Abstract Today, energy spectrum is in high demand as theusage of the spectrum increase day by day at a tremendous rate;there is a need to find a way to efficiently utilize these scarceresources, cognitive radio is a huge step towards this. Anenormous research papers have been published in variousinternational conferences as well as journals in the last few yearsto make cognitive radio applicable with various applications aswell as to make it better suit with various environmentalscenarios. On the other side MIMO opened the door for manyresearches in the form of high diversity and reliability achievedwithin it compared to SISO. Very much research has been noticedin each individual direction but very few approach found inliterature where both are combined & utilized. In this researchpaper cognitive radio scheme is implemented with MIMO, energyspectrum sensing is used for spectrum sensing as it is a simple andeasy to implement. The simulation results indicate MIMO-cognitive radio outcomes SISO-cognitive radio. Simulation resultshave been expressed with various modulation techniques anddetection probability and false probability are used asperformance metrics.

Index Terms Cognitive radio (CR), Multiple-Input Multiple-Output system model, Single-Input Single-output system model,Simulation.

I. INTRODUCTION

ognitive radio is an intelligent wireless communicationsystem that is being aware of its environment, learns from

it and adapts it transmission features according to variations inthe environment to maximize utilization of resources such asspectrum while ensuring good QoS. Two main entities areintroduced, namely primary and secondary users transmit andreceive signals over the licensed spectra. The secondary usersshould have the ability to measure the radio environment andintelligently exploit the unused licensed spectrum andrelinquish it when primary users are active. Key to thesuccessful operation of CR systems is to measure the wirelessenvironments over wide frequency bands and identifyspectrum holes and occupied bands. The challenge is

Jenishkumar.S.Gamit is now with Department of Electronics &Communication Engineering, G. H. Patel College of Engineering &Technology, V. V. Nagar(e-mail: jenishgamit@yahoo.in)Sameer.D.Trapasiya Department of Electronics & CommunicationEngineering, G. H. Patel College of Engineering & Technology, V. V.Nagar(e-mail: sameertrapasiya@gmail.com)

in the identification and detection of primary user signalsharsh and Noisy environments. Speed and accuracy ofmeasurement are the main metrics to determine the suitablespectrum sensing technique for CR. Accuracy of the estimationdepends on frequency resolution. Greater the frequencyresolution, more accurate the estimated power at eachfrequency. There are other important metrics too. One is theright tradeoff between the time and frequency resolutionachievable. Due to the uncertainty principle, it is not possibleto have the best frequency and time resolution at the same time[14].

The increasing demand for networks has turned spectruminto a precious resource. For this reason, there is always a needfor methods to pack more bits per Hz. A particular solutionthat has caught the researchers attention is the use of multipleantennas at both transmitter (TX) and receiver (RX). Such asystem is called a Multiple-Input Multiple-Output (MIMO)system. Advantages of MIMO systems include [12]:

Beamforming - A transmitter receiver pair can performbeamforming and direct their main beams at eachother, thereby increasing the receivers receivedpower and consequently the SNR.

Spatial diversity - A signal can be coded through thetransmit antennas, creating redundancy, whichreduces the outage probability.

Spatial multiplexing - A set of streams can betransmitted in parallel, each using a different transmitantenna element. The receiver can then perform theappropriate signal processing to separate the signals.

It is important to note that each antenna element on aMIMO system operates on the same frequency and thereforedoes not require extra bandwidth. Also, the total powerthrough all antenna elements is less than or equal to that of asingle antenna system, i.e.

1

N

kk

p P=

(1)Where N is the total number of antenna elements, pk is the

power allocated through the kth antenna element and P is thepower if the system had a single antenna element. Effectively,(1) ensures that a MIMO system consumes no extra power dueto its multiple antenna elements.

In this paper, we will use multiple antenna technique usingenergy detection sensing. We are going to simulate the resultsfor probability of detection and probability of failure andcompare these results with the results of SISO technique.

Cognitive Radio Energy Based SpectrumSensing using MIMO

Jenishkumar.S.Gamit Sameer.D.Trapasiya

C

International conference on Communication and Signal Processing, April 3-5, 2013, India

978-1-4673-4866-9/13/$31.00 2013 IEEE708

The paper is organized as follows. In section II, theliterature review is discussed. In section III, generalbackground information on MIMO and SISO is provided.Mathematical evaluation is discussed in section IV. Results arepresented in section V. Finally, section VI concludes thispaper.

