Methods for Detecting Energy and Signals in Cognitive Radio: A Review

Methods for Detecting Energy and Signals in Cognitive Radio: A Review

1M.Tech, Electronic and Communication Engineering, GITM, Lucknow, India

2Professor Electronic and Communication Engineering, GITM, Lucknow, India

Abstract - Cognitive radio is an emerging technology that allows for dynamicandadaptive useoftheradio spectrum by wireless communication devices. The technology has gained significant attention in recent years due to its potential to address the increasing demand for wireless communication and the scarcity of available radio spectrum. In this review paper, we provide a comprehensive overview of cognitive radiotechnology,includingitsprinciples,architecture,andkey components. We also discuss the major challenges and research directions in cognitive radio, such as spectrum sensing, channel selection, and security. Furthermore, we provide a survey of the recent advances and applications of cognitive radio in various domains, such as military communication, emergency response, and commercial communication. Finally, we conclude the review paper by summarizing the key findings and identifying the future research directions in cognitive radio. This review paper providesavaluableresourceforresearchersandpractitioners interested in cognitive radio technology and its applications.

Key Words: Cognitiveradio,Wirelesscommunication,Radio spectrum, Dynamic spectrum access, Spectrum sensing, Channelselection,Interferencemitigation

1. INTRODUCTION

Cognitiveradioisatypeofwirelesscommunicationsystem that enables devices to use available radio frequencies dynamically and adaptively. It is also known as softwaredefined radio because it uses software to change the frequency,modulation,orothercharacteristicsoftheradio signal. The technology was developed to address the growing demand for wireless communication and the increasingscarcityofavailableradiospectrum.

Thecognitiveradiotechnologyallowsdevicestosensethe radio spectrum, analyze the available channels, and select thebestfrequencyfortransmissionbasedonthequalityof thesignal,thebandwidthavailability,andtheinterference level.Thisdynamicandadaptiveuseoftheradiospectrum can improve the efficiency and performance of wireless communication systems, as it allows them to utilize underutilized frequency bands, avoid interference, and improvespectrumutilization.

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Figuew-1: cognitive radio

1.1. Principle of Cognitive Radio

Theprincipleofcognitiveradioistoenablewirelessdevices tointelligentlyanddynamicallyadapttotheirenvironment and make efficient use of available radio frequency spectrum. The technology operates on the principle of "dynamic spectrum access," which allows cognitive radio devicestodetectanduseavailablefrequencybandsinrealtime.

Thekeyprinciplesofcognitiveradioinclude:

Spectrum sensing: Cognitiveradiosaredesignedtodetect andmonitorthefrequencyspectrum,identifyingavailable frequencybandsthatcanbeusedforcommunication.

Spectrum management: Onceanavailablefrequencyband is detected, cognitive radios can use algorithms to dynamically and automatically select the best frequency bandforcommunication,optimizingsystemperformance.

Interference management: Cognitive radios can detect interference from other wireless devices and adjust their operating parameters to avoid interference and improve networkperformance.

Spectrum sharing: Cognitive radios can share available frequency bands with other wireless devices, including licensed users, without causing interference, allowing for moreefficientuseofspectrum.

Theprincipleofcognitiveradioistocreateamoreflexible and efficient use of radio frequency spectrum, improving overall system performance and enabling new wireless applicationsandservices.

1.2. Cognitive Radio Network

Acognitiveradionetworkisawirelessnetworkthatutilizes cognitiveradiotechnologytointelligentlyanddynamically managetheuseofavailableradiofrequencyspectrum.Itisa network of cognitive radios that communicate with each otherandwithotherwirelessdevicestomaximizetheuseof available frequencies and improve overall system performance.

In a cognitive radio network, each cognitive radio has the ability to sense its environment, including the frequency spectrum and the activity of other wireless devices in the area.Basedonthisinformation,thecognitiveradiocanmake intelligentdecisionsaboutwhichfrequencybandstouseand howtoavoidinterferencewithotherwirelessdevices.

