INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
UK: Managing Editor
International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249
USA: Editor
International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626
India: Editor
International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 17/14 Ganapathy Nagar 2nd Street Ekkattuthangal Chennai -600032 Mobile: 91-7598208700
www.ijitce.co.uk
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
IJITCE PUBLICATION
International Journal of Innovative Technology & Creative Engineering
Vol.4 No.4
April 2014
www.ijitce.co.uk
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Editorial Members
Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.
Review Board Members
Dr. Paul Koltun
Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33,
Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia
Dr. Zhiming Yang MD., Ph. D.
Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA
Dr. Jifeng Wang
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA
Dr. Giuseppe Baldacchini
ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY.
Dr. Mutamed Turki Nayef Khatib
Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University
(Kadoorie), Tul Karm, PALESTINE.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Dr.P.Uma Maheswari
Prof & Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.
Dr. T. Christopher, Ph.D.,
Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.
Dr. T. DEVI Ph.D. Engg. (Warwick, UK),
Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.
Dr. Renato J. orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil
Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France
Y. Benal Yurtlu
Assist. Prof. Ondokuz Mayis University
Dr.Sumeer Gul
Assistant Professor,Department of Library and Information Science,University of Kashmir,India
Dr. Chutima Boonthum-Denecke, Ph.D
Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil
Dr. Lucy M. Brown, Ph.D.
Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666
Javad Robati
Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran
Vinesh Sukumar (PhD, MBA)
Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc.
Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE
HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India
Dr. S. B. Warkad
Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India
Dr. doc. Ing. Rostislav Choteborský, Ph.D.
Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. Paul Koltun
Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33,
Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168
DR.Chutima Boonthum-Denecke, Ph.D
Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688
Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil.,
Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra.
(India).
Dr. Ing. Rostislav Chotěborský,ph.d,
Katedra materiálu a strojírenské technologie, Technická fakulta,Česká zemědělská univerzita v Praze,Kamýcká 129, Praha 6, 165 21
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d,
Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin Ashokrao B.sc,M.Sc
Lecturer in Yeshwant Mahavidyalaya Nanded University
Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG
Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai
Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed.
Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41
Dr. David Batten
Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale,
Vic. 3195,AUSTRALIA
Dr R C Panda
(MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India
Miss Jing He
PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329
Jeremiah Neubert
Assistant Professor,Mechanical Engineering,University of North Dakota
Hui Shen
Mechanical Engineering Dept,Ohio Northern Univ.
Dr. Xiangfa Wu, Ph.D.
Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY
Seraphin Chally Abou
Professor,Mechanical & Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 Ordean Court,Duluth, Minnesota 55812-3042
Dr. Qiang Cheng, Ph.D.
Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive,
Carbondale, IL 62901
Dr. Carlos Barrios, PhD
Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America
Y. Benal Yurtlu
Assist. Prof. Ondokuz Mayis University
Dr. Lucy M. Brown, Ph.D.
Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666
Dr. Paul Koltun
Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials CSIRO Process Science & Engineering
Dr.Sumeer Gul
Assistant Professor,Department of Library and Information Science,University of Kashmir,India
Dr. Chutima Boonthum-Denecke, Ph.D
Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Dr. Renato J. Orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil
Dr. Wael M. G. Ibrahim
Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road -
Suite 100,Virginia Beach, VA 23462
Dr. Messaoud Jake Bahoura
Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504
Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D.,
Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.
Dr. P. Kamakkannan,M.C.A., Ph.D .,
Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.
Dr. V. Karthikeyani Ph.D.,
Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.
Dr. K. Thangadurai Ph.D.,
Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India.
Dr. N. Maheswari Ph.D.,
Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India.
Mr. Md. Musfique Anwar B.Sc(Engg.)
Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.
Mrs. Smitha Ramachandran M.Sc(CS).,
SAP Analyst, Akzonobel, Slough, United Kingdom.
Dr. V. Vallimayil Ph.D.,
Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India.
Mr. M. Moorthi M.C.A., M.Phil.,
Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India
Prema Selvaraj Bsc,M.C.A,M.Phil
Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode
Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF.,
Assistant Professor, Department of Computer Science, Government Arts College, Salem, India.
Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d
Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India
Muhammad Javed
Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland
Dr. G. GOBI
Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007
Dr.S.Senthilkumar
Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),Universiti Sains Malaysia,School of Mathematical Sciences,
Pulau Pinang-11800,[PENANG],MALAYSIA.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Manoj Sharma
Associate Professor Deptt. of ECE, Prannath Parnami Institute of Management & Technology, Hissar, Haryana, India
RAMKUMAR JAGANATHAN
Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India
Dr. S. B. Warkad
Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India
Dr. Saurabh Pal
Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India
Manimala
Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post,
Kottayam Dt. Kerala -686579
Dr. Qazi S. M. Zia-ul-Haque
Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan
Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D.
Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215
Dr. Seraphin Chally Abou
Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042
Dr. K. Kousalya
Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052
Dr. (Mrs.) R. Uma Rani
Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.
