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Opportunistic Spectrum Sensing by Employing Matched Filter in Cognitive Radio

Network

Shipra Kapoor, SVRK Rao and Ghanshyam Singh

Department of Electronics and Communication Engineering,

Jaypee University of Information Technology

Waknaghat, Solan - 173215, India

Email: {ece.shiprakapoor.dit, svrk.rao, drghanshyam.singh}@gmail.com

Abstract The sophistication possible in a software defined

radio has now reached a level where each radio can

conceivably perform many beneficial tasks that help the user

and network. It can also minimize the spectral congestion. If a

radio could use favourable frequencies and choose waveforms

that would minimize and avoid interference with existing radio

communication systems, it would be an ideal software defined

radio or in general sense cognitive radio which has all the

properties of software defined radio along with the property of

artificial intelligence. The presented paper is underpinning on

the spectrum sensing, which is performed by implementing

matched filter and is supported along with the simulation

results.

Keywords- cognitive radio, spectrum utilization, non-cooperative

sensing, matched filter.

I. INTRODUCTION

With the increasing demand for wireless communication,efficient spectrum management and access are necessary and

critical. What we have witnessed is an underlying trend ofparadigm shift from the fixed centralized communication todecentralized and dynamic communication with highlydistributed resources, computation and processing. Given thelimitations of the natural frequency spectrum, it becomesobvious that the current static frequency allocation schemescannot accommodate the requirements of an increasingnumber of higher data rate devices. With the FederalCommunications Commissions (FCC) spectrum policyreform, new ways to dynamically access spectrum arebecoming possible. Among one of the new technologieswhich are emerging is cognitive radio.

Cognitive radio arises to be a tempting solution to thespectral congestion problem by introducing opportunisticusage of the frequency bands that are not heavily occupiedby licensed users [1], [2]. Cognitive radio is theconcatenation of software defined radio and artificialintelligence which relies on the principle of efficientspectrum usage as shown in Fig. 1. Cognitive radio asdefined by Federal Communications Commission (FCC) isgiven as: Cognitive radio: A radio or system that senses itsoperational electromagnetic environment and candynamically and autonomously adjust its radio operatingparameters to modify system operation, such as maximize

throughput, mitigate interference, facilitate interoperability,access secondary markets [2].

Figure 1. Concatenation of software defined radio and ar tificial intelligence

give birth to cognitive radio.

Hence, one main aspect of the cognitive radio is related

to autonomously exploiting locally unused spectrum knownas spectrum hole to provide new paths to spectrum access asshown in Fig. 2(a). The Fig. 2(b) is the schematic of thecognitive radio.

Figure 2. Spectrum hole concept and cognitive radio transceiverarchitecture.

In order to efficiently utilize the spectrum cognitive radiohas some challenges which can be enumerated as:a) Spectrum sensing.b) Spectrum management.c) Spectrum sharing.d) Spectrum mobility.

The first challenge is to sense the spectrum that is to findthe spectrum holes in the radio frequency spectrum.

2011 International Conference on Communication Systems and Network Technologies

978-0-7695-4437-3/11 $26.00 2011 IEEE

DOI 10.1109/CSNT.2011.124

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Spectrum sensing by far is the most importathe establishment of cognitive radio [3]. Spthe task of obtaining awareness about theand existence of primary users in a geograawareness can be obtained by using gdatabase, by using beacons, or by local spcognitive radios. Spectrum can be sense

different ways either non co-operativeoperative sensing. The concept of interferenused in concatenation with these two technlower the interference level in the received s

The presented paper mainly emphasisspectrum through non co-operative way. Ththe paper is as follows. A general idea aboas used in cognitive radio is given in theSection III discusses about the proposedsimulation results are discussed in SectionSection V records the conclusion and futurwork.