Cognitive radio networks can operate in different modes, including opportunistic mode, where the system can dynamicallyaccessunusedfrequencybands,andspectrum sharingmode,wherethesystemcansharefrequencybands withlicensedusers.Thetechnologycanbeusedinvarious applications, including wireless networking, cellular communications,andpublicsafetysystems.

Cognitive radio networks offer several advantages over traditionalwirelessnetworks,includingimprovedspectrum utilization,betterresistancetointerference,andincreased network capacity. The technology has the potential to revolutionizethewayweusewirelessspectrum, enabling more efficient and effective use of radio frequencies and supporting the development of new wireless applications andservices.

2. COGNITIVE RADIO STATION

Acognitiveradiostationisawirelesscommunicationdevice thatisequippedwithcognitiveradiotechnology,allowingit tointelligentlyanddynamicallyadapttoitsenvironmentand makeefficientuseofavailableradiofrequencyspectrum.A cognitiveradiostationtypicallyconsistsofatransceiver,a processor,andanantenna,andiscapableofdetectingand usingavailablefrequencybandsinreal-time.

3. LITERATURE REVIEW

A literature review is a critical evaluation of the existing body of research on a specific topic. It involves analyzing, summarizing, and synthesizing relevant scholarly articles, books,andothersourcestoidentifycurrentknowledgegaps, identifykeythemes,andprovideabroadercontextforthe research. The summary of all research paper are given below:

Sharma: The beginning of this article provides a comprehensiveintroductiontothetopicofenergydetection. Theissuewiththeconventionalmethodofenergydetection is that it is unable to discern between the principal signal and the background noise. In this particular piece of research,anuniquemethodofenergydetectionknownas squared energy detection is suggested. Calculating the performance of the proposed squared energy detection systemrequiresemployingperformancemeasuressuchas the probability of detection and the probability of a false alarm.Theteststatisticiscomparedinthespectrumsensing technique that was presented with the mean value of the difference between the squares of two samples. The test statisticisnowcomparedwiththenewthreshold,whichis relatedtotheintensityofthesignalthathasbeenreceived. Thiscausestheoddsofsuccessfullydetectingasignaltorise whenusingthisapproach.

Abdulsattar, Hussein: EnergySignalDetectionisafigureof meritthathasbeenestablishedasa basisforquantitative evaluation of the design of a radiometer, including its calibration architecture and algorithm. It was determined thatthechallengeofspectrumdetectiontechniquesshould incorporateenergydetectioninboththetimedomainand thefrequencydomain.Energydetectionhasbeenaccepted asanalternateapproachforspectrumsensinginCRsowing to the fact that its circuit in the actual implementation is simple,anditdoesnotneedanyinformationaboutthesignal thathastobedetectedinordertodoso.

Fernando: Because of its ease of use and general applicability(regardlessofthesignalformatthatneedstobe detected), as well as its low computational and implementation costs, energy detection has become a popularspectrumsensingtechniqueforcognitiveradio.This popularitycanbeattributedtoitslowcosts.Yet,themost significantdisadvantageithasisthewell-knownlimitsinits detecting performance. It has been demonstrated that a variety of alternative spectrum sensing methods are superior to energy detection; however, this comes at the expenseofsignificantlyincreasedcomputationalcostanda restricted field of application because such methods are typically developed for the purpose of improving the detectionofspecificsignalformats.Anenhancedtechnique for detecting energy has been proposed as a result of this bodyofwork.Thistechniquehasthepotentialtooutperform the traditional technique for detecting energy while maintainingthesamelevelofcomplexityandcomputational cost as well as its general field of applicability. The capabilitiesofthesuggestedtechniquehavebeenshownvia ananalyticalevaluationofthealgorithm'sperformanceas wellascorroborationwithexperimentaldata.