MOHAMMAD YAZDANI-ASRAMI
Electrical and Computer Engineering Department, Babol "Noshirvani" University of Technology, Iran.
Dr. Kulasekharan, N, Ph.D
Technical Lead - CFD,GE Appliances and Lighting,
GE India,John F Welch Technology Center, Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India.
Dr. Manjeet Bansal
Dean (Post Graduate),Department of Civil Engineering ,Punjab Technical University,Giani Zail Singh Campus, Bathinda -151001 (Punjab),INDIA
Dr. Oliver Jukić
Vice Dean for education, Virovitica College, Matije Gupca 78,33000 Virovitica, Croatia
Dr. Lori A. Wolff, Ph.D., J.D.
Professor of Leadership and Counselor Education, The University of Mississippi, Department of Leadership and Counselor Education, 139 Guyton
University, MS 38677
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
www.ijitce.co.uk
Contents
Implementation of Cyclostationary Feature Detection & Energy Detection Methods for Spectrum Sensing in Cognitive Radio by Abhijeet A. Chincholkar, Chaitali H. Thakare ……………………………………………….[199]
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
200 www.ijitce.co.uk
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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
201 www.ijitce.co.uk
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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
202 www.ijitce.co.uk
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.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
203 www.ijitce.co.uk
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.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
204 www.ijitce.co.uk
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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
205 www.ijitce.co.uk
REFERENCES
1.Marcos E. Castro,“Cyclostationary detection for OFDM
in cognitive radio systems”University of Nebraska-
Lincoln, [email protected].
2.Mahmood A. Abdulsattar and Zahir A. Hussein –
“Energy detection technique for spectrum sensing in
cognitive radio: a survey” International Journal of
Computer Networks & Communications September
2012,[email protected].
3.Allen Ginsberg, Jeffrey D. Poston, and William D.
Horne-“ Experiments in Cognitive Radio and Dynamic
Spectrum Access using An Ontology-Rule Hybrid
Architecture”The MITRE Corporation McLean,
whorne}@MITRE.org.
4.Dr. Mary Ann Ingram –“Smart Antenna Research
Laboratory” Guillermo Acosta August, 2000.
5.Peltola J. (2007) –“Distributed Spectrum Sensing for a
Cognitive Ultra Wideband System. University of Oulu,
Department of Electrical and Information Engineering.
6.Vesa Turunen1, Marko Kosunen1, Sami Kallioinen2,
Aarno Pärssinen2, “ Spectrum Sensor Hardware
Implementation Based on Cyclostationary Feature
Detector”.
7.David A. Clendenen “A software defined radio
testbed for research in dynamic spectrum access”
Purdue University Fort Wayne, Indiana.
8.Xiaolong Li –“Simulink-based Simulation of
Quadrature Amplitude Modulation(QAM) system”
Indiana State University ,[email protected].
9.Tevfik Yucek and Huseyin Arslan-“A Survey of
Spectrum Sensing Algorithms for Cognitive Radio
Applications”.
10.Jørgen Berle Christiansen –“Distribution Based
Spectrum Sensing inCognitive Radio
11.Samson sequeira-“energy based spectrum sensing
for Enabling dynamic spectrum access in Cognitive
radios”.
12.H. Urkowitz Energy detection of unknown
deterministic signals Proceedings of The IEEE, vol.55,
no.4, pp. 523- 531, April 1967.
13.J. Mitola III Cognitive radio integrated agent
architecture for software defined Radio Ph.D. thesis,
KTH Royal Institute of technology, Stockholm, Sweden,
2000.
14.By Artem Tkachenk –“Testbed Design for
Cognitive Radio Spectrum Sensing Experiments .
15.Reuters Business Wires; “Sharing Digital Dividend
Spectrum Could Boost French Economy by an Extra
EUR25bn” Tue May 27, 2008.
16.Zamat, H; Nassar, C.; “Introducing Software
Defined Radio to 4GWireless: Necessity,
Advantage, and Impediment ,”Journal of
Communication and Networks.2002 .
17.Mahmood A. Abdulsattar and Zahir A. Hussein-
“energy detection technique for Spectrum sensing in
cognitive radio: a survey” International Journal of
Computer Networks & Communications (IJCNC) Vol.4,
No.5, September 2012.
18.S. Ziafat, W. Ejaz, and H. Jamal, “Spectrum sensing
techniques for cognitive radio networks: Performance
analysis,” 2011 IEEE MTT-S International Microwave
Workshop Series on Intelligent Radio for Future
Personal Terminals, pp. 1-4, 2011.
19. "Sensing techniques for cognitive radio,” White
paper, SCC 41-P1900.6, 15th April 2009.
20. ECE4305: Software-Defined R a d io S ys t e m s
a n d An a l ys i s G e t t i n g S t a r t e d w i t h MATLAB,
Simulink, USRP2 Hardware and USRP2 Blocks.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.4 APRIL 2014
206 www.ijitce.co.uk
Recommended