II. RELATED WORK

A. Spectrum sensing methods for cognitiveSpectrum sensing is regarded to be the

cognitive radio because only after the spdetected then the further processing will bmost efficient way to detect spectrum holewhether there is any primary user (1) thatthe communication range of an next gegeneration user [4]. In reality, however, itcognitive radio to have a direct measurembetween a primary receiver and a transmitterecent work focuses on primary transmitteon local observations of the xG users (spectrum sensing techniques has been classFig. 3.

Figure 3. Various spectrum sensing tec

A.1 Transmitter detection (non-cooperative

The main aim of cognitive radio shouldbetween used and unused spectrum bands.

exhibit ability to determine whether a sprimary transmitter which is locally presspectrum or not. In order to cope up withtransmitter detection approach is implebased on the detection of the weak signaltransmitter through the local observationsThe basic hypothesis model for transmitteron this approach and is defined as follows [5

nt component forctrum sensing isspectrum usage

phical area. Thiseo location andctrum sensing at

widely in two

sensing or co-ce temperature isiques in order toignal.

on sensing thee organization ofut matched filterSection II. The

ork whereas theIV of the paper.direction of the

adiokey element forctrum holes areproceeded. Theis to first detect

re present withineration xG nextis difficult for aent of a channelr. Thus, the most

detection based). The various

ified as shown in

nique.

detection)

be to distinguish. Thus, it should

ignal is from aent in a certainhis problem, theented which isfrom a primary

of the xG users.etection is based]:

, ,where x(t) is the signal received bs(t) is the transmitted signal by theAWGN (additive white Gaussian nis the amplitude gain of the chanhypothesis, which states that therein a certain spectrum band. On talternative hypothesis, which indicalicensed user signal. This modelwhether there is any local (primfocused geographic location for furthe usage of virtual unlicensetransmitter detection technique, tschemes which are generally emplmodel accomplishes that primaryfollowing subsections, we therebytechniques under various environalso shown in Fig. 3. Underenvironment the techniques are:

Matched Filter Detection. Energy Detection. Cyclostationary Feature Dete

Under the sub heading of co-omajor divisions are:

Centralized detection. Distributed detection.

The third category is the intewhich mainly emphasis on the malevel and can be applied with both[7] that is the co-operative and non

A.2 Brief introduction to Matched fi

In telecommunications, a matccorrelating a known signal, with anthe presence of the known signal inis equivalent to convolving theconjugated time-reversed versionmatched filter is the optimal linearsignal to noise ratio (SNR) in tstochastic noise. Two-dimensioncommonly used in image proceimprove the SNR for X-ray pictusignal to be sent by the transmitter iFig. 4(a).

Figure 4 Representation of transmitted

waveform diagram such as: (a) transmittetransmitted signal in the channel (c) signa

matched filter (d) signal received with the us

(1)the xG user transmitter,

primary user, n(t) is theise) in the channel and hel. H0 represents a nulls no licensed user signale other hand, H1 is an

tes that there exists someonly helps to identify

ry) user present in theher processing or not for

spectrum [6]. Underere are three differentyed after the hypothesisuser is present. In theenumerate the detectionents in the xG networkthe non co-operative

ction.perative sensing the two

ference based detectionagement of interferencethe variations of sensingo-operative.

lter

ed filter is obtained byunknown signal to detectthe unknown signal. Thisunknown signal with a

of the template. Thefilter for maximizing thee presence of additivel matched filters are

ssing, for example, toes. For example, let thes 0101100100 as sown in

and received signal through

signal (b) AWGN added tol received without the use of

e of matched filter.

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If we model our noisy channel as anwhite Gaussian noise is added to thedemonstrates it whereas the received signaof matched filter is shown in Fig. 4(c). Wheis implemented at the receiver end the rconvolved with a conjugated time-reversetransmitted signal, the waveform of which

Fig. 4(d). Thus, by comparing the obtainedFig. 4(c) with a particular threshold level wtransmitted signal without any error as sho

A.2.1 Frequency-domain interpretation of

When viewed in the frequency domain,the matched filter applies the greatest weicomponents that have the greatest signAlthough in general this requires a noresponse, the associated distortion is nsituations such as radar and digital commthe original waveform is known and the objthe presence of this signal against the backg

III. PROPOSED WORK

B. Non co-operative sensing techniques

B.1 Matched filter detection

This technique can be used only whenpriori information related with the various pPHY and MAC layer of the primary user sidetector is the matched filter which is Gausworks on the principle in maximizing the rnoise ratio.