Hemant: Afterthat,theresultoftheerrorratecalculation for primary energy and the output of the error rate calculation for the noise signal are added together and shown,whichiswhatispresentedwhiletheuserisrunning

the programme. Via the examination of this output, the existence of the principal user may be determined. If the resultis1,thisindicatesthattheprincipaluserisloggedin. Insuchcase,theprincipaluserisnotpresent.Analyzingthe energyspectrumofthemainuserisstillanothermethodfor determining whether or not the primary user is present. Accordingtothismethod,theprimaryuserispresentifthe energypeaksarehigherthanthethresholdvalue;otherwise, theprimaryuserisnotthere.Whilelookingatthe energy banddiagram,onecannoticethatallofthepeaksarehigher thanthethresholdvalue;thissuggeststhatthereisamain userpresent.Evenintheabsenceofamainuser,theenergy detectorisabletodeterminewhetherornotaprimaryuser ispresentinenvironmentswithlowsignal-to-noiseratios. Themostsignificantlimitationofenergydetectionisthatit isunabletodifferentiatebetweenbackgroundnoiseandthe signal's own energy. If the energy detector is operating in situationswithalowSNR,itwilldetectthatthemainuseris presentanywhere throughoutthespectrumif the noise is white. After determining the energy of the principal user signal,thesignaliscomparedtoanexperimentalthreshold value;forthisexample,wehavedecidedthatthethreshold valuewillbeequalto55e-5.InSimulink,thecomparisonis carried out through the Error rate calculation block. The input data from the energy outport iscompared with the inputdatafromtheconstantthresholdportinthisblock.It doessobydividingthetotalnumberofunevenpairingsof data elements by the total number of input data elements comingfromasinglesourceinordertoarriveatarunning statistic thatitreferstoastheerror rate.Iftheinputsare bits,thentheblockcomputesthebiterrorrate;otherwise,it computesthesymbolerrorrate.Wehavetoutilisethisblock toeithercomputethesymbolorbiterrorrate.Intheevent that the inputs consist of symbols, it will calculate the symbolerrorrate.

Kale el.al: Using time domain analysis, we are able to achievedetectionforthenumberofusersinaccordancewith the threshold formula. According to the results of the pfa study,pfa=0.1istheoptimalvalue.Accordingtothefindings oftheSNRstudy,detectionworksbestwhennoiselevelsare low. In the temporal domain, there is no indication of the user's frequency. Hence, the frequency domain will playa role in our future study. The work being presented is connectedto16QAMmodulation.Theinvestigationanduse of digital modulation OFDM in cognitive radio will be the focusofourstudyinthenearfuture.Spectrumsensingwill beourfocusintheworkweconductinthefuture,andwe willusethefrequencydomaintechnique.

Sindhubargavi: In the event that the threshold energy is higherthantheenergyofthesignal,thespectrumholewill be created. Many conclusions may be drawn from this investigation,includingthefollowing:thethresholdenergy isdeterminedbytheavailablenoise value,thefalsealarm likelihood,andthefrequency.ThePCAdetectionmethodis usedinordertocarryoutthecomputationofsignalpowerin

addition to noise power. The typical PCA method yields a ratioofSNRthatdoesnotcorrespondtothevalueofthereal SNR. The power of the signal as measured by the PCA approach is not the same as the power that is actually conveyed.Thestandardprincipalcomponentanalysis(PCA) technique'sfindingshavebeensubjectedtotheuseof the firstordercorrectionterm,whichequalstheratioofsignal power to noise power to the real signal's signal-to-noise ratio(SNR).

Nayak, Sharma: The concept of cooperative spectrum sensingforthepurposesofdatafusionanddecisionfusion has been thoroughly researched. In order to adjust the outcome, a respectable method known as ANFIS is suggested, and as was to be anticipated, it enhances the result. The implementation of the model makes use of a generalisedk-out-of-nrule.Theinclusionofseveralrelaysor thecooperativenetworkhasanumberofbeneficialeffects; nevertheless,thechanneldetectiontimewilllengthenasa resultofthismulti-relayinvolvement.Thus,theinclusionof CSI(channelstatusinformation)isstronglyrecommended forthepurposeofdeterminingwhetherchannelisoperating efficiently. If more than one route has the same CSI, the systemwillpickapathatrandomtotakeiftheyallhavethe same score. The system will choose the road that has the bestCSIoverthelastthreeiterationsautomatically.