II. LITERATURE REVIEWIn paper (Amna Saad Kamil, Ibrahim Khider 2008) after

several studies indicating that up to 90% of the allocated radiospectrum less than 3GHz is idle most of the time. Currentresearches are investigating different techniques of usingcognitive radio to reuse more locally unused spectrums toincrease the total system capacity. Cognitive radios have agreat potential to improve spectrum utilization by enablingusers to access the spectrum dynamically without disturbinglicensed primary radios. This paper presents some of thecognitive radio issues used to determine the effectiveness inwireless communication. These characteristics are crucialwhen applying the cognitive radios in order to determine theeffectiveness and reliability of wireless networks. Spectrummanagement, unlicensed spectrum usage, spectrum sharing,hidden node and sharing issues, security, and complexity areintroduced [10].

In paper ( D. D. Ariananda, M. K. Lakshmanan,H. Nikookar 2009) they discussed CR is an intelligent wirelesscommunication system that is cognizant of its environment,learns from it and adapts it transmission features according tothe utilization of resources such as spectrum while ensuringgood QoS. Key to the successful operation of CR systems is togauge the wireless environments over wide frequency bandsand identify spectrum holes and occupied bands. Thechallenge is in the identification and detection of primary usersignals amidst harsh and noisy environments. In this context,speed and accuracy of measurement are the main metrics todetermine the suitable spectrum sensing technique for CR.Accuracy of the estimation depends on frequency resolution.Greater the frequency resolution, more accurate the estimatedpower at each frequency. They reviewed of traditionalspectrum estimation techniques which are based on estimationthrough detection of pilots, features or energy. It is notpossible to simultaneously have best frequency and timeresolutions. A good time resolution is necessary to locatediscontinuities in the time domain signal. The time resolutioncan be compromised for segments of the signal which arestationary for long periods of time or slow varying .The theoryof wavelet stands for spectrum estimation applications.Wavelet has the properties required to dynamically tune thetime and frequency resolution with diluted versions of thewavelet and scaling functions. By taking advantage of the firstand second order derivative, the location of the frequencyboundaries of each band within the wide band of interest isfound. To conclude, the best spectrum sensing approach forcognitive radio would be the ones which offer a tradeoffbetween time-frequency resolutions with minimum complexity[14].

In paper (Luis Miguel Cortes-pena 2009), presents adetailed study of diversity for MIMO systems. The multiple

antennas allow MIMO systems to perform beamforming,spatial multiplexing .Beamforming consists of transmitting thesame signal with different gain over all transmit antennas suchthat the receiver signal is maximized. In spatial multiplexingasset of streams can be transmitted in parallel, each using adifferent transmit antenna element. The receiver can thenperform the appropriate signal processing to separate thesignals. They discuss Alamoutis space time block coding [12].In paper (Mahmood A. Abdulsattar and Zahir A. Hussein2012) they presented a DSP processor to be used as abaseband energy detector based spectrum sensing for cognitiveradio (CR) under fading channels. The proposed technique isa low complexity scheme by using two energy detectors andnot need prior information about the channel gain incomparison with other methods. The radio spectrum used forwireless communications is a finite resource. A band assignedto a primary user may be absolutely free or idle at a particulartime. As a solution for the spectrum used inefficientlyproblem, cognitive radio (CR) proposes an opportunisticspectrum usage approach in which frequency bands that arenot being used by their licensed users, also called the primaryuser (PU), is utilized by cognitive radios, also called thesecond user (SU). CR is a hybrid technology involvingsoftware defined radio (SDR). The basic concept of the SDR isthat the radio can be totally configured or defined by thesoftware like mixer, filters, modulators, demodulators. Thus,simply modifying the software, CR can completely change itsfunctionality or improve its performances, without replacinghardware. This characteristic allows adding new features toCR and improving its existing ability. They are proposing amultiple antenna sensing in a multipath fading environment.To reduce the multipath and shadowing effects of wirelesschannels, diversity reception techniques (i.e. Multipleantennas) provide promising solutions to improve BERperformances. Diversity means the providing of two or moreindependent fading paths from transmitter to receiver. Theproposed scheme has minimum hardware used. Advantage ofenergy detector technique is that it does not require knowledgeof transmitting signal of the primary user but it has thedisadvantage of requiring an accurate estimation of receivingambient noise .In urban regions where line of sight componentis often blocked by obstacles. In proposing a techniquediversity method first energy detector sums the output ofsquared signals come from multiple antennas and secondaryenergy detector summing the output comes from the firstenergy detector and compared with threshold to makingdecision [19].