Figure 5. Flow sequence of matched filter

AWGN channel,signal Fig 4(b)l without the use

a matched filterceived signal is

d version of theis shown in the

eceived signal ine can recover then in Fig 4 (d).

atched filter

it is evident thathting to spectrall-to-noise ratio.

n-flat frequencyt significant innications, wherective is to detectound noise.

the xG user hasarameters at bothnal. The optimalian in nature andceived signal-to-

detection.

While the main advantage of tis that it requires less time to achidue to coherency and also gouncertainty with moderate comprequires a priori knowledge of theas the modulation type and order,packet format. Alternately, if this in

then the matched filter performsmost wireless network systemssynchronization word or spreadingfor the coherent detection.

The flow sequence of matcbeginning with the hypothesis modin Fig. 5 Another limitation of tmatched filter is unable to diinformation signal and the noisereceiver of the secondary user.

IV. SIMULATION

As in section II we discussed abwhich decides whether a primary

geographical region by using equatwhich is thus shown in Fig. 6 (a), (b

(a)

(b)

(c)

Figure 6. Simulation results of the hypo-thypothesis (b) absolute hypothesis indicati

absolute hypothesis indicating the use of

e matched filter detectorve high processing gaind robustness to noisetational complexity. It

primary user signal suchthe pulse shape, and theformation is not accurate,

poorly. However, sincehave pilot, preambles,codes, these can be used

ed filter detection [8]el has been demonstratedis technique is that the

fferentiate between thesignal received by the

RESULTS

out the hypothesis modeluser is present in that

on (1), the simulation of), (c).

hesis model such as (a) nullg the use of matched filter (c)energy detection technique.

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According to the concept of transmittesensing through matched filter, the optimalmatched filter which is Gaussian in natureprinciple in maximizing the received signal-discussed in the former section the mainimplementing matched filter is that seconduser should have information regarding the

of the primary user such as pilot, preamblesword or spreading codes, modulation typpulse shape, and the packet format. Basconsiderations simulations results have bee7.

The first subplot is the received signtransmitted by the primary user which isimpulse function shown in second subplot.shows the multiplicative result which is addGaussian noise w(t) which is random in natshown in the fifth subplot which is the signprocessor of the receiver.

Figure 7. Simulation result showing the execution

The signal is now compared with a pre

based on the input signal to noise ratio andto noise ratio. The content of the signal thabands which lie below the assumed thrassumed to be neglected and thus treatedholes as shown in fig. 8.

Figure 8. Detection of spectrum holes by implemenmatched filter.

Spectrum holes can be of three differentwhite hole or a gray hole. The obtained specbe a black hole because a black hole is a

based spectrumdetector used is and works on the

to-noise ratio. Asrequirements forry user or a CR

basic parameters

, synchronizatione and order, thed on the aboveobtained in Fig.

al x(t) which isultiplied by the

The third subploted with the whiteure. The result isl obtained at the

of matched filter.

ecided threshold

the output signalis the frequencyeshold level areas the spectrum

ting the precept of

types. It can be atrum hole cannotset of frequency

band which is fully utilized. If theutilized then the magnitude of thethreshold level according to the isensing the spectrum [9] and thushole which cannot be utilized by ausing the concept of signal to noiseperformed following its advantage

high processing gain due to coheren

V. CONCLUSION AND

Next generation networks arecurrent wireless network problemsavailable spectrum and the ineffiusage by exploiting the existopportunistically. xG networks, eqcapabilities of the cognitive radio,spectrum-aware communicationcommunications. The presented pathe detection of spectrum hole withby employing its properties. The cby the simulation results. Furt

interference management can be inconceptual work more efficaciously

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