Rawat, Korde: Itwasshownthateverydetectionmethod has a minimum SNR threshold value, below which the methodisunabletofunctioninareliablemanner.So,when there is noise present in the signal, employing cyclostationarydetectionwill behelpful,butusing energy detection will result in failure as soon as the noise in the signalsurpassesthethreshold.Whentheamountofnoisein thesignalismorethanthethreshold,theperformanceofthe cyclostationary detection approach was shown to be superiorthanthatoftheenergydetectiontechnique.

Upadhyay: MATLABSIMULINKModelisusedinordertodo thesimulationoftheenergydetectionapproachusingOFDM forspectrumsensing.Thesespectrumgapsinthespectrum oftheprincipalusermaybenoticedwiththeassistanceof thismodel,andsecondaryusersareofferedopportunitiesin amannerthatisappropriate.Calculatingperformanceand BER may also be accomplished with the assistance of the BERandEb/N0curve.

Youness: Using a dynamic threshold selection that was determined by evaluating the amount of noise that was presentinthereceivedsignal,weproposedanapproachthat wasbasedonimprovedenergydetectiontoraisethechance of detection while decreasing the risk of a false alarm. A method that is based on the sample covariance matrix eigenvalues of the received signal is used to conduct the measurementthatdeterminesthedegreeofnoise.TheGNU RadiosoftwareandUSRPdeviceswereusedinordertoput thesuggestedstrategyintoaction.Accordingtothefindings, the dynamic selection of the sensing threshold that was

presented in this study is able to enhance the chance of detectionwhilesimultaneouslyreducingthelikelihoodofa falsealarmbeingtriggered.

Snehal: Asaconsequenceofthesimulationandthefindings, it was discovered that the AWGN channel provides the greatestperformanceforenergydetectionincomparisonto theotherchannels.Nevertheless,ifthenoisepowerishigher than the signal-to-noise ratio (SNR), the Energy Detection method will not function correctly. The ease with which Energy detection can be implemented is the system's primarystrength.Accordingtothesimulationresults,itis also apparent that the Matched filter has superior performanceincomparisontotheEnergydetectionineach ofthethreechannels.However,theprimarydisadvantageof theMatchedfilteristhatitrequirespriorknowledge,suchas the modulation type and order, the pulse shape, and the packet format. In addition, the sensing of the spectrum requires a distinct matching filter detector to be used for eachfrequency.Theconclusionthatcanbedrawnfromthis investigationisthattheMatchingfilterworksbetterthanthe energy detector in each of the three channels (AWGN, Rayleighfading,andflatfading).

Mahdi et.al: Spectrum sensing in cognitive radio applications was addressed with the presentation of an adaptiveenergydetectiontechniquethatwasbasedonEMD. This technique makes use of the capacity of EMD to deconstructasignalintoIMFs,theenergyofwhichmaybe exploitedforchanneldiscovery.Theapproachreliesonthe capabilitiesofEMD.TheenergyoftheinitialIMFisscaledin ordertodoanevaluationofthenoise-onlymodel,whichis thenemployedinordertoproduceadetectionthresholdat some confidence interval (). In terms of Pd and Pfa, the scalingmodelthatwassuggesteddemonstratesmuchhigher performancethanthefixedscalemodeldid.Inaddition,the EMD-based energy detector (built on the use of the suggestedscalemodel)outperformsbothED(withanoise uncertainity) and MME over a range of SNR values, demonstratingsuperiorperformanceineachcase.