III. SYSTEM MODEL

A. SISO system modelEnergy detection is an optimal way to detect primary signals

when priori information of the primary signal is unknown tosecondary users. It measures the energy of the receivedwaveform over a specified observation time.

Energy Detector simply needs a band-pass filter; an analogto digital converter, square law device and an Integrator. Firstthe input signal's bandwidth is limited to focusing through a

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band-pass filter. Then the filtered signal is squared andintegrated over an observation interval T. Finally the output ofthe Integrator is compared with a threshold to decide whether aprimary signal exists or not. The block diagram of energydetection is shown in Figure 1.

Fig. 1. SISO system model

The output of the energy detector can be given in timedomain using the block diagram:

2

1( )

N

nE x n

== (2)

Finally, this output signal E is compared to the threshold inorder to decide whether a signal is present or not in thatfrequency band [15].

E or E (3)B. MIMO system model

MIMO systems are composed of three main elements,namely the transmitter, the channel, and the receiver [12]. Inour simulation model we are going to use energy detectionsensing using multiple antenna technique. The block diagramof the proposed model is given below.

Fig. 2. MIMO system model

A signal is transmitted by transmitter1 to the receiver1 andreceiver2 which has the same frequency. To take diversityadvantage with energy spectrum sensing multiple inputmultiple output antennas (MIMO) approach can be used.

As signals receive from different antennas of MIMO inwhich signal has the maximum good response is given toenergy detector. One signal is applied to the energy detector ata time. The energy signal is compared with the threshold valuethen the decision makers that decide signal present or not. Thechannel matrix is:

1;1 1;2

2;1 2;2

h hh

h h =

(4)

IV. MATHEMATICAL EVALUATIONSignal detection can be reduced to a simple identification

problem, formalized as a hypothesis test.

1: ( ) ( ) ( )0 : ( ) ( )

H x n s n h w nH x n w n

= += (5)

Where x (n) is the received signal by secondary users, s (n) isthe transmitted signal of the primary user, h is the channelcoefficient; and w (n) is additive noise.

The output signal E in (2) is compared to the threshold inorder to decide whether a signal is present or not in thatfrequency band [5].

E > decide signal absentE < decide signal present

If the energy detection can be applied in a relay fadingenvironment the probability of false alarm Pf are given asfollows:

Probability of false alarm PFA for SISO [15]:

2,2 2

2

wFA

N

PN

= (6)

Where ( ). is the gamma function, which is defined as( ) 1

0

u tu t e dt

= , and incomplete gamma function is given by( ) 1, a t

s

a s t e dt

= [19].Probability of false alarm PFA for MIMO [15] by using CDF

may be found using the following equation:

( )( )

2* 2

1

,2

rN

j wj

FA

Nh

PN

=

=

710

The probability of detection PD is found at:

( ){ }11 22 FADP erfc erfc P X= (8)Where,

X =

V. SIMULATION RESULTIn simulation, we evaluate the simulations for QPSK, 4-

QPSK, 64-QPSK modulated using energy detection method.We evaluate the simulation for different SNR.

Fig. 3. PFA vs. SNR for MIMO

In fig. 3. We plot PFA vs. SNR using value of N= 2, 4, 8.WE can see that when the SNR is increased than theprobability of failure is decreased.

Fig. 4. Pd vs. SNR for MIMO

For energy detection method maximum SNR is required.Maximum SNR is given a better probability of detection. Inabove fig. 4. We can see that when the SNR is increase it givesa good probability of detection.

Fig. 5. PFA vs. SNR for SISO

In above fig. 5. PFA vs. SNR plot is given for SISO. Plot ofPd vs. The SNR for SISO is given in fig. 6.

Fig. 6. Pd vs. SNR for SISO

We can see from fig. 4. and fig. 6. MIMO gives good resultthan SISO.

