Cognitiveradio

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Spectrum Sensing in Cognitive Radio

Dr.M.Jayasheela, Professor/ECEKIT-Kalaignar Karunanidhi Institute of Technology

Introduction Introduction

Cyclostationary feature detection method for Spectrum Sensing in Cognitive Radio

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COGNITIVE RADIOCOGNITIVE RADIO

• Cognitive radio is a smart radio which is capable of identifying an idle spectrum, and to quickly tune to that frequency range for the transmission of its own signals.

• Cognitive radio is based on Software Defined Radio(SDR)

• Term coined by Sir Joseph Mitola III

MOTIVATION: SPECTRUM SCARCITYMOTIVATION: SPECTRUM SCARCITY

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Why spectrum scarcity? Due to inefficient usage rather than a

physical shortage

Cognitive approach: Spectrum utilization through opportunistic

use of available frequencies

A spectrum hole: A band of frequencies assigned to a primary

user, but, at a particular time and specific geographic location, the band is not being utilized by that user.

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COGNITIVE RADIO SCENARIOCOGNITIVE RADIO SCENARIO

Secondary User Network

Terminals with sensing and

communication capabilities

Primary UserNetwork

BLOCK DIAGRAMBLOCK DIAGRAM

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CYCLOSTATIONARY FEATURE DETECTION CYCLOSTATIONARY FEATURE DETECTION

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* Takes advantage of cyclostationarity properties of the received signals to detect primary user transmissions.

* Cyclic spectral correlation function (SCF) is used for detecting presence of signals.

* Cyclic SCF of received signal is given by:

CYCLOSTATIONARY FEATURE DETECTION: FLOW CHART

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SPECTRUM SENSING: A MAJOR PROBLEMSPECTRUM SENSING: A MAJOR PROBLEM

• Shadowing: Individual sensing node blocked by an obstacle

• Multipath fading: The signals from the PU that travel different paths may not add coherently at the receiver.

Solution: Cooperative Sensing

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COOPERATIVE SPECTRUM SENSINGCOOPERATIVE SPECTRUM SENSINGMultiple cognitive users can cooperate to

conduct spectrum sensing.

Advantages:Reduced Hidden node problem Increase in agilityReduced false alarmsMore accurate signal detection

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TerminologyTerminology

Local & Global Decisions

AND rule for Decision fusion

Probability of Detection ,Pd

Probability of Missed detection ,Pmd=1-Pd

Probability of False Alarm ,Pfa

Receiver operating characteristics : Pfa vs. Pd

UWB channel model

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Spectral Correlation Function when Primary User Spectral Correlation Function when Primary User is absent:is absent:

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-4000 -3000 -2000 -1000 0 1000 2000 3000 4000-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1Cyclic SCF

Am

plitu

de

frequency in Hz

Spectral Correlation Function when Primary User is Spectral Correlation Function when Primary User is present:present:

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-4000 -3000 -2000 -1000 0 1000 2000 3000 40000

1

2

3

4

5

6

7

8x 10

10 Cyclic SCF

Am

plitu

de

frequency in Hz

Cyclic SCF when Primary User signal is BPSK modulatedCyclic SCF when Primary User signal is BPSK modulated

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-4000 -3000 -2000 -1000 0 1000 2000 3000 40000.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6x 10

8 Cyclic SCF

Am

plitu

de

frequency in Hz

Cyclic SCF when Primary User signal is QPSK modulatedCyclic SCF when Primary User signal is QPSK modulated

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-4000 -3000 -2000 -1000 0 1000 2000 3000 40001.5

2

2.5

3

3.5

4

4.5

5

5.5x 10

8 Cyclic SCF

Am

plitu

de

frequency in Hz

CONCLUSIONCONCLUSION

The spectrum sensing using the cyclostationary detection method is more reliable and efficient. Its noise immunity is also immensely high. Also the modulation scheme employed by the primary user at the transmitter section can be accurately guessed using this method. Cooperative spectrum sensing helps to overcome the multi path fading and shadowing effects . Cooperative method will also improve the cognitive user’s agility and usability. By incorporating UWB, the benefits of the cognitive radio is applied onto many of the indoor applications.

For example in a hospital environment.

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REFERENCESREFERENCES[1] Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks,

Saman Atapattu, Student Member, IEEE, Chintha Tellambura, Fellow, IEEE, and Hai Jiang, Member, IEEE, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 10, NO. 4, APRIL 2011

[2] Energy Efficient Ultra Wideband Signaling For Cooperative Sensing In Cognitive Radio, Daniel Bielefeld, Gernot Fabeck, Milan Zivkovic, Rudolf Mathar, Institute For Theoretical Information Technology, Rwth Aachen University, D-52056 Aachen, Germany , (2011).

[3] A Low-Complexity Cyclostationary-Based Spectrum Sensing for UWB and WiMAX Coexistence With Noise Uncertainty Andrea Tani and Romano Fantacci, Fellow, IEEE.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 59, NO. 6, JULY 2010

[4] Cognitive UWB system and cognitive UWB data communication method UNITED STATES PATENT, Hwang et al., Patent No.: US 7864830 B2, Date of Patent: Jan.4, 2011

[5] Cooperative Sensing for Primary Detection in Cognitive Radio, Jayakrishnan Unnikrishnan, Student Member, IEEE, and Venugopal V. Veeravalli, Fellow, IEEE, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 2, NO. 1, FEBRUARY 2008

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[6] A Survey of Spectrum Sensing Algorithms for Cognitive Radio ApplicationsTevfik Y¨ucek and H¨useyin Arslan, IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 11, NO. 1, First Quarter 2009

[7] Spectrum Sensing Techniques In Cognitive Radio Networks: A Survey, Mansi Subhedar and Gajanan Birajdar, Sies Graduate School Of Technology, Navi Mumbai, India. International Journal Of Next-Generation Networks (Ijngn) Vol.3, No.2, 2011.

[8] Spectrum Sensing Methods And Dynamic Spectrum Sharing In Cognitive Radio Networks: A Survey, Danda B. Rawat and Gongjun Yan, Department Of Electrical And Computer Engineering, Old Dominion University, Norfolk, USA, International Journal Of Research And Reviews In Wireless Sensor Networks Vol. 1, No. 1, 2011, Copyright © Science Academy Publisher, United Kingdom.

[9] Cooperative Ultra-wideband and Communication System, Baptiste Castel, Kungliga Tekniska, Hgskolan, Stockholm May 2009

[10]Cooperative Spectrum Sensing in Cognitive Radio,Part I: Two User Networks,Ghurumuruhan Ganesan and Ye (Geoffrey) Li.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 6, NO. 6, JUNE 2007

[11] A Channel Model for Ultrawideband Indoor Communication Jeffrey R. Foerster, Marcus Pendergrass and Andreas F. Molisch, TR-2003-73 November 2003MERL – A MITSUBISHI ELECTRIC RESEARCH LABORATORY, http://www.merl.com

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