Goyal, Mathur: Inthisstudy,thebasicnotionofcognitive radioanditsfunctionswereintroducedinaconcisefashion. Also, spectrum sensing, which is the most sophisticated componentofCRN,wasanalysedindepth.Sinceinefficiency in the use of spectrum and energy both become critical problems, energy-efficient CRNs are being offered as a potentialsolutiontosolveenergyconservationconcernsin thecontextofcontemporarywirelesscommunications.This topicanalysesandanswerstheissuesthatimpacttheenergy usageinnoncooperativeandcooperativespectrumsensing. Itisofferedinthisreview.Researchcontributionsmadeup tothispointclearlyimplythattheCRN'senergyefficiency may be improved by optimising the number of secondary users and selecting a suitable sensing period. Further improvementsinenergyefficiencyarealsopossibleviathe useofrelevantmethodsforreporting.Therolesofspectrum allocation and sharing, administration, and handoff were

also discussed in this article in relation to CR's impact on energyefficiency.

Deshmukh, Shruti: Inthisstudy,wediscussedtheenergy detectionofspectrumsensingforcognitiveradionetworks usingBPSKandQPSKmodulatedsignalsinthepresenceof samplingperiod,samplingfrequency,andnoiseratio.These factorsweretakenintoconsiderationalongsidethesignal's sampling period and frequency. Signals modulated with BPSKandQPSKwereusedtoaccomplishthis.Wekeepan eyeoutforsignalsthatarefree,andwhenwefindthem,we relay those detections to the secondary user through switchingthatishandledbytheFPGA.Inadditiontothat,we examinedtheROCcurvesofPdincomparisontoPfa.While doing Monte Carlo simulations in MATLAB R2014a, the chanceofdetectionaswellasthepossibilityofafalsealarm are both affected by the signal-to-noise ratio in varying degrees.Thismightbeconstruedtoshowthatthedetection probability decreases as the false alarm probability decreasesinproportiontoanincreaseinthesignal-to-noise ratio(SNR),andviceversa.Thiswouldbeademonstration of the inverse relationship between the two variables. In order to do this, the hardware in the form of an FPGA platform is being used to carry out the functions of the energy detector as well as the switching of the absence signal.

Sanjog, Yelalwar: In this study, a comparison is made betweentheEDapproachandtheANNalgorithmforSS.As compared to the ED approach, the ANN demonstrates superiorperformance.Theamountoftimeneededtomakea choiceis1secondfortheEDtechniqueand14milliseconds for the ANN algorithm, respectively. The ANN produces accurateresultsevenwithalowSNR.ByemployingtheANN algorithm,thezeroerrorratecanbeachievedatanSNRof5dB, however when using the ED approach, the smallest errorratethatcanbeachievedis0.1.InthecaseoftheED technique,theperformanceofthesystemisdependenton the associated threshold value. In the case of the ANN algorithm,theperformanceisdependentonthenumberof hiddenlayers.WhetherusingtheEDtechniqueortheANN algorithm,thePd=1targetmaybeachievedwithanSNRof1dBor-6dB,respectively.WhentheSNRisequalto-5dB, theANNmethodachievesanaccuracyof100%,whereasthe EDmethodachievesanaccuracyof70%.TheEDtechnique is simpler than the ANN approach since it does not need training data, previous understanding of the signal, or a multilayer network. This makes the ED method less complicated.

Kenan: Inordertoimprovethespectrumsensingaccuracy ofcognitiveradionetworks,theauthorsofthispaperoffera novelthresholdexpressionmodelthatisbasedonanonline learning algorithm. The probability of detection, false detection,andfalsealarmhaveallbeensubjectedtoin-depth statistical analysis, and the optimal decision threshold expression has been re-defined in order to reduce the likelihood of making a mistake in the decision-making

process. Numerical results derived from simulations on AWGNandseveralfadingchannels(Rayleigh,Nakagami-m, Rician,andWeibull)areshowninordertodemonstratethe performanceoftheproposedapproachandcompareitwith thetechniqueofdynamicdecisionthresholddetermination. The detection performance of the energy detection- and matching filter-based spectrum sensing has been significantly enhanced according to the newly suggested sensingtechnique,eveninenvironmentswithalowsignalto-noiseratio(SNR).Infurtherresearch,oneofourgoalsis toimplementandevaluatetheperformanceofthesuggested algorithmusingavarietyofspectrumsensingtechniques.In addition,wewillconcentrateontheoptimisationofcertain expressionsthatareusedinthealgorithminordertolessen the amount of mathematical complexity and speed up the detectionprocess.

Alexandru et.al: Withinthescopeofthisstudy,anovelED methodforspectrumsensinginCRsystemsisproposed.The approach that has been suggested utilises an adaptive sensingthresholdthatisdesignedtoreducetheDEPandhas minimumoverhead,sincethevalueoftheidealthresholdis discovered by a one-step iterative process. Both of these featurescontributetothealgorithm'soverallefficiency.The performance of the proposed method is shown by the presentationofnumericalresultsacquiredfromsimulations. Thesedataarealsousedtocomparetheproposedapproach totheadaptiveCEDalgorithm.Accordingtothefindings,the proposedalgorithmperformsbetterthantheCEDalgorithm when it comes to spectrum sensing. This leads to lower values for the DEP for all of the values of the spectral utilisationratiothatareofpracticalinterestforCRsystems that give SU access to licenced spectrum. The suggested method will be implemented and validated using SDR systemsfromtheUSRPfamily,accordingtoourplans.Inthis context,wewillalsoplaceanemphasisontheoptimisation oftheexpressionsthatareusedinthemethodtoestimate thedecisionthresholdinordertominimisetheamountof computational complexity as well as the amount of time spentsensing.

Rammani, Mazhar: Theprimarylessonsthatcanbelearned from the simulations are summarised as follows: Dualthresholdenergyrecognitiondoesnotfunctionwellwhen the background noise is unexpected. In spite of the unpredictabilityofthenoise,theadaptivespectrumsensing approachworkstooptimisethedetectionthresholdofthe energydetector.Whenthesignal-to-noiseratio(SNR)drops below the noise unpredictability threshold, a commutable dualthresholdspectrumrecognitionapproachworksbetter than the other two algorithms. This occurs when the detectionprobabilitydecreases.

Rahul st.al: This article provides some background information on cognitive radio technology, including its severalcategoriesandvariousspectrumsensingapproaches. Thestudypresentedinthisarticlemakesacontributionto the energy detection technique and approach, which is

ultimately carried out via MATLAB code. MATLAB and MATLABsimulationwereusedthroughoutallofthetasks presentedinthisresearch.Inordertoinvestigatehowwell energy detection works, the results of the simulation are obtainedforvaryingnumbersofsamples.

Jun et.al: A novel approach to sensing spectrums has just beendeveloped.+Thesuggestedtechniqueisderivedfrom ED,butunlikeED,itdoesnotneedanestimationofthenoise varianceinordertocalculatethethreshold.Thisisbecause, incontrasttoED,thecomputationofthethresholdinthis method does not require prior knowledge of the noise variance.+us,ithasthepotentialtoconsiderablylessenthe effectthatthenoisevariancehasonuncertainty.Gaussian and Rayleigh fading, two traditional channel models, are taken into consideration here. +e The results of the simulationshaveshownthattheproposedmethodperforms significantlybetterthantheEDmethodwhentherearetwo ormoreantennasforaGaussianchannelandthreeormore antennas for a Rayleigh fading channel. In addition, the proposedmethodoutperformstheEDmethodwhenthereis nonoisevarianceestimation.

4. CONCLUSION

Energydetection,ontheotherhand,involvesdetectingthe presenceofprimaryusersbymeasuringtheenergyinthe frequency band of interest. The received signal is passed through a bandpass filter, and the energy of the filtered signaliscalculated.Iftheenergyexceedsapredetermined threshold, the presence of the primary user is detected. Energy detection is simple to implement and requires no knowledgeoftheprimaryusersignalcharacteristicsbutis lessreliablethanmatchedfilterdetection.

Insummary,signalandenergydetectionmethodsareused incognitiveradiotodetectthepresenceofprimaryusersin thefrequencybandsofinterest.Matchedfilterdetectionis morereliablebutrequiresknowledgeoftheprimaryuser signal characteristics, while energy detection is simple to implementbutlessreliable.

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