY).*conj (fft (XY))

Step 4: On experimental basis when results at low and

high SNR are compared then threshold is set to 1<λ<5.

Step 5: Finally output of integrator, pt is compared with a

threshold value λ to decide whether primary user is

present or not.

Step 6: Now if primary user is present then we can find

features of primary signal like operating frequency and

modulation technique.

Flow chart and procedural steps gives us idea of

overall process, which yields required results for

Cyclostationary feature detection when implemented in

Matlab Simulink, Matlab Simulink provides an

environment for designer in which physical system can

modeled to evaluate results. Following steps shows

designing steps of building blocks of Cyclostationary

feature detection.

VI. ENERGY DETECTION

The simplest detection technique for spectrum

sensing is Energy Detection. Energy detector measures

the energy received from primary user during the

observation interval. If energy is less then certain

threshold value then it declares it as spectrum hole. Let

r(t) is the received signal which is generated by primary

user Simulink model, now this we have to pass from

energy detector. The procedure of the Energy Detector

is as follows.

VII. COMPARISON OF TRANSMITTER DETECTION

TECHNIQUES

Now consider some metrics on the basis of

which we can compare transmitter detection techniques.

There are three metrics on the basis of which we can

compare these techniques.

Fig.4:- Comparison Of Transmitter Detection

Techniques

7.1 Sensing Time

During communication cognitive radio continuously

sense radio environment for spectrum holes and CR

can’t transmit and sense at same time. Therefore we

need sensing time as small as possible.

A) Comparing Energy Detector and Cyclostationary feature detection, Cyclostationary feature detection requires longer sensing time to achieve good results.

B) Cyclostationary Feature Detection is also non-coherent technique which makes it superior to Matched Filtering and energy detection.

Cyclostationary Feature Detection technique is

computationally very complex and it takes long observation time for sensing.



Fig.5:- Flowchart of Cyclostationary Fig.6:- Flow chart of Energy Detection

Feature Detection

7.2 Detection Sensitivity

Energy detector is better under noisy environment.

Major drawback of energy detector is that it is unable to

differentiate between source of received energy i.e. it

cannot distinguish between noise and primary user. So

this makes it susceptible technique when there are

uncertainties in background noise power, especially

at low SNR.

Cyclostationary is unable to detect primary user but

energy detector still detect it. When there is no primary

user present even then energy detector detects primary

user at low SNR, which makes energy detector

unreliable technique under low SNR values. Hence,

when we have no prior knowledge about primary user’s

waveform then best technique is Cyclostationary feature

detection.



7.3 Ease for implementation:

Advantage of energy detector is low cost and simple to

implement, which makes it good candidate for

spectrum sensing in cognitive radio networks.

Cyclostationary Feature Detection is also very complex

technique which takes high cost and high computational

complexity.

Table 1. Summary of comparison of Transmitter

Detection Techniques

VIII. MINIMIZED SENSING TIME FOR DETECTION

To minimize sensing time, if prior knowledge about

primary user’s waveform is known at receiver end

then under good SNR we can sense spectrum

accurately by using matched filter. But if prior

knowledge of primary user is not known then we should

consult with energy detector for detection of primary

user. In this case computation time is increased to

achieve reliability. Further if energy detector doesn’t

give accurate result then Cyclostationary feature

detection comes into play. In this case it takes too much

computation time to achieve reliability. This is worst

case of this algorithm. Best case for this algorithm is that

if matched filter provides indication about presence or

absence of primary user.

IX. CONCLUSION AND FUTURE WORK

This work helps to detect primary users in cognitive

radio networks. It also fulfill requirement of a spectrum

sensing system and its real time processing and ability

for decision making. The proposed methodology has

been implemented in MATLAB. Its implementation can

be done on FPGA kit or DSP processor.

First all the transmitter detection techniques are

compared on the basis of three metrics: Sensing Time,

Detection Sensitivity and ease of implementation. By

comparing these techniques it is concluded that

Cyclostationary feature detection gives best results but

take long computation time as compared to spectrum

sensing technique.

Most of the researchers work on spectrum sensing

which is mainly focused on reliable sensing to meet the

regulatory requirements. One of the important areas

for the research is to focus on user level cooperation

among cognitive radios and system level cooperation

among different cognitive radio networks to overcome

the noise level uncertainties. Another area for research

is cross layer communication in which spectrum sensing

and higher layer functionalities can help in improving

quality of service (QoS).

Sr. No.

Type Energy

Detection

Cyclostationary feature Detection

detection 1 Sensing Time More Most

2 Simple to Implement

Yes No

3 Performance

under

Noise

Poor Good

4 Prior Knowledge

Required No No



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