VI. CONCLUSION AND FUTURE WORKIt has been found that if cognitive radios used with MIMO

than it outperform the SISO-cognitive radio. Results indicateMIMO-cognitive radio gives better performance than the SISOwith cognitive radio in terms of reduced false alarmprobability and improved detection probability. MIMOprovides the diversity advantage compared to SISO & henceimprove performance. Simulation results have been found invarious modulation techniques & better results is obtained ineach case. This approach used here for the energy spectrumsensing same approach can be further investigated for waveletspectrum sensing. Wavelet spectrum sensing gives best time &frequency resolution among all other spectrums sensing

711

techniques. So, in future work the performance waveletspectrum sensing technique is evaluated & analyzed.

REFERENCES[1] FCC Spectrum Efficiency Working Group, 2002. Report of the

Spectrum Efficiency Working Group U.S.A.: FederalCommunications Commission Spectrum Policy Task Force.

[2] Digham, F., M. Alouini, and M. Simon. 2003. On the energydetection of unknown signals over fading channels Proc. IEEEInt. Conf. on Communications.

[3] Cabric, D., S. Mishra, and R. Brodersen. 2004. Implementationissues in spectrum sensing for cognitive radios Proc. Asilomarconf. on signals, systems and computers

[4] Haykin S. and M. Moher eds. 2004. Modern WirelessCommunications United States: Prentice-Hal

[5] Cabric. D., A. Tkachenko, and R. Brodersen. 2006. Spectrumsensing measurements of pilot, energy, and collaborativedetection Proc. IEEE Military Commun. Conf.

[6] Akyildiz, I.F., W. Y. Lee, M. C. Vuran, and S. Mohanty. 2006.Next generation dynamic spectrum access/cognitive radiowireless networks: a survey Comput. Networks

[7] Zui Tian and Georgios B. Giannakis 2006. A wavelet approach towideband spectrum sensing for cognitive radios Cognitive RadioOriented Wireless Networks and Communications, 2006. 1stInternational Conference

[8] Arslan. H. and T.Yucek 2007. Spectrum sensing for cognitiveradio applications Cognitive radio, Software defined radio andAdaptive wireless systems springer 3:263-289.

[9] Rajarshi. M. and M. Krusheel. 2008. Cyclostationary Detectionfor Cognitive Radio with Multiple Receivers IEEE IntlSymposium on Wireless Communication Systems: 493-497.

[10] Amna Saad Kamil, Ibrahim Khider 2008. Open research issues incognitive radio Telecommunications forum 2008

[11] Tevfik Yucek and Huseyin Arslan 2009. A Survey of SpectrumSensing Algorithms for Cognitive Radio Applications IEEECommunications Surveys & Tutorials, Vol. 11

[12] Luis Miguel Cortes-pena 2009. MIMO space-time block coding:simulations and results .Personal & mobile communications ;presented to DR. Gordon Stuber

[13] Jian Chen, Andrew Gibson, Junaid Zafar 2009. Cyclostationaryspectrum detection in cognitive radios Cognitive Radio andSoftware Defined Radios: Technologies and Techniques, 2008IET Seminar

[14] D. D. Ariananda, M. K. Lakshmanan, H. Nikookar 2009. Asurvey on spectrum sensing techniques for cognitive radioCognitive Radio and Advanced Spectrum Management, 2009.CogART 2009. Second International Workshop

[15] Refik Fatih Ustok 2010. Spectrum sensing techniques forcognitive radio systems with multiple antennas M.E. thesis, IzmirInstitute of Technology

[16] Muhammad Fainan .Hanif, Peter J. Smith 2011. MIMO cognitiveradios with Antenna selection. IEEE transactions on wirelesscommunications

[17] Danda.B.Rawat and Gongjun Yan 2011. Spectrum sensingmethods and dynamic spectrum sharing in cognitive radionetworks : A survey International journal of research andreviews in wireless sensor networks

[18] Varadharajan. E, Rajkumari. M 2012. Discrete wavelet transformbased spectrum sensing in cognitive radio using Eigen filterInternational Journal of Advanced Engineering Technology.

[19] Mahmood A. Abdulsattar and Zahir. A. Hussein 2012. A NewMultiple Antennas Method based Energy Detector for CognitiveRadio over Fading Channels International Journal of ComputerApplications (0975-8